http://gcat.davidson.edu/GcatWiki/api.php?action=feedcontributions&feedformat=atom&user=ErhaasGcatWiki - User contributions [en]2026-07-01T13:40:57ZUser contributionsMediaWiki 1.28.2http://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=19005Eric2017-04-24T20:38:00Z<p>Erhaas: /* Genes in depth */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
[http://www.jbc.org/content/285/41/31895 Dyrk1a effects on neurogenesis]<br />
<br />
<br />
[https://en.wikipedia.org/wiki/CCT8 '''CCT8''']<br><br />
[https://genome.ucsc.edu/cgi-bin/hgGene?db=hg38&hgg_gene=CCT8 UCSC info]<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/28342823<br />
https://www.ncbi.nlm.nih.gov/pubmed/28096629<br />
https://www.ncbi.nlm.nih.gov/pubmed/28069794<br />
http://www.nature.com/nrg/journal/v18/n3/full/nrg.2016.154.html<br />
<br />
[http://www.uniprot.org/uniprot/Q13009 '''TIAM1'''] -cardiac muscle hypertrophy, Wnt signalling pathway<br><br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4024075/ Heart defects and tiam1]<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3257027/ Congenetial heart failure and tiam1]<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3364940/-tiam1 contributes to heart defects in a gene dosage manner]<br />
<br />
[http://www.futuremedicine.com/doi/full/10.2217/epi-2016-0138 greater methylation in general in DS, along with faster fetal acquisition of methylation]-RUNX1, which is crucial for hematopoietic stem/progenitor cell, NKT and T-cell development.<br />
<br />
[https://en.wikipedia.org/wiki/GABPA '''GABPA'''] -Ets2 transcription factor<br />
[https://www.ncbi.nlm.nih.gov/pubmed/25977370 makes me wonder if this could cause greater risk of cancer in paternally originating ts21, as this gene is down regulated in Ts21 Maternal]<br />
http://www.sciencedirect.com/science/article/pii/S0925443904001632<br />
https://academic.oup.com/hmg/article/23/13/3456/659667/METTL23-a-transcriptional-partner-of-GABPA-is<br />
<br />
<br />
[https://molecularcytogenetics.biomedcentral.com/articles/10.1186/1755-8166-3-4 Parental of Origin DS]<br />
[http://www.sciencedirect.com/science/article/pii/S0002929707613457 gene-expression variations on phenotype]<br />
[http://www.sciencedirect.com/science/article/pii/S0002929707611926 more about expression]<br />
<br />
[https://www.ncbi.nlm.nih.gov/pubmed/27029737 Jam2]-involved in CHD in DS patients<br />
<br />
Talk about loosening standards (p<0.05, basically any logFC) and looking specfically at triplicated region. Since last time, we have explored the gene ontology and found more information on different genes.<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]-implies that this might not be the best model due to epigenetic changes that would occur in these cells down the line in development<br />
-gain of histone modification is tissue specific and associated with a loss in methylation, the opposite is rarely seen. hypomethylation at specific loci are important in cell lineage fate<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109279/ Sex Specific Effects on ESC transcriptome and epigenome] -hormonal influence on epigenetic regulation, can be carried on even with new neuronal genesis<br />
<br />
<br />
These genes were differentially expressed between maternal and paternal trisomic *in ascending order (MTvsPT):<br />
Prmt9<br />
Sumo2<br />
Flot2<br />
Aftph<br />
Gsn<br />
Dab2ip<br />
Chm<br />
Syncrip<br />
Rprd2<br />
Wipf3<br />
Stag1<br />
Prmt5<br />
Col3a1<br />
Snrnp48<br />
Wdr19<br />
Zdhhc4<br />
Ipo9<br />
Zgpat<br />
Sh3tc1<br />
Cib1<br />
Arpp19<br />
Cse1l<br />
Zfp207<br />
Dolpp1<br />
Slc38a3<br />
Ccnd3<br />
Gm10222<br />
Pmvk<br />
Rab6b<br />
Sohlh2<br />
Elavl4<br />
Cep85<br />
Serpinb6a<br />
Matr3<br />
Prdm5<br />
Gas7<br />
Shroom4<br />
Tom1l1<br />
Nop56<br />
Kank1<br />
Mrps10<br />
Baz2b<br />
Wnk1<br />
Ubap2l<br />
Eloc<br />
Prrc1<br />
<br />
mt-Atp6<br />
Fubp1<br />
Mcm10<br />
Chm<br />
Srsf5<br />
Ezh2<br />
Cyb5b<br />
Zkscan5<br />
Hmgn2<br />
Mark2<br />
Matr3<br />
Zfp207<br />
Vps51<br />
Gstm1<br />
Zfp12<br />
Fubp3<br />
Slit2<br />
Ube2k<br />
Gatb<br />
Apbb2<br />
Rangrf<br />
Acox1<br />
Spata5<br />
Senp6<br />
Rps13<br />
Zc3h11a<br />
Dohh<br />
Dnajc11<br />
Actn4<br />
Gcat<br />
Hnrnpdl<br />
Elavl4<br />
Fgfr1<br />
Mtx1<br />
Bptf<br />
Ccdc136<br />
Tbc1d24<br />
Ptp4a3<br />
Psmd2<br />
Rcor3<br />
Esrrb<br />
Kif20a<br />
Zfp993<br />
Crocc<br />
D930048N14Rik<br />
Fus<br />
Ikbkg<br />
Relb<br />
Ing5<br />
Asap1<br />
Cryz<br />
<br />
<br />
<br />
Why do we see so many genes differentially expressed between maternal trisomic and paternal trisomic that are not on the triplicated region of 17^16? What role do the triplicated genes play on the expression of these genes on other chromosomes. Does the triplicated region affect the geography of the nuclear DNA enough to cause these changes?</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=19004Eric2017-04-20T14:37:29Z<p>Erhaas: </p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
[http://www.jbc.org/content/285/41/31895 Dyrk1a effects on neurogenesis]<br />
<br />
<br />
[https://en.wikipedia.org/wiki/CCT8 '''CCT8''']<br><br />
[https://genome.ucsc.edu/cgi-bin/hgGene?db=hg38&hgg_gene=CCT8 UCSC info]<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/28342823<br />
https://www.ncbi.nlm.nih.gov/pubmed/28096629<br />
https://www.ncbi.nlm.nih.gov/pubmed/28069794<br />
http://www.nature.com/nrg/journal/v18/n3/full/nrg.2016.154.html<br />
<br />
[http://www.uniprot.org/uniprot/Q13009 '''TIAM1'''] -cardiac muscle hypertrophy, Wnt signalling pathway<br><br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4024075/ Heart defects and tiam1]<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3257027/ Congenetial heart failure and tiam1]<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3364940/-tiam1 contributes to heart defects in a gene dosage manner]<br />
<br />
[http://www.futuremedicine.com/doi/full/10.2217/epi-2016-0138 greater methylation in general in DS, along with faster fetal acquisition of methylation]-RUNX1, which is crucial for hematopoietic stem/progenitor cell, NKT and T-cell development.<br />
<br />
[https://en.wikipedia.org/wiki/GABPA '''GABPA'''] -Ets2 transcription factor<br />
[https://www.ncbi.nlm.nih.gov/pubmed/25977370 makes me wonder if this could cause greater risk of cancer in paternally originating ts21, as this gene is down regulated in Ts21 Maternal]<br />
<br />
[https://molecularcytogenetics.biomedcentral.com/articles/10.1186/1755-8166-3-4 Parental of Origin DS]<br />
[http://www.sciencedirect.com/science/article/pii/S0002929707613457 gene-expression variations on phenotype]<br />
[http://www.sciencedirect.com/science/article/pii/S0002929707611926 more about expression]<br />
<br />
[https://www.ncbi.nlm.nih.gov/pubmed/27029737 Jam2]-involved in CHD in DS patients<br />
<br />
Talk about loosening standards (p<0.05, basically any logFC) and looking specfically at triplicated region. Since last time, we have explored the gene ontology and found more information on different genes.<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]-implies that this might not be the best model due to epigenetic changes that would occur in these cells down the line in development<br />
-gain of histone modification is tissue specific and associated with a loss in methylation, the opposite is rarely seen. hypomethylation at specific loci are important in cell lineage fate<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109279/ Sex Specific Effects on ESC transcriptome and epigenome] -hormonal influence on epigenetic regulation, can be carried on even with new neuronal genesis<br />
<br />
<br />
These genes were differentially expressed between maternal and paternal trisomic *in ascending order (MTvsPT):<br />
Prmt9<br />
Sumo2<br />
Flot2<br />
Aftph<br />
Gsn<br />
Dab2ip<br />
Chm<br />
Syncrip<br />
Rprd2<br />
Wipf3<br />
Stag1<br />
Prmt5<br />
Col3a1<br />
Snrnp48<br />
Wdr19<br />
Zdhhc4<br />
Ipo9<br />
Zgpat<br />
Sh3tc1<br />
Cib1<br />
Arpp19<br />
Cse1l<br />
Zfp207<br />
Dolpp1<br />
Slc38a3<br />
Ccnd3<br />
Gm10222<br />
Pmvk<br />
Rab6b<br />
Sohlh2<br />
Elavl4<br />
Cep85<br />
Serpinb6a<br />
Matr3<br />
Prdm5<br />
Gas7<br />
Shroom4<br />
Tom1l1<br />
Nop56<br />
Kank1<br />
Mrps10<br />
Baz2b<br />
Wnk1<br />
Ubap2l<br />
Eloc<br />
Prrc1<br />
<br />
mt-Atp6<br />
Fubp1<br />
Mcm10<br />
Chm<br />
Srsf5<br />
Ezh2<br />
Cyb5b<br />
Zkscan5<br />
Hmgn2<br />
Mark2<br />
Matr3<br />
Zfp207<br />
Vps51<br />
Gstm1<br />
Zfp12<br />
Fubp3<br />
Slit2<br />
Ube2k<br />
Gatb<br />
Apbb2<br />
Rangrf<br />
Acox1<br />
Spata5<br />
Senp6<br />
Rps13<br />
Zc3h11a<br />
Dohh<br />
Dnajc11<br />
Actn4<br />
Gcat<br />
Hnrnpdl<br />
Elavl4<br />
Fgfr1<br />
Mtx1<br />
Bptf<br />
Ccdc136<br />
Tbc1d24<br />
Ptp4a3<br />
Psmd2<br />
Rcor3<br />
Esrrb<br />
Kif20a<br />
Zfp993<br />
Crocc<br />
D930048N14Rik<br />
Fus<br />
Ikbkg<br />
Relb<br />
Ing5<br />
Asap1<br />
Cryz<br />
<br />
<br />
<br />
Why do we see so many genes differentially expressed between maternal trisomic and paternal trisomic that are not on the triplicated region of 17^16? What role do the triplicated genes play on the expression of these genes on other chromosomes. Does the triplicated region affect the geography of the nuclear DNA enough to cause these changes?</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=19003Eric2017-04-13T14:57:51Z<p>Erhaas: /* Genes in depth */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
[http://www.jbc.org/content/285/41/31895 Dyrk1a effects on neurogenesis]<br />
<br />
<br />
[https://en.wikipedia.org/wiki/CCT8 '''CCT8''']<br><br />
[https://genome.ucsc.edu/cgi-bin/hgGene?db=hg38&hgg_gene=CCT8 UCSC info]<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/28342823<br />
https://www.ncbi.nlm.nih.gov/pubmed/28096629<br />
https://www.ncbi.nlm.nih.gov/pubmed/28069794<br />
http://www.nature.com/nrg/journal/v18/n3/full/nrg.2016.154.html<br />
<br />
[http://www.uniprot.org/uniprot/Q13009 '''TIAM1'''] -cardiac muscle hypertrophy, Wnt signalling pathway<br><br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4024075/ Heart defects and tiam1]<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3257027/ Congenetial heart failure and tiam1]<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3364940/-tiam1 contributes to heart defects in a gene dosage manner]<br />
<br />
[http://www.futuremedicine.com/doi/full/10.2217/epi-2016-0138 greater methylation in general in DS, along with faster fetal acquisition of methylation]-RUNX1, which is crucial for hematopoietic stem/progenitor cell, NKT and T-cell development.<br />
<br />
[https://en.wikipedia.org/wiki/GABPA '''GABPA'''] -Ets2 transcription factor<br />
[https://www.ncbi.nlm.nih.gov/pubmed/25977370 makes me wonder if this could cause greater risk of cancer in paternally originating ts21, as this gene is down regulated in Ts21 Maternal]<br />
<br />
[https://molecularcytogenetics.biomedcentral.com/articles/10.1186/1755-8166-3-4 Parental of Origin DS]<br />
[http://www.sciencedirect.com/science/article/pii/S0002929707613457 gene-expression variations on phenotype]<br />
[http://www.sciencedirect.com/science/article/pii/S0002929707611926 more about expression]<br />
<br />
[https://www.ncbi.nlm.nih.gov/pubmed/27029737 Jam2]-involved in CHD in DS patients<br />
<br />
Talk about loosening standards (p<0.05, basically any logFC) and looking specfically at triplicated region. Since last time, we have explored the gene ontology and found more information on different genes.<br />
<br />
Using R, it seems as though these data are not significantly different after running a two-sample t-test on the log FC between each group. not sure if this is an appropriate test in this case, as the individual genes are of greater interest than the whole set versus the whole other set.<br />
<br />
Welch Two Sample t-test<br />
<br />
data: MTvPTlocalized$LogFC and MDvPDlocalized$LogFC<br />
t = 0.60789, df = 24.559, p-value = 0.5488<br />
alternative hypothesis: true difference in means is not equal to 0<br />
95 percent confidence interval:<br />
-0.2644515 0.4856487<br />
sample estimates:<br />
mean of x mean of y <br />
0.20042373 0.08982513<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]-implies that this might not be the best model due to epigenetic changes that would occur in these cells down the line in development<br />
-gain of histone modification is tissue specific and associated with a loss in methylation, the opposite is rarely seen. hypomethylation at specific loci are important in cell lineage fate<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109279/ Sex Specific Effects on ESC transcriptome and epigenome] -hormonal influence on epigenetic regulation, can be carried on even with new neuronal genesis<br />
<br />
<br />
These genes were differentially expressed between maternal and paternal trisomic *in ascending order (MTvsPT):<br />
Prmt9<br />
Sumo2<br />
Flot2<br />
Aftph<br />
Gsn<br />
Dab2ip<br />
Chm<br />
Syncrip<br />
Rprd2<br />
Wipf3<br />
Stag1<br />
Prmt5<br />
Col3a1<br />
Snrnp48<br />
Wdr19<br />
Zdhhc4<br />
Ipo9<br />
Zgpat<br />
Sh3tc1<br />
Cib1<br />
Arpp19<br />
Cse1l<br />
Zfp207<br />
Dolpp1<br />
Slc38a3<br />
Ccnd3<br />
Gm10222<br />
Pmvk<br />
Rab6b<br />
Sohlh2<br />
Elavl4<br />
Cep85<br />
Serpinb6a<br />
Matr3<br />
Prdm5<br />
Gas7<br />
Shroom4<br />
Tom1l1<br />
Nop56<br />
Kank1<br />
Mrps10<br />
Baz2b<br />
Wnk1<br />
Ubap2l<br />
Eloc<br />
Prrc1<br />
<br />
mt-Atp6<br />
Fubp1<br />
Mcm10<br />
Chm<br />
Srsf5<br />
Ezh2<br />
Cyb5b<br />
Zkscan5<br />
Hmgn2<br />
Mark2<br />
Matr3<br />
Zfp207<br />
Vps51<br />
Gstm1<br />
Zfp12<br />
Fubp3<br />
Slit2<br />
Ube2k<br />
Gatb<br />
Apbb2<br />
Rangrf<br />
Acox1<br />
Spata5<br />
Senp6<br />
Rps13<br />
Zc3h11a<br />
Dohh<br />
Dnajc11<br />
Actn4<br />
Gcat<br />
Hnrnpdl<br />
Elavl4<br />
Fgfr1<br />
Mtx1<br />
Bptf<br />
Ccdc136<br />
Tbc1d24<br />
Ptp4a3<br />
Psmd2<br />
Rcor3<br />
Esrrb<br />
Kif20a<br />
Zfp993<br />
Crocc<br />
D930048N14Rik<br />
Fus<br />
Ikbkg<br />
Relb<br />
Ing5<br />
Asap1<br />
Cryz<br />
<br />
<br />
<br />
Why do we see so many genes differentially expressed between maternal trisomic and paternal trisomic that are not on the triplicated region of 17^16? What role do the triplicated genes play on the expression of these genes on other chromosomes. Does the triplicated region affect the geography of the nuclear DNA enough to cause these changes?</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=19000Eric2017-04-11T14:56:21Z<p>Erhaas: /* Genes in depth */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
[http://www.jbc.org/content/285/41/31895 Dyrk1a effects on neurogenesis]<br />
<br />
<br />
[https://en.wikipedia.org/wiki/CCT8 '''CCT8''']<br><br />
[https://genome.ucsc.edu/cgi-bin/hgGene?db=hg38&hgg_gene=CCT8 UCSC info]<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/28342823<br />
https://www.ncbi.nlm.nih.gov/pubmed/28096629<br />
https://www.ncbi.nlm.nih.gov/pubmed/28069794<br />
http://www.nature.com/nrg/journal/v18/n3/full/nrg.2016.154.html<br />
<br />
<br />
[http://www.uniprot.org/uniprot/Q13009 '''TIAM1'''] -cardiac muscle hypertrophy, Wnt signalling pathway<br><br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4024075/ Heart defects and tiam1]<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3257027/ Congenetial heart failure and tiam1]<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3364940/-tiam1 contributes to heart defects in a gene dosage manner]<br />
<br />
[http://www.futuremedicine.com/doi/full/10.2217/epi-2016-0138 greater methylation in general in DS, along with faster fetal acquisition of methylation]-RUNX1, which is crucial for hematopoietic stem/progenitor cell, NKT and T-cell development.<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/?term=gabpa<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]-implies that this might not be the best model due to epigenetic changes that would occur in these cells down the line in development<br />
-gain of histone modification is tissue specific and associated with a loss in methylation, the opposite is rarely seen. hypomethylation at specific loci are important in cell lineage fate<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109279/ Sex Specific Effects on ESC transcriptome and epigenome] -hormonal influence on epigenetic regulation, can be carried on even with new neuronal genesis<br />
<br />
<br />
These genes were differentially expressed between maternal and paternal trisomic *in ascending order (MTvsPT):<br />
Prmt9<br />
Sumo2<br />
Flot2<br />
Aftph<br />
Gsn<br />
Dab2ip<br />
Chm<br />
Syncrip<br />
Rprd2<br />
Wipf3<br />
Stag1<br />
Prmt5<br />
Col3a1<br />
Snrnp48<br />
Wdr19<br />
Zdhhc4<br />
Ipo9<br />
Zgpat<br />
Sh3tc1<br />
Cib1<br />
Arpp19<br />
Cse1l<br />
Zfp207<br />
Dolpp1<br />
Slc38a3<br />
Ccnd3<br />
Gm10222<br />
Pmvk<br />
Rab6b<br />
Sohlh2<br />
Elavl4<br />
Cep85<br />
Serpinb6a<br />
Matr3<br />
Prdm5<br />
Gas7<br />
Shroom4<br />
Tom1l1<br />
Nop56<br />
Kank1<br />
Mrps10<br />
Baz2b<br />
Wnk1<br />
Ubap2l<br />
Eloc<br />
Prrc1<br />
<br />
mt-Atp6<br />
Fubp1<br />
Mcm10<br />
Chm<br />
Srsf5<br />
Ezh2<br />
Cyb5b<br />
Zkscan5<br />
Hmgn2<br />
Mark2<br />
Matr3<br />
Zfp207<br />
Vps51<br />
Gstm1<br />
Zfp12<br />
Fubp3<br />
Slit2<br />
Ube2k<br />
Gatb<br />
Apbb2<br />
Rangrf<br />
Acox1<br />
Spata5<br />
Senp6<br />
Rps13<br />
Zc3h11a<br />
Dohh<br />
Dnajc11<br />
Actn4<br />
Gcat<br />
Hnrnpdl<br />
Elavl4<br />
Fgfr1<br />
Mtx1<br />
Bptf<br />
Ccdc136<br />
Tbc1d24<br />
Ptp4a3<br />
Psmd2<br />
Rcor3<br />
Esrrb<br />
Kif20a<br />
Zfp993<br />
Crocc<br />
D930048N14Rik<br />
Fus<br />
Ikbkg<br />
Relb<br />
Ing5<br />
Asap1<br />
Cryz<br />
<br />
<br />
<br />
Why do we see so many genes differentially expressed between maternal trisomic and paternal trisomic that are not on the triplicated region of 17^16? What role do the triplicated genes play on the expression of these genes on other chromosomes. Does the triplicated region affect the geography of the nuclear DNA enough to cause these changes?</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18999Eric2017-04-11T14:55:50Z<p>Erhaas: /* Genes in depth */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
[http://www.jbc.org/content/285/41/31895 Dyrk1a effects on neurogenesis]<br />
<br />
<br />
[https://en.wikipedia.org/wiki/CCT8 '''CCT8''']<br><br />
[https://genome.ucsc.edu/cgi-bin/hgGene?db=hg38&hgg_gene=CCT8 UCSC info]<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/28342823<br />
https://www.ncbi.nlm.nih.gov/pubmed/28096629<br />
https://www.ncbi.nlm.nih.gov/pubmed/28069794<br />
http://www.nature.com/nrg/journal/v18/n3/full/nrg.2016.154.html<br />
<br />
<br />
[http://www.uniprot.org/uniprot/Q13009 '''TIAM1'''] -cardiac muscle hypertrophy, Wnt signalling pathway<br><br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4024075/ Heart defects and tiam1]<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3257027/ Congenetial heart failure and tiam1]<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3364940/-tiam1 contributes to heart defects in a gene dosage manner]<br />
<br />
[http://www.futuremedicine.com/doi/full/10.2217/epi-2016-0138 greater methylation in general in DS, along with faster fetal acquisition of methylation]-RUNX1, which is crucial for hematopoietic stem/progenitor cell, NKT and T-cell development.<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]-implies that this might not be the best model due to epigenetic changes that would occur in these cells down the line in development<br />
-gain of histone modification is tissue specific and associated with a loss in methylation, the opposite is rarely seen. hypomethylation at specific loci are important in cell lineage fate<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109279/ Sex Specific Effects on ESC transcriptome and epigenome] -hormonal influence on epigenetic regulation, can be carried on even with new neuronal genesis<br />
<br />
<br />
These genes were differentially expressed between maternal and paternal trisomic *in ascending order (MTvsPT):<br />
Prmt9<br />
Sumo2<br />
Flot2<br />
Aftph<br />
Gsn<br />
Dab2ip<br />
Chm<br />
Syncrip<br />
Rprd2<br />
Wipf3<br />
Stag1<br />
Prmt5<br />
Col3a1<br />
Snrnp48<br />
Wdr19<br />
Zdhhc4<br />
Ipo9<br />
Zgpat<br />
Sh3tc1<br />
Cib1<br />
Arpp19<br />
Cse1l<br />
Zfp207<br />
Dolpp1<br />
Slc38a3<br />
Ccnd3<br />
Gm10222<br />
Pmvk<br />
Rab6b<br />
Sohlh2<br />
Elavl4<br />
Cep85<br />
Serpinb6a<br />
Matr3<br />
Prdm5<br />
Gas7<br />
Shroom4<br />
Tom1l1<br />
Nop56<br />
Kank1<br />
Mrps10<br />
Baz2b<br />
Wnk1<br />
Ubap2l<br />
Eloc<br />
Prrc1<br />
<br />
mt-Atp6<br />
Fubp1<br />
Mcm10<br />
Chm<br />
Srsf5<br />
Ezh2<br />
Cyb5b<br />
Zkscan5<br />
Hmgn2<br />
Mark2<br />
Matr3<br />
Zfp207<br />
Vps51<br />
Gstm1<br />
Zfp12<br />
Fubp3<br />
Slit2<br />
Ube2k<br />
Gatb<br />
Apbb2<br />
Rangrf<br />
Acox1<br />
Spata5<br />
Senp6<br />
Rps13<br />
Zc3h11a<br />
Dohh<br />
Dnajc11<br />
Actn4<br />
Gcat<br />
Hnrnpdl<br />
Elavl4<br />
Fgfr1<br />
Mtx1<br />
Bptf<br />
Ccdc136<br />
Tbc1d24<br />
Ptp4a3<br />
Psmd2<br />
Rcor3<br />
Esrrb<br />
Kif20a<br />
Zfp993<br />
Crocc<br />
D930048N14Rik<br />
Fus<br />
Ikbkg<br />
Relb<br />
Ing5<br />
Asap1<br />
Cryz<br />
<br />
<br />
<br />
Why do we see so many genes differentially expressed between maternal trisomic and paternal trisomic that are not on the triplicated region of 17^16? What role do the triplicated genes play on the expression of these genes on other chromosomes. Does the triplicated region affect the geography of the nuclear DNA enough to cause these changes?</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18997Eric2017-04-11T14:47:15Z<p>Erhaas: /* Genes in depth */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
[http://www.jbc.org/content/285/41/31895 Dyrk1a effects on neurogenesis]<br />
<br />
<br />
[https://en.wikipedia.org/wiki/CCT8 '''CCT8''']<br><br />
[https://genome.ucsc.edu/cgi-bin/hgGene?db=hg38&hgg_gene=CCT8 UCSC info]<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/28342823<br />
https://www.ncbi.nlm.nih.gov/pubmed/28096629<br />
https://www.ncbi.nlm.nih.gov/pubmed/28069794<br />
http://www.nature.com/nrg/journal/v18/n3/full/nrg.2016.154.html<br />
<br />
<br />
[http://www.uniprot.org/uniprot/Q13009 '''TIAM1'''] -cardiac muscle hypertrophy, Wnt signalling pathway<br><br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4024075/ Heart defects and tiam1]<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3257027/ Congenetial heart failure and tiam1]<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3364940/-tiam1 contributes to heart defects in a gene dosage manner]<br />
<br />
[http://www.futuremedicine.com/doi/full/10.2217/epi-2016-0138 greater methylation in general in DS, along with faster fetal acquisition of methylation]<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]-implies that this might not be the best model due to epigenetic changes that would occur in these cells down the line in development<br />
-gain of histone modification is tissue specific and associated with a loss in methylation, the opposite is rarely seen. hypomethylation at specific loci are important in cell lineage fate<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109279/ Sex Specific Effects on ESC transcriptome and epigenome] -hormonal influence on epigenetic regulation, can be carried on even with new neuronal genesis<br />
<br />
<br />
These genes were differentially expressed between maternal and paternal trisomic *in ascending order (MTvsPT):<br />
Prmt9<br />
Sumo2<br />
Flot2<br />
Aftph<br />
Gsn<br />
Dab2ip<br />
Chm<br />
Syncrip<br />
Rprd2<br />
Wipf3<br />
Stag1<br />
Prmt5<br />
Col3a1<br />
Snrnp48<br />
Wdr19<br />
Zdhhc4<br />
Ipo9<br />
Zgpat<br />
Sh3tc1<br />
Cib1<br />
Arpp19<br />
Cse1l<br />
Zfp207<br />
Dolpp1<br />
Slc38a3<br />
Ccnd3<br />
Gm10222<br />
Pmvk<br />
Rab6b<br />
Sohlh2<br />
Elavl4<br />
Cep85<br />
Serpinb6a<br />
Matr3<br />
Prdm5<br />
Gas7<br />
Shroom4<br />
Tom1l1<br />
Nop56<br />
Kank1<br />
Mrps10<br />
Baz2b<br />
Wnk1<br />
Ubap2l<br />
Eloc<br />
Prrc1<br />
<br />
mt-Atp6<br />
Fubp1<br />
Mcm10<br />
Chm<br />
Srsf5<br />
Ezh2<br />
Cyb5b<br />
Zkscan5<br />
Hmgn2<br />
Mark2<br />
Matr3<br />
Zfp207<br />
Vps51<br />
Gstm1<br />
Zfp12<br />
Fubp3<br />
Slit2<br />
Ube2k<br />
Gatb<br />
Apbb2<br />
Rangrf<br />
Acox1<br />
Spata5<br />
Senp6<br />
Rps13<br />
Zc3h11a<br />
Dohh<br />
Dnajc11<br />
Actn4<br />
Gcat<br />
Hnrnpdl<br />
Elavl4<br />
Fgfr1<br />
Mtx1<br />
Bptf<br />
Ccdc136<br />
Tbc1d24<br />
Ptp4a3<br />
Psmd2<br />
Rcor3<br />
Esrrb<br />
Kif20a<br />
Zfp993<br />
Crocc<br />
D930048N14Rik<br />
Fus<br />
Ikbkg<br />
Relb<br />
Ing5<br />
Asap1<br />
Cryz<br />
<br />
<br />
<br />
Why do we see so many genes differentially expressed between maternal trisomic and paternal trisomic that are not on the triplicated region of 17^16? What role do the triplicated genes play on the expression of these genes on other chromosomes. Does the triplicated region affect the geography of the nuclear DNA enough to cause these changes?</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18996Eric2017-04-11T14:10:43Z<p>Erhaas: /* Genes in depth */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
[http://www.jbc.org/content/285/41/31895 Dyrk1a effects on neurogenesis]<br />
<br />
<br />
[https://en.wikipedia.org/wiki/CCT8 '''CCT8''']<br><br />
[https://genome.ucsc.edu/cgi-bin/hgGene?db=hg38&hgg_gene=CCT8 UCSC info]<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/28342823<br />
https://www.ncbi.nlm.nih.gov/pubmed/28096629<br />
https://www.ncbi.nlm.nih.gov/pubmed/28069794<br />
http://www.nature.com/nrg/journal/v18/n3/full/nrg.2016.154.html<br />
<br />
<br />
[http://www.uniprot.org/uniprot/Q13009 '''TIAM1'''] -cardiac muscle hypertrophy, Wnt signalling pathway<br><br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4024075/ Heart defects and tiam1]<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3257027/ Congenetial heart failure and tiam1]<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3364940/-tiam1 contributes to heart defects in a gene dosage manner]<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]-implies that this might not be the best model due to epigenetic changes that would occur in these cells down the line in development<br />
-gain of histone modification is tissue specific and associated with a loss in methylation, the opposite is rarely seen. hypomethylation at specific loci are important in cell lineage fate<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109279/ Sex Specific Effects on ESC transcriptome and epigenome] -hormonal influence on epigenetic regulation, can be carried on even with new neuronal genesis<br />
<br />
<br />
These genes were differentially expressed between maternal and paternal trisomic *in ascending order (MTvsPT):<br />
Prmt9<br />
Sumo2<br />
Flot2<br />
Aftph<br />
Gsn<br />
Dab2ip<br />
Chm<br />
Syncrip<br />
Rprd2<br />
Wipf3<br />
Stag1<br />
Prmt5<br />
Col3a1<br />
Snrnp48<br />
Wdr19<br />
Zdhhc4<br />
Ipo9<br />
Zgpat<br />
Sh3tc1<br />
Cib1<br />
Arpp19<br />
Cse1l<br />
Zfp207<br />
Dolpp1<br />
Slc38a3<br />
Ccnd3<br />
Gm10222<br />
Pmvk<br />
Rab6b<br />
Sohlh2<br />
Elavl4<br />
Cep85<br />
Serpinb6a<br />
Matr3<br />
Prdm5<br />
Gas7<br />
Shroom4<br />
Tom1l1<br />
Nop56<br />
Kank1<br />
Mrps10<br />
Baz2b<br />
Wnk1<br />
Ubap2l<br />
Eloc<br />
Prrc1<br />
<br />
mt-Atp6<br />
Fubp1<br />
Mcm10<br />
Chm<br />
Srsf5<br />
Ezh2<br />
Cyb5b<br />
Zkscan5<br />
Hmgn2<br />
Mark2<br />
Matr3<br />
Zfp207<br />
Vps51<br />
Gstm1<br />
Zfp12<br />
Fubp3<br />
Slit2<br />
Ube2k<br />
Gatb<br />
Apbb2<br />
Rangrf<br />
Acox1<br />
Spata5<br />
Senp6<br />
Rps13<br />
Zc3h11a<br />
Dohh<br />
Dnajc11<br />
Actn4<br />
Gcat<br />
Hnrnpdl<br />
Elavl4<br />
Fgfr1<br />
Mtx1<br />
Bptf<br />
Ccdc136<br />
Tbc1d24<br />
Ptp4a3<br />
Psmd2<br />
Rcor3<br />
Esrrb<br />
Kif20a<br />
Zfp993<br />
Crocc<br />
D930048N14Rik<br />
Fus<br />
Ikbkg<br />
Relb<br />
Ing5<br />
Asap1<br />
Cryz<br />
<br />
<br />
<br />
Why do we see so many genes differentially expressed between maternal trisomic and paternal trisomic that are not on the triplicated region of 17^16? What role do the triplicated genes play on the expression of these genes on other chromosomes. Does the triplicated region affect the geography of the nuclear DNA enough to cause these changes?</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18995Eric2017-04-11T14:09:05Z<p>Erhaas: /* Genes in depth */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
[http://www.jbc.org/content/285/41/31895 Dyrk1a effects on neurogenesis]<br />
<br />
<br />
[https://en.wikipedia.org/wiki/CCT8 '''CCT8''']<br><br />
[https://genome.ucsc.edu/cgi-bin/hgGene?db=hg38&hgg_gene=CCT8 UCSC info]<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/28342823<br />
https://www.ncbi.nlm.nih.gov/pubmed/28096629<br />
https://www.ncbi.nlm.nih.gov/pubmed/28069794<br />
http://www.nature.com/nrg/journal/v18/n3/full/nrg.2016.154.html<br />
<br />
<br />
[http://www.uniprot.org/uniprot/Q13009 '''TIAM1'''] -cardiac muscle hypertrophy, Wnt signalling pathway<br><br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4024075/ Heart defects and tiam1]<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3257027/]<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3364940/-tiam1 contributes to heart defects in a gene dosage manner]<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3257027/]<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]-implies that this might not be the best model due to epigenetic changes that would occur in these cells down the line in development<br />
-gain of histone modification is tissue specific and associated with a loss in methylation, the opposite is rarely seen. hypomethylation at specific loci are important in cell lineage fate<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109279/ Sex Specific Effects on ESC transcriptome and epigenome] -hormonal influence on epigenetic regulation, can be carried on even with new neuronal genesis<br />
<br />
<br />
These genes were differentially expressed between maternal and paternal trisomic *in ascending order (MTvsPT):<br />
Prmt9<br />
Sumo2<br />
Flot2<br />
Aftph<br />
Gsn<br />
Dab2ip<br />
Chm<br />
Syncrip<br />
Rprd2<br />
Wipf3<br />
Stag1<br />
Prmt5<br />
Col3a1<br />
Snrnp48<br />
Wdr19<br />
Zdhhc4<br />
Ipo9<br />
Zgpat<br />
Sh3tc1<br />
Cib1<br />
Arpp19<br />
Cse1l<br />
Zfp207<br />
Dolpp1<br />
Slc38a3<br />
Ccnd3<br />
Gm10222<br />
Pmvk<br />
Rab6b<br />
Sohlh2<br />
Elavl4<br />
Cep85<br />
Serpinb6a<br />
Matr3<br />
Prdm5<br />
Gas7<br />
Shroom4<br />
Tom1l1<br />
Nop56<br />
Kank1<br />
Mrps10<br />
Baz2b<br />
Wnk1<br />
Ubap2l<br />
Eloc<br />
Prrc1<br />
<br />
mt-Atp6<br />
Fubp1<br />
Mcm10<br />
Chm<br />
Srsf5<br />
Ezh2<br />
Cyb5b<br />
Zkscan5<br />
Hmgn2<br />
Mark2<br />
Matr3<br />
Zfp207<br />
Vps51<br />
Gstm1<br />
Zfp12<br />
Fubp3<br />
Slit2<br />
Ube2k<br />
Gatb<br />
Apbb2<br />
Rangrf<br />
Acox1<br />
Spata5<br />
Senp6<br />
Rps13<br />
Zc3h11a<br />
Dohh<br />
Dnajc11<br />
Actn4<br />
Gcat<br />
Hnrnpdl<br />
Elavl4<br />
Fgfr1<br />
Mtx1<br />
Bptf<br />
Ccdc136<br />
Tbc1d24<br />
Ptp4a3<br />
Psmd2<br />
Rcor3<br />
Esrrb<br />
Kif20a<br />
Zfp993<br />
Crocc<br />
D930048N14Rik<br />
Fus<br />
Ikbkg<br />
Relb<br />
Ing5<br />
Asap1<br />
Cryz<br />
<br />
<br />
<br />
Why do we see so many genes differentially expressed between maternal trisomic and paternal trisomic that are not on the triplicated region of 17^16? What role do the triplicated genes play on the expression of these genes on other chromosomes. Does the triplicated region affect the geography of the nuclear DNA enough to cause these changes?</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18993Eric2017-04-06T14:56:44Z<p>Erhaas: /* Genes in depth */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
<br />
<br />
[https://en.wikipedia.org/wiki/CCT8 '''CCT8''']<br><br />
[https://genome.ucsc.edu/cgi-bin/hgGene?db=hg38&hgg_gene=CCT8 UCSC info]<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/28342823<br />
https://www.ncbi.nlm.nih.gov/pubmed/28096629<br />
https://www.ncbi.nlm.nih.gov/pubmed/28069794<br />
http://www.nature.com/nrg/journal/v18/n3/full/nrg.2016.154.html<br />
<br />
<br />
[http://www.uniprot.org/uniprot/Q13009 '''TIAM1'''] -cardiac muscle hypertrophy, Wnt signalling pathway<br><br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4024075/ Heart defects and tiam1]<br />
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3257027/<br />
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3364940/-tiam1 contributes to heart defects in a gene dosage manner<br />
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3257027/<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]-implies that this might not be the best model due to epigenetic changes that would occur in these cells down the line in development<br />
-gain of histone modification is tissue specific and associated with a loss in methylation, the opposite is rarely seen. hypomethylation at specific loci are important in cell lineage fate<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109279/ Sex Specific Effects on ESC transcriptome and epigenome] -hormonal influence on epigenetic regulation, can be carried on even with new neuronal genesis<br />
<br />
<br />
These genes were differentially expressed between maternal and paternal trisomic *in ascending order (MTvsPT):<br />
Prmt9<br />
Sumo2<br />
Flot2<br />
Aftph<br />
Gsn<br />
Dab2ip<br />
Chm<br />
Syncrip<br />
Rprd2<br />
Wipf3<br />
Stag1<br />
Prmt5<br />
Col3a1<br />
Snrnp48<br />
Wdr19<br />
Zdhhc4<br />
Ipo9<br />
Zgpat<br />
Sh3tc1<br />
Cib1<br />
Arpp19<br />
Cse1l<br />
Zfp207<br />
Dolpp1<br />
Slc38a3<br />
Ccnd3<br />
Gm10222<br />
Pmvk<br />
Rab6b<br />
Sohlh2<br />
Elavl4<br />
Cep85<br />
Serpinb6a<br />
Matr3<br />
Prdm5<br />
Gas7<br />
Shroom4<br />
Tom1l1<br />
Nop56<br />
Kank1<br />
Mrps10<br />
Baz2b<br />
Wnk1<br />
Ubap2l<br />
Eloc<br />
Prrc1<br />
<br />
mt-Atp6<br />
Fubp1<br />
Mcm10<br />
Chm<br />
Srsf5<br />
Ezh2<br />
Cyb5b<br />
Zkscan5<br />
Hmgn2<br />
Mark2<br />
Matr3<br />
Zfp207<br />
Vps51<br />
Gstm1<br />
Zfp12<br />
Fubp3<br />
Slit2<br />
Ube2k<br />
Gatb<br />
Apbb2<br />
Rangrf<br />
Acox1<br />
Spata5<br />
Senp6<br />
Rps13<br />
Zc3h11a<br />
Dohh<br />
Dnajc11<br />
Actn4<br />
Gcat<br />
Hnrnpdl<br />
Elavl4<br />
Fgfr1<br />
Mtx1<br />
Bptf<br />
Ccdc136<br />
Tbc1d24<br />
Ptp4a3<br />
Psmd2<br />
Rcor3<br />
Esrrb<br />
Kif20a<br />
Zfp993<br />
Crocc<br />
D930048N14Rik<br />
Fus<br />
Ikbkg<br />
Relb<br />
Ing5<br />
Asap1<br />
Cryz<br />
<br />
<br />
<br />
Why do we see so many genes differentially expressed between maternal trisomic and paternal trisomic that are not on the triplicated region of 17^16? What role do the triplicated genes play on the expression of these genes on other chromosomes. Does the triplicated region affect the geography of the nuclear DNA enough to cause these changes?</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18990Eric2017-04-04T14:55:49Z<p>Erhaas: /* Genes in depth */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
<br />
<br />
[https://en.wikipedia.org/wiki/CCT8 '''CCT8''']<br><br />
[https://genome.ucsc.edu/cgi-bin/hgGene?db=hg38&hgg_gene=CCT8 UCSC info]<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/28342823<br />
https://www.ncbi.nlm.nih.gov/pubmed/28096629<br />
https://www.ncbi.nlm.nih.gov/pubmed/28069794<br />
http://www.nature.com/nrg/journal/v18/n3/full/nrg.2016.154.html<br />
<br />
<br />
[http://www.uniprot.org/uniprot/Q13009 '''TIAM1'''] -cardiac muscle hypertrophy, Wnt signalling pathway<br><br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4024075/ Heart defects and tiam1]<br />
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3257027/<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]-implies that this might not be the best model due to epigenetic changes that would occur in these cells down the line in development<br />
-gain of histone modification is tissue specific and associated with a loss in methylation, the opposite is rarely seen. hypomethylation at specific loci are important in cell lineage fate<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109279/ Sex Specific Effects on ESC transcriptome and epigenome] -hormonal influence on epigenetic regulation, can be carried on even with new neuronal genesis<br />
<br />
<br />
These genes were differentially expressed between maternal and paternal trisomic *in ascending order (MTvsPT):<br />
Prmt9<br />
Sumo2<br />
Flot2<br />
Aftph<br />
Gsn<br />
Dab2ip<br />
Chm<br />
Syncrip<br />
Rprd2<br />
Wipf3<br />
Stag1<br />
Prmt5<br />
Col3a1<br />
Snrnp48<br />
Wdr19<br />
Zdhhc4<br />
Ipo9<br />
Zgpat<br />
Sh3tc1<br />
Cib1<br />
Arpp19<br />
Cse1l<br />
Zfp207<br />
Dolpp1<br />
Slc38a3<br />
Ccnd3<br />
Gm10222<br />
Pmvk<br />
Rab6b<br />
Sohlh2<br />
Elavl4<br />
Cep85<br />
Serpinb6a<br />
Matr3<br />
Prdm5<br />
Gas7<br />
Shroom4<br />
Tom1l1<br />
Nop56<br />
Kank1<br />
Mrps10<br />
Baz2b<br />
Wnk1<br />
Ubap2l<br />
Eloc<br />
Prrc1<br />
<br />
mt-Atp6<br />
Fubp1<br />
Mcm10<br />
Chm<br />
Srsf5<br />
Ezh2<br />
Cyb5b<br />
Zkscan5<br />
Hmgn2<br />
Mark2<br />
Matr3<br />
Zfp207<br />
Vps51<br />
Gstm1<br />
Zfp12<br />
Fubp3<br />
Slit2<br />
Ube2k<br />
Gatb<br />
Apbb2<br />
Rangrf<br />
Acox1<br />
Spata5<br />
Senp6<br />
Rps13<br />
Zc3h11a<br />
Dohh<br />
Dnajc11<br />
Actn4<br />
Gcat<br />
Hnrnpdl<br />
Elavl4<br />
Fgfr1<br />
Mtx1<br />
Bptf<br />
Ccdc136<br />
Tbc1d24<br />
Ptp4a3<br />
Psmd2<br />
Rcor3<br />
Esrrb<br />
Kif20a<br />
Zfp993<br />
Crocc<br />
D930048N14Rik<br />
Fus<br />
Ikbkg<br />
Relb<br />
Ing5<br />
Asap1<br />
Cryz<br />
<br />
<br />
<br />
Why do we see so many genes differentially expressed between maternal trisomic and paternal trisomic that are not on the triplicated region of 17^16? What role do the triplicated genes play on the expression of these genes on other chromosomes. Does the triplicated region affect the geography of the nuclear DNA enough to cause these changes?</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18988Eric2017-04-04T14:16:41Z<p>Erhaas: /* Genes in depth */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
<br />
<br />
[https://en.wikipedia.org/wiki/CCT8 '''CCT8''']<br><br />
[https://genome.ucsc.edu/cgi-bin/hgGene?db=hg38&hgg_gene=CCT8 UCSC info]<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/28342823<br />
https://www.ncbi.nlm.nih.gov/pubmed/28096629<br />
https://www.ncbi.nlm.nih.gov/pubmed/28069794<br />
http://www.nature.com/nrg/journal/v18/n3/full/nrg.2016.154.html<br />
<br />
<br />
[http://www.uniprot.org/uniprot/Q13009 '''TIAM1''']<br><br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]-implies that this might not be the best model due to epigenetic changes that would occur in these cells down the line in development<br />
-gain of histone modification is tissue specific and associated with a loss in methylation, the opposite is rarely seen. hypomethylation at specific loci are important in cell lineage fate<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109279/ Sex Specific Effects on ESC transcriptome and epigenome] -hormonal influence on epigenetic regulation, can be carried on even with new neuronal genesis<br />
<br />
<br />
These genes were differentially expressed between maternal and paternal trisomic *in ascending order (MTvsPT):<br />
Prmt9<br />
Sumo2<br />
Flot2<br />
Aftph<br />
Gsn<br />
Dab2ip<br />
Chm<br />
Syncrip<br />
Rprd2<br />
Wipf3<br />
Stag1<br />
Prmt5<br />
Col3a1<br />
Snrnp48<br />
Wdr19<br />
Zdhhc4<br />
Ipo9<br />
Zgpat<br />
Sh3tc1<br />
Cib1<br />
Arpp19<br />
Cse1l<br />
Zfp207<br />
Dolpp1<br />
Slc38a3<br />
Ccnd3<br />
Gm10222<br />
Pmvk<br />
Rab6b<br />
Sohlh2<br />
Elavl4<br />
Cep85<br />
Serpinb6a<br />
Matr3<br />
Prdm5<br />
Gas7<br />
Shroom4<br />
Tom1l1<br />
Nop56<br />
Kank1<br />
Mrps10<br />
Baz2b<br />
Wnk1<br />
Ubap2l<br />
Eloc<br />
Prrc1<br />
<br />
mt-Atp6<br />
Fubp1<br />
Mcm10<br />
Chm<br />
Srsf5<br />
Ezh2<br />
Cyb5b<br />
Zkscan5<br />
Hmgn2<br />
Mark2<br />
Matr3<br />
Zfp207<br />
Vps51<br />
Gstm1<br />
Zfp12<br />
Fubp3<br />
Slit2<br />
Ube2k<br />
Gatb<br />
Apbb2<br />
Rangrf<br />
Acox1<br />
Spata5<br />
Senp6<br />
Rps13<br />
Zc3h11a<br />
Dohh<br />
Dnajc11<br />
Actn4<br />
Gcat<br />
Hnrnpdl<br />
Elavl4<br />
Fgfr1<br />
Mtx1<br />
Bptf<br />
Ccdc136<br />
Tbc1d24<br />
Ptp4a3<br />
Psmd2<br />
Rcor3<br />
Esrrb<br />
Kif20a<br />
Zfp993<br />
Crocc<br />
D930048N14Rik<br />
Fus<br />
Ikbkg<br />
Relb<br />
Ing5<br />
Asap1<br />
Cryz<br />
<br />
<br />
<br />
Why do we see so many genes differentially expressed between maternal trisomic and paternal trisomic that are not on the triplicated region of 17^16? What role do the triplicated genes play on the expression of these genes on other chromosomes. Does the triplicated region affect the geography of the nuclear DNA enough to cause these changes?</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18985Eric2017-03-30T14:56:38Z<p>Erhaas: /* Genes in depth */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
<br />
<br />
[https://en.wikipedia.org/wiki/CCT8 '''CCT8''']<br><br />
[https://genome.ucsc.edu/cgi-bin/hgGene?db=hg38&hgg_gene=CCT8 UCSC info]<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/28342823<br />
https://www.ncbi.nlm.nih.gov/pubmed/28096629<br />
https://www.ncbi.nlm.nih.gov/pubmed/28069794<br />
http://www.nature.com/nrg/journal/v18/n3/full/nrg.2016.154.html<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]-implies that this might not be the best model due to epigenetic changes that would occur in these cells down the line in development<br />
-gain of histone modification is tissue specific and associated with a loss in methylation, the opposite is rarely seen. hypomethylation at specific loci are important in cell lineage fate<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109279/ Sex Specific Effects on ESC transcriptome and epigenome] -hormonal influence on epigenetic regulation, can be carried on even with new neuronal genesis<br />
<br />
<br />
These genes were differentially expressed between maternal and paternal trisomic *in ascending order (MTvsPT):<br />
Prmt9<br />
Sumo2<br />
Flot2<br />
Aftph<br />
Gsn<br />
Dab2ip<br />
Chm<br />
Syncrip<br />
Rprd2<br />
Wipf3<br />
Stag1<br />
Prmt5<br />
Col3a1<br />
Snrnp48<br />
Wdr19<br />
Zdhhc4<br />
Ipo9<br />
Zgpat<br />
Sh3tc1<br />
Cib1<br />
Arpp19<br />
Cse1l<br />
Zfp207<br />
Dolpp1<br />
Slc38a3<br />
Ccnd3<br />
Gm10222<br />
Pmvk<br />
Rab6b<br />
Sohlh2<br />
Elavl4<br />
Cep85<br />
Serpinb6a<br />
Matr3<br />
Prdm5<br />
Gas7<br />
Shroom4<br />
Tom1l1<br />
Nop56<br />
Kank1<br />
Mrps10<br />
Baz2b<br />
Wnk1<br />
Ubap2l<br />
Eloc<br />
Prrc1<br />
<br />
mt-Atp6<br />
Fubp1<br />
Mcm10<br />
Chm<br />
Srsf5<br />
Ezh2<br />
Cyb5b<br />
Zkscan5<br />
Hmgn2<br />
Mark2<br />
Matr3<br />
Zfp207<br />
Vps51<br />
Gstm1<br />
Zfp12<br />
Fubp3<br />
Slit2<br />
Ube2k<br />
Gatb<br />
Apbb2<br />
Rangrf<br />
Acox1<br />
Spata5<br />
Senp6<br />
Rps13<br />
Zc3h11a<br />
Dohh<br />
Dnajc11<br />
Actn4<br />
Gcat<br />
Hnrnpdl<br />
Elavl4<br />
Fgfr1<br />
Mtx1<br />
Bptf<br />
Ccdc136<br />
Tbc1d24<br />
Ptp4a3<br />
Psmd2<br />
Rcor3<br />
Esrrb<br />
Kif20a<br />
Zfp993<br />
Crocc<br />
D930048N14Rik<br />
Fus<br />
Ikbkg<br />
Relb<br />
Ing5<br />
Asap1<br />
Cryz<br />
<br />
<br />
<br />
Why do we see so many genes differentially expressed between maternal trisomic and paternal trisomic that are not on the triplicated region of 17^16? What role do the triplicated genes play on the expression of these genes on other chromosomes. Does the triplicated region affect the geography of the nuclear DNA enough to cause these changes?</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18982Eric2017-03-30T14:40:36Z<p>Erhaas: </p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
<br />
<br />
[https://en.wikipedia.org/wiki/CCT8 '''CCT8''']<br><br />
[https://genome.ucsc.edu/cgi-bin/hgGene?db=hg38&hgg_gene=CCT8 UCSC info]<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]-implies that this might not be the best model due to epigenetic changes that would occur in these cells down the line in development<br />
-gain of histone modification is tissue specific and associated with a loss in methylation, the opposite is rarely seen. hypomethylation at specific loci are important in cell lineage fate<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109279/ Sex Specific Effects on ESC transcriptome and epigenome] -hormonal influence on epigenetic regulation, can be carried on even with new neuronal genesis<br />
<br />
<br />
These genes were differentially expressed between maternal and paternal trisomic *in ascending order (MTvsPT):<br />
Prmt9<br />
Sumo2<br />
Flot2<br />
Aftph<br />
Gsn<br />
Dab2ip<br />
Chm<br />
Syncrip<br />
Rprd2<br />
Wipf3<br />
Stag1<br />
Prmt5<br />
Col3a1<br />
Snrnp48<br />
Wdr19<br />
Zdhhc4<br />
Ipo9<br />
Zgpat<br />
Sh3tc1<br />
Cib1<br />
Arpp19<br />
Cse1l<br />
Zfp207<br />
Dolpp1<br />
Slc38a3<br />
Ccnd3<br />
Gm10222<br />
Pmvk<br />
Rab6b<br />
Sohlh2<br />
Elavl4<br />
Cep85<br />
Serpinb6a<br />
Matr3<br />
Prdm5<br />
Gas7<br />
Shroom4<br />
Tom1l1<br />
Nop56<br />
Kank1<br />
Mrps10<br />
Baz2b<br />
Wnk1<br />
Ubap2l<br />
Eloc<br />
Prrc1<br />
<br />
mt-Atp6<br />
Fubp1<br />
Mcm10<br />
Chm<br />
Srsf5<br />
Ezh2<br />
Cyb5b<br />
Zkscan5<br />
Hmgn2<br />
Mark2<br />
Matr3<br />
Zfp207<br />
Vps51<br />
Gstm1<br />
Zfp12<br />
Fubp3<br />
Slit2<br />
Ube2k<br />
Gatb<br />
Apbb2<br />
Rangrf<br />
Acox1<br />
Spata5<br />
Senp6<br />
Rps13<br />
Zc3h11a<br />
Dohh<br />
Dnajc11<br />
Actn4<br />
Gcat<br />
Hnrnpdl<br />
Elavl4<br />
Fgfr1<br />
Mtx1<br />
Bptf<br />
Ccdc136<br />
Tbc1d24<br />
Ptp4a3<br />
Psmd2<br />
Rcor3<br />
Esrrb<br />
Kif20a<br />
Zfp993<br />
Crocc<br />
D930048N14Rik<br />
Fus<br />
Ikbkg<br />
Relb<br />
Ing5<br />
Asap1<br />
Cryz<br />
<br />
<br />
<br />
Why do we see so many genes differentially expressed between maternal trisomic and paternal trisomic that are not on the triplicated region of 17^16? What role do the triplicated genes play on the expression of these genes on other chromosomes. Does the triplicated region affect the geography of the nuclear DNA enough to cause these changes?</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18981Eric2017-03-30T14:38:19Z<p>Erhaas: /* Genes in depth */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
<br />
<br />
[https://en.wikipedia.org/wiki/CCT8 '''CCT8''']<br><br />
[https://genome.ucsc.edu/cgi-bin/hgGene?db=hg38&hgg_gene=CCT8 UCSC info]<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]-implies that this might not be the best model due to epigenetic changes that would occur in these cells down the line in development<br />
-gain of histone modification is tissue specific and associated with a loss in methylation, the opposite is rarely seen. hypomethylation at specific loci are important in cell lineage fate<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109279/ Sex Specific Effects on ESC transcriptome and epigenome] -hormonal influence on epigenetic regulation, can be carried on even with new neuronal genesis<br />
<br />
<br />
These genes were differentially expressed between maternal and paternal trisomic *in ascending order (MTvsPT):<br />
Prmt9<br />
Sumo2<br />
Flot2<br />
Aftph<br />
Gsn<br />
Dab2ip<br />
Chm<br />
Syncrip<br />
Rprd2<br />
Wipf3<br />
Stag1<br />
Prmt5<br />
Col3a1<br />
Snrnp48<br />
Wdr19<br />
Zdhhc4<br />
Ipo9<br />
Zgpat<br />
Sh3tc1<br />
Cib1<br />
Arpp19<br />
Cse1l<br />
Zfp207<br />
Dolpp1<br />
Slc38a3<br />
Ccnd3<br />
Gm10222<br />
Pmvk<br />
Rab6b<br />
Sohlh2<br />
Elavl4<br />
Cep85<br />
Serpinb6a<br />
Matr3<br />
Prdm5<br />
Gas7<br />
Shroom4<br />
Tom1l1<br />
Nop56<br />
Kank1<br />
Mrps10<br />
Baz2b<br />
Wnk1<br />
Ubap2l<br />
Eloc<br />
Prrc1<br />
<br />
mt-Atp6<br />
Fubp1<br />
Mcm10<br />
Chm<br />
Srsf5<br />
Ezh2<br />
Cyb5b<br />
Zkscan5<br />
Hmgn2<br />
Mark2<br />
Matr3<br />
Zfp207<br />
Vps51<br />
Gstm1<br />
Zfp12<br />
Fubp3<br />
Slit2<br />
Ube2k<br />
Gatb<br />
Apbb2<br />
Rangrf<br />
Acox1<br />
Spata5<br />
Senp6<br />
Rps13<br />
Zc3h11a<br />
Dohh<br />
Dnajc11<br />
Actn4<br />
Gcat<br />
Hnrnpdl<br />
Elavl4<br />
Fgfr1<br />
Mtx1<br />
Bptf<br />
Ccdc136<br />
Tbc1d24<br />
Ptp4a3<br />
Psmd2<br />
Rcor3<br />
Esrrb<br />
Kif20a<br />
Zfp993<br />
Crocc<br />
D930048N14Rik<br />
Fus<br />
Ikbkg<br />
Relb<br />
Ing5<br />
Asap1<br />
Cryz</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18978Eric2017-03-30T13:53:59Z<p>Erhaas: /* Genes in depth */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
<br />
<br />
[https://en.wikipedia.org/wiki/CCT8 '''CCT8''']<br><br />
[https://genome.ucsc.edu/cgi-bin/hgGene?db=hg38&hgg_gene=CCT8 UCSC info]<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]-implies that this might not be the best model due to epigenetic changes that would occur in these cells down the line in development<br />
-gain of histone modification is tissue specific and associated with a loss in methylation, the opposite is rarely seen. hypomethylation at specific loci are important in cell lineage fate<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109279/ Sex Specific Effects on ESC transcriptome and epigenome] -hormonal influence on epigenetic regulation, can be carried on even with new neuronal genesis<br />
<br />
<br />
These genes were differentially expressed between maternal and paternal trisomic *in ascending order (MTvsPT):<br />
Prmt9<br />
Sumo2<br />
Flot2<br />
Aftph<br />
Gsn<br />
Dab2ip<br />
Chm<br />
Syncrip<br />
Rprd2<br />
Wipf3<br />
Stag1<br />
Prmt5<br />
Col3a1<br />
Snrnp48<br />
Wdr19<br />
Zdhhc4<br />
Ipo9<br />
Zgpat<br />
Sh3tc1<br />
Cib1<br />
Arpp19<br />
Cse1l<br />
Zfp207<br />
Dolpp1<br />
Slc38a3<br />
Ccnd3<br />
Gm10222<br />
Pmvk<br />
Rab6b<br />
Sohlh2<br />
Elavl4<br />
Cep85<br />
Serpinb6a<br />
Matr3<br />
Prdm5<br />
Gas7<br />
Shroom4<br />
Tom1l1<br />
Nop56<br />
Kank1<br />
Mrps10<br />
Baz2b<br />
Wnk1<br />
Ubap2l<br />
Eloc<br />
Prrc1<br />
<br />
mt-Atp6<br />
Fubp1<br />
Mcm10<br />
Chm<br />
Srsf5<br />
Ezh2<br />
Cyb5b<br />
Zkscan5<br />
Hmgn2<br />
Mark2<br />
Matr3<br />
Zfp207<br />
Vps51<br />
Gstm1<br />
Zfp12<br />
Fubp3<br />
Slit2<br />
Ube2k<br />
Gatb<br />
Apbb2<br />
Rangrf<br />
Acox1<br />
Spata5<br />
Senp6<br />
Rps13<br />
Zc3h11a<br />
Dohh<br />
Dnajc11<br />
Actn4<br />
Gcat<br />
Hnrnpdl<br />
Elavl4<br />
Fgfr1<br />
Mtx1<br />
Bptf<br />
Ccdc136<br />
Tbc1d24<br />
Ptp4a3<br />
Psmd2<br />
Rcor3<br />
Esrrb<br />
Kif20a<br />
Zfp993<br />
Crocc<br />
D930048N14Rik<br />
Fus<br />
Ikbkg<br />
Relb<br />
Ing5<br />
Asap1<br />
Cryz</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18977Eric2017-03-30T13:53:42Z<p>Erhaas: /* == */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
<br />
<br />
[https://en.wikipedia.org/wiki/CCT8 "'CCT8'"]<br><br />
[https://genome.ucsc.edu/cgi-bin/hgGene?db=hg38&hgg_gene=CCT8 UCSC info]<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]-implies that this might not be the best model due to epigenetic changes that would occur in these cells down the line in development<br />
-gain of histone modification is tissue specific and associated with a loss in methylation, the opposite is rarely seen. hypomethylation at specific loci are important in cell lineage fate<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109279/ Sex Specific Effects on ESC transcriptome and epigenome] -hormonal influence on epigenetic regulation, can be carried on even with new neuronal genesis<br />
<br />
<br />
These genes were differentially expressed between maternal and paternal trisomic *in ascending order (MTvsPT):<br />
Prmt9<br />
Sumo2<br />
Flot2<br />
Aftph<br />
Gsn<br />
Dab2ip<br />
Chm<br />
Syncrip<br />
Rprd2<br />
Wipf3<br />
Stag1<br />
Prmt5<br />
Col3a1<br />
Snrnp48<br />
Wdr19<br />
Zdhhc4<br />
Ipo9<br />
Zgpat<br />
Sh3tc1<br />
Cib1<br />
Arpp19<br />
Cse1l<br />
Zfp207<br />
Dolpp1<br />
Slc38a3<br />
Ccnd3<br />
Gm10222<br />
Pmvk<br />
Rab6b<br />
Sohlh2<br />
Elavl4<br />
Cep85<br />
Serpinb6a<br />
Matr3<br />
Prdm5<br />
Gas7<br />
Shroom4<br />
Tom1l1<br />
Nop56<br />
Kank1<br />
Mrps10<br />
Baz2b<br />
Wnk1<br />
Ubap2l<br />
Eloc<br />
Prrc1<br />
<br />
mt-Atp6<br />
Fubp1<br />
Mcm10<br />
Chm<br />
Srsf5<br />
Ezh2<br />
Cyb5b<br />
Zkscan5<br />
Hmgn2<br />
Mark2<br />
Matr3<br />
Zfp207<br />
Vps51<br />
Gstm1<br />
Zfp12<br />
Fubp3<br />
Slit2<br />
Ube2k<br />
Gatb<br />
Apbb2<br />
Rangrf<br />
Acox1<br />
Spata5<br />
Senp6<br />
Rps13<br />
Zc3h11a<br />
Dohh<br />
Dnajc11<br />
Actn4<br />
Gcat<br />
Hnrnpdl<br />
Elavl4<br />
Fgfr1<br />
Mtx1<br />
Bptf<br />
Ccdc136<br />
Tbc1d24<br />
Ptp4a3<br />
Psmd2<br />
Rcor3<br />
Esrrb<br />
Kif20a<br />
Zfp993<br />
Crocc<br />
D930048N14Rik<br />
Fus<br />
Ikbkg<br />
Relb<br />
Ing5<br />
Asap1<br />
Cryz</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18976Eric2017-03-30T13:53:34Z<p>Erhaas: /* Genes in depth */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
<br />
====<br />
[https://en.wikipedia.org/wiki/CCT8 "'CCT8'"]<br><br />
[https://genome.ucsc.edu/cgi-bin/hgGene?db=hg38&hgg_gene=CCT8 UCSC info]<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]-implies that this might not be the best model due to epigenetic changes that would occur in these cells down the line in development<br />
-gain of histone modification is tissue specific and associated with a loss in methylation, the opposite is rarely seen. hypomethylation at specific loci are important in cell lineage fate<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109279/ Sex Specific Effects on ESC transcriptome and epigenome] -hormonal influence on epigenetic regulation, can be carried on even with new neuronal genesis<br />
<br />
<br />
These genes were differentially expressed between maternal and paternal trisomic *in ascending order (MTvsPT):<br />
Prmt9<br />
Sumo2<br />
Flot2<br />
Aftph<br />
Gsn<br />
Dab2ip<br />
Chm<br />
Syncrip<br />
Rprd2<br />
Wipf3<br />
Stag1<br />
Prmt5<br />
Col3a1<br />
Snrnp48<br />
Wdr19<br />
Zdhhc4<br />
Ipo9<br />
Zgpat<br />
Sh3tc1<br />
Cib1<br />
Arpp19<br />
Cse1l<br />
Zfp207<br />
Dolpp1<br />
Slc38a3<br />
Ccnd3<br />
Gm10222<br />
Pmvk<br />
Rab6b<br />
Sohlh2<br />
Elavl4<br />
Cep85<br />
Serpinb6a<br />
Matr3<br />
Prdm5<br />
Gas7<br />
Shroom4<br />
Tom1l1<br />
Nop56<br />
Kank1<br />
Mrps10<br />
Baz2b<br />
Wnk1<br />
Ubap2l<br />
Eloc<br />
Prrc1<br />
<br />
mt-Atp6<br />
Fubp1<br />
Mcm10<br />
Chm<br />
Srsf5<br />
Ezh2<br />
Cyb5b<br />
Zkscan5<br />
Hmgn2<br />
Mark2<br />
Matr3<br />
Zfp207<br />
Vps51<br />
Gstm1<br />
Zfp12<br />
Fubp3<br />
Slit2<br />
Ube2k<br />
Gatb<br />
Apbb2<br />
Rangrf<br />
Acox1<br />
Spata5<br />
Senp6<br />
Rps13<br />
Zc3h11a<br />
Dohh<br />
Dnajc11<br />
Actn4<br />
Gcat<br />
Hnrnpdl<br />
Elavl4<br />
Fgfr1<br />
Mtx1<br />
Bptf<br />
Ccdc136<br />
Tbc1d24<br />
Ptp4a3<br />
Psmd2<br />
Rcor3<br />
Esrrb<br />
Kif20a<br />
Zfp993<br />
Crocc<br />
D930048N14Rik<br />
Fus<br />
Ikbkg<br />
Relb<br />
Ing5<br />
Asap1<br />
Cryz</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18975Eric2017-03-30T13:53:09Z<p>Erhaas: /* Genes in depth */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
====<br />
[https://en.wikipedia.org/wiki/CCT8 "'CCT8'"]<br><br />
[https://genome.ucsc.edu/cgi-bin/hgGene?db=hg38&hgg_gene=CCT8 UCSC info]<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]-implies that this might not be the best model due to epigenetic changes that would occur in these cells down the line in development<br />
-gain of histone modification is tissue specific and associated with a loss in methylation, the opposite is rarely seen. hypomethylation at specific loci are important in cell lineage fate<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109279/ Sex Specific Effects on ESC transcriptome and epigenome] -hormonal influence on epigenetic regulation, can be carried on even with new neuronal genesis<br />
<br />
<br />
These genes were differentially expressed between maternal and paternal trisomic *in ascending order (MTvsPT):<br />
Prmt9<br />
Sumo2<br />
Flot2<br />
Aftph<br />
Gsn<br />
Dab2ip<br />
Chm<br />
Syncrip<br />
Rprd2<br />
Wipf3<br />
Stag1<br />
Prmt5<br />
Col3a1<br />
Snrnp48<br />
Wdr19<br />
Zdhhc4<br />
Ipo9<br />
Zgpat<br />
Sh3tc1<br />
Cib1<br />
Arpp19<br />
Cse1l<br />
Zfp207<br />
Dolpp1<br />
Slc38a3<br />
Ccnd3<br />
Gm10222<br />
Pmvk<br />
Rab6b<br />
Sohlh2<br />
Elavl4<br />
Cep85<br />
Serpinb6a<br />
Matr3<br />
Prdm5<br />
Gas7<br />
Shroom4<br />
Tom1l1<br />
Nop56<br />
Kank1<br />
Mrps10<br />
Baz2b<br />
Wnk1<br />
Ubap2l<br />
Eloc<br />
Prrc1<br />
<br />
mt-Atp6<br />
Fubp1<br />
Mcm10<br />
Chm<br />
Srsf5<br />
Ezh2<br />
Cyb5b<br />
Zkscan5<br />
Hmgn2<br />
Mark2<br />
Matr3<br />
Zfp207<br />
Vps51<br />
Gstm1<br />
Zfp12<br />
Fubp3<br />
Slit2<br />
Ube2k<br />
Gatb<br />
Apbb2<br />
Rangrf<br />
Acox1<br />
Spata5<br />
Senp6<br />
Rps13<br />
Zc3h11a<br />
Dohh<br />
Dnajc11<br />
Actn4<br />
Gcat<br />
Hnrnpdl<br />
Elavl4<br />
Fgfr1<br />
Mtx1<br />
Bptf<br />
Ccdc136<br />
Tbc1d24<br />
Ptp4a3<br />
Psmd2<br />
Rcor3<br />
Esrrb<br />
Kif20a<br />
Zfp993<br />
Crocc<br />
D930048N14Rik<br />
Fus<br />
Ikbkg<br />
Relb<br />
Ing5<br />
Asap1<br />
Cryz</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18974Eric2017-03-30T13:52:53Z<p>Erhaas: /* Genes in depth */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
<br />
[https://en.wikipedia.org/wiki/CCT8 "'CCT8'"]<br><br />
[https://genome.ucsc.edu/cgi-bin/hgGene?db=hg38&hgg_gene=CCT8 UCSC info]<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]-implies that this might not be the best model due to epigenetic changes that would occur in these cells down the line in development<br />
-gain of histone modification is tissue specific and associated with a loss in methylation, the opposite is rarely seen. hypomethylation at specific loci are important in cell lineage fate<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109279/ Sex Specific Effects on ESC transcriptome and epigenome] -hormonal influence on epigenetic regulation, can be carried on even with new neuronal genesis<br />
<br />
<br />
These genes were differentially expressed between maternal and paternal trisomic *in ascending order (MTvsPT):<br />
Prmt9<br />
Sumo2<br />
Flot2<br />
Aftph<br />
Gsn<br />
Dab2ip<br />
Chm<br />
Syncrip<br />
Rprd2<br />
Wipf3<br />
Stag1<br />
Prmt5<br />
Col3a1<br />
Snrnp48<br />
Wdr19<br />
Zdhhc4<br />
Ipo9<br />
Zgpat<br />
Sh3tc1<br />
Cib1<br />
Arpp19<br />
Cse1l<br />
Zfp207<br />
Dolpp1<br />
Slc38a3<br />
Ccnd3<br />
Gm10222<br />
Pmvk<br />
Rab6b<br />
Sohlh2<br />
Elavl4<br />
Cep85<br />
Serpinb6a<br />
Matr3<br />
Prdm5<br />
Gas7<br />
Shroom4<br />
Tom1l1<br />
Nop56<br />
Kank1<br />
Mrps10<br />
Baz2b<br />
Wnk1<br />
Ubap2l<br />
Eloc<br />
Prrc1<br />
<br />
mt-Atp6<br />
Fubp1<br />
Mcm10<br />
Chm<br />
Srsf5<br />
Ezh2<br />
Cyb5b<br />
Zkscan5<br />
Hmgn2<br />
Mark2<br />
Matr3<br />
Zfp207<br />
Vps51<br />
Gstm1<br />
Zfp12<br />
Fubp3<br />
Slit2<br />
Ube2k<br />
Gatb<br />
Apbb2<br />
Rangrf<br />
Acox1<br />
Spata5<br />
Senp6<br />
Rps13<br />
Zc3h11a<br />
Dohh<br />
Dnajc11<br />
Actn4<br />
Gcat<br />
Hnrnpdl<br />
Elavl4<br />
Fgfr1<br />
Mtx1<br />
Bptf<br />
Ccdc136<br />
Tbc1d24<br />
Ptp4a3<br />
Psmd2<br />
Rcor3<br />
Esrrb<br />
Kif20a<br />
Zfp993<br />
Crocc<br />
D930048N14Rik<br />
Fus<br />
Ikbkg<br />
Relb<br />
Ing5<br />
Asap1<br />
Cryz</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18973Eric2017-03-30T13:52:42Z<p>Erhaas: /* Genes in depth */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
<br />
[https://en.wikipedia.org/wiki/CCT8 "'CCT8'"]<br />
[https://genome.ucsc.edu/cgi-bin/hgGene?db=hg38&hgg_gene=CCT8 UCSC info]<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]-implies that this might not be the best model due to epigenetic changes that would occur in these cells down the line in development<br />
-gain of histone modification is tissue specific and associated with a loss in methylation, the opposite is rarely seen. hypomethylation at specific loci are important in cell lineage fate<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109279/ Sex Specific Effects on ESC transcriptome and epigenome] -hormonal influence on epigenetic regulation, can be carried on even with new neuronal genesis<br />
<br />
<br />
These genes were differentially expressed between maternal and paternal trisomic *in ascending order (MTvsPT):<br />
Prmt9<br />
Sumo2<br />
Flot2<br />
Aftph<br />
Gsn<br />
Dab2ip<br />
Chm<br />
Syncrip<br />
Rprd2<br />
Wipf3<br />
Stag1<br />
Prmt5<br />
Col3a1<br />
Snrnp48<br />
Wdr19<br />
Zdhhc4<br />
Ipo9<br />
Zgpat<br />
Sh3tc1<br />
Cib1<br />
Arpp19<br />
Cse1l<br />
Zfp207<br />
Dolpp1<br />
Slc38a3<br />
Ccnd3<br />
Gm10222<br />
Pmvk<br />
Rab6b<br />
Sohlh2<br />
Elavl4<br />
Cep85<br />
Serpinb6a<br />
Matr3<br />
Prdm5<br />
Gas7<br />
Shroom4<br />
Tom1l1<br />
Nop56<br />
Kank1<br />
Mrps10<br />
Baz2b<br />
Wnk1<br />
Ubap2l<br />
Eloc<br />
Prrc1<br />
<br />
mt-Atp6<br />
Fubp1<br />
Mcm10<br />
Chm<br />
Srsf5<br />
Ezh2<br />
Cyb5b<br />
Zkscan5<br />
Hmgn2<br />
Mark2<br />
Matr3<br />
Zfp207<br />
Vps51<br />
Gstm1<br />
Zfp12<br />
Fubp3<br />
Slit2<br />
Ube2k<br />
Gatb<br />
Apbb2<br />
Rangrf<br />
Acox1<br />
Spata5<br />
Senp6<br />
Rps13<br />
Zc3h11a<br />
Dohh<br />
Dnajc11<br />
Actn4<br />
Gcat<br />
Hnrnpdl<br />
Elavl4<br />
Fgfr1<br />
Mtx1<br />
Bptf<br />
Ccdc136<br />
Tbc1d24<br />
Ptp4a3<br />
Psmd2<br />
Rcor3<br />
Esrrb<br />
Kif20a<br />
Zfp993<br />
Crocc<br />
D930048N14Rik<br />
Fus<br />
Ikbkg<br />
Relb<br />
Ing5<br />
Asap1<br />
Cryz</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18972Eric2017-03-30T13:51:59Z<p>Erhaas: /* Genes in depth */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
<br />
[https://en.wikipedia.org/wiki/CCT8 "'CCT8'"]<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]-implies that this might not be the best model due to epigenetic changes that would occur in these cells down the line in development<br />
-gain of histone modification is tissue specific and associated with a loss in methylation, the opposite is rarely seen. hypomethylation at specific loci are important in cell lineage fate<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109279/ Sex Specific Effects on ESC transcriptome and epigenome] -hormonal influence on epigenetic regulation, can be carried on even with new neuronal genesis<br />
<br />
<br />
These genes were differentially expressed between maternal and paternal trisomic *in ascending order (MTvsPT):<br />
Prmt9<br />
Sumo2<br />
Flot2<br />
Aftph<br />
Gsn<br />
Dab2ip<br />
Chm<br />
Syncrip<br />
Rprd2<br />
Wipf3<br />
Stag1<br />
Prmt5<br />
Col3a1<br />
Snrnp48<br />
Wdr19<br />
Zdhhc4<br />
Ipo9<br />
Zgpat<br />
Sh3tc1<br />
Cib1<br />
Arpp19<br />
Cse1l<br />
Zfp207<br />
Dolpp1<br />
Slc38a3<br />
Ccnd3<br />
Gm10222<br />
Pmvk<br />
Rab6b<br />
Sohlh2<br />
Elavl4<br />
Cep85<br />
Serpinb6a<br />
Matr3<br />
Prdm5<br />
Gas7<br />
Shroom4<br />
Tom1l1<br />
Nop56<br />
Kank1<br />
Mrps10<br />
Baz2b<br />
Wnk1<br />
Ubap2l<br />
Eloc<br />
Prrc1<br />
<br />
mt-Atp6<br />
Fubp1<br />
Mcm10<br />
Chm<br />
Srsf5<br />
Ezh2<br />
Cyb5b<br />
Zkscan5<br />
Hmgn2<br />
Mark2<br />
Matr3<br />
Zfp207<br />
Vps51<br />
Gstm1<br />
Zfp12<br />
Fubp3<br />
Slit2<br />
Ube2k<br />
Gatb<br />
Apbb2<br />
Rangrf<br />
Acox1<br />
Spata5<br />
Senp6<br />
Rps13<br />
Zc3h11a<br />
Dohh<br />
Dnajc11<br />
Actn4<br />
Gcat<br />
Hnrnpdl<br />
Elavl4<br />
Fgfr1<br />
Mtx1<br />
Bptf<br />
Ccdc136<br />
Tbc1d24<br />
Ptp4a3<br />
Psmd2<br />
Rcor3<br />
Esrrb<br />
Kif20a<br />
Zfp993<br />
Crocc<br />
D930048N14Rik<br />
Fus<br />
Ikbkg<br />
Relb<br />
Ing5<br />
Asap1<br />
Cryz</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18971Eric2017-03-30T13:51:36Z<p>Erhaas: /* Genes in depth */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
<br />
[https://en.wikipedia.org/wiki/CCT8 'CCT8']<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]-implies that this might not be the best model due to epigenetic changes that would occur in these cells down the line in development<br />
-gain of histone modification is tissue specific and associated with a loss in methylation, the opposite is rarely seen. hypomethylation at specific loci are important in cell lineage fate<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109279/ Sex Specific Effects on ESC transcriptome and epigenome] -hormonal influence on epigenetic regulation, can be carried on even with new neuronal genesis<br />
<br />
<br />
These genes were differentially expressed between maternal and paternal trisomic *in ascending order (MTvsPT):<br />
Prmt9<br />
Sumo2<br />
Flot2<br />
Aftph<br />
Gsn<br />
Dab2ip<br />
Chm<br />
Syncrip<br />
Rprd2<br />
Wipf3<br />
Stag1<br />
Prmt5<br />
Col3a1<br />
Snrnp48<br />
Wdr19<br />
Zdhhc4<br />
Ipo9<br />
Zgpat<br />
Sh3tc1<br />
Cib1<br />
Arpp19<br />
Cse1l<br />
Zfp207<br />
Dolpp1<br />
Slc38a3<br />
Ccnd3<br />
Gm10222<br />
Pmvk<br />
Rab6b<br />
Sohlh2<br />
Elavl4<br />
Cep85<br />
Serpinb6a<br />
Matr3<br />
Prdm5<br />
Gas7<br />
Shroom4<br />
Tom1l1<br />
Nop56<br />
Kank1<br />
Mrps10<br />
Baz2b<br />
Wnk1<br />
Ubap2l<br />
Eloc<br />
Prrc1<br />
<br />
mt-Atp6<br />
Fubp1<br />
Mcm10<br />
Chm<br />
Srsf5<br />
Ezh2<br />
Cyb5b<br />
Zkscan5<br />
Hmgn2<br />
Mark2<br />
Matr3<br />
Zfp207<br />
Vps51<br />
Gstm1<br />
Zfp12<br />
Fubp3<br />
Slit2<br />
Ube2k<br />
Gatb<br />
Apbb2<br />
Rangrf<br />
Acox1<br />
Spata5<br />
Senp6<br />
Rps13<br />
Zc3h11a<br />
Dohh<br />
Dnajc11<br />
Actn4<br />
Gcat<br />
Hnrnpdl<br />
Elavl4<br />
Fgfr1<br />
Mtx1<br />
Bptf<br />
Ccdc136<br />
Tbc1d24<br />
Ptp4a3<br />
Psmd2<br />
Rcor3<br />
Esrrb<br />
Kif20a<br />
Zfp993<br />
Crocc<br />
D930048N14Rik<br />
Fus<br />
Ikbkg<br />
Relb<br />
Ing5<br />
Asap1<br />
Cryz</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18970Eric2017-03-30T13:51:21Z<p>Erhaas: /* Genes in depth */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
<br />
[https://en.wikipedia.org/wiki/CCT8 '"CCT8"']<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]-implies that this might not be the best model due to epigenetic changes that would occur in these cells down the line in development<br />
-gain of histone modification is tissue specific and associated with a loss in methylation, the opposite is rarely seen. hypomethylation at specific loci are important in cell lineage fate<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109279/ Sex Specific Effects on ESC transcriptome and epigenome] -hormonal influence on epigenetic regulation, can be carried on even with new neuronal genesis<br />
<br />
<br />
These genes were differentially expressed between maternal and paternal trisomic *in ascending order (MTvsPT):<br />
Prmt9<br />
Sumo2<br />
Flot2<br />
Aftph<br />
Gsn<br />
Dab2ip<br />
Chm<br />
Syncrip<br />
Rprd2<br />
Wipf3<br />
Stag1<br />
Prmt5<br />
Col3a1<br />
Snrnp48<br />
Wdr19<br />
Zdhhc4<br />
Ipo9<br />
Zgpat<br />
Sh3tc1<br />
Cib1<br />
Arpp19<br />
Cse1l<br />
Zfp207<br />
Dolpp1<br />
Slc38a3<br />
Ccnd3<br />
Gm10222<br />
Pmvk<br />
Rab6b<br />
Sohlh2<br />
Elavl4<br />
Cep85<br />
Serpinb6a<br />
Matr3<br />
Prdm5<br />
Gas7<br />
Shroom4<br />
Tom1l1<br />
Nop56<br />
Kank1<br />
Mrps10<br />
Baz2b<br />
Wnk1<br />
Ubap2l<br />
Eloc<br />
Prrc1<br />
<br />
mt-Atp6<br />
Fubp1<br />
Mcm10<br />
Chm<br />
Srsf5<br />
Ezh2<br />
Cyb5b<br />
Zkscan5<br />
Hmgn2<br />
Mark2<br />
Matr3<br />
Zfp207<br />
Vps51<br />
Gstm1<br />
Zfp12<br />
Fubp3<br />
Slit2<br />
Ube2k<br />
Gatb<br />
Apbb2<br />
Rangrf<br />
Acox1<br />
Spata5<br />
Senp6<br />
Rps13<br />
Zc3h11a<br />
Dohh<br />
Dnajc11<br />
Actn4<br />
Gcat<br />
Hnrnpdl<br />
Elavl4<br />
Fgfr1<br />
Mtx1<br />
Bptf<br />
Ccdc136<br />
Tbc1d24<br />
Ptp4a3<br />
Psmd2<br />
Rcor3<br />
Esrrb<br />
Kif20a<br />
Zfp993<br />
Crocc<br />
D930048N14Rik<br />
Fus<br />
Ikbkg<br />
Relb<br />
Ing5<br />
Asap1<br />
Cryz</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18969Eric2017-03-28T14:57:23Z<p>Erhaas: </p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]-implies that this might not be the best model due to epigenetic changes that would occur in these cells down the line in development<br />
-gain of histone modification is tissue specific and associated with a loss in methylation, the opposite is rarely seen. hypomethylation at specific loci are important in cell lineage fate<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109279/ Sex Specific Effects on ESC transcriptome and epigenome] -hormonal influence on epigenetic regulation, can be carried on even with new neuronal genesis<br />
<br />
<br />
These genes were differentially expressed between maternal and paternal trisomic *in ascending order (MTvsPT):<br />
Prmt9<br />
Sumo2<br />
Flot2<br />
Aftph<br />
Gsn<br />
Dab2ip<br />
Chm<br />
Syncrip<br />
Rprd2<br />
Wipf3<br />
Stag1<br />
Prmt5<br />
Col3a1<br />
Snrnp48<br />
Wdr19<br />
Zdhhc4<br />
Ipo9<br />
Zgpat<br />
Sh3tc1<br />
Cib1<br />
Arpp19<br />
Cse1l<br />
Zfp207<br />
Dolpp1<br />
Slc38a3<br />
Ccnd3<br />
Gm10222<br />
Pmvk<br />
Rab6b<br />
Sohlh2<br />
Elavl4<br />
Cep85<br />
Serpinb6a<br />
Matr3<br />
Prdm5<br />
Gas7<br />
Shroom4<br />
Tom1l1<br />
Nop56<br />
Kank1<br />
Mrps10<br />
Baz2b<br />
Wnk1<br />
Ubap2l<br />
Eloc<br />
Prrc1<br />
<br />
mt-Atp6<br />
Fubp1<br />
Mcm10<br />
Chm<br />
Srsf5<br />
Ezh2<br />
Cyb5b<br />
Zkscan5<br />
Hmgn2<br />
Mark2<br />
Matr3<br />
Zfp207<br />
Vps51<br />
Gstm1<br />
Zfp12<br />
Fubp3<br />
Slit2<br />
Ube2k<br />
Gatb<br />
Apbb2<br />
Rangrf<br />
Acox1<br />
Spata5<br />
Senp6<br />
Rps13<br />
Zc3h11a<br />
Dohh<br />
Dnajc11<br />
Actn4<br />
Gcat<br />
Hnrnpdl<br />
Elavl4<br />
Fgfr1<br />
Mtx1<br />
Bptf<br />
Ccdc136<br />
Tbc1d24<br />
Ptp4a3<br />
Psmd2<br />
Rcor3<br />
Esrrb<br />
Kif20a<br />
Zfp993<br />
Crocc<br />
D930048N14Rik<br />
Fus<br />
Ikbkg<br />
Relb<br />
Ing5<br />
Asap1<br />
Cryz</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18968Eric2017-03-28T14:08:37Z<p>Erhaas: </p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]-implies that this might not be the best model due to epigenetic changes that would occur in these cells down the line in development<br />
-gain of histone modification is tissue specific and associated with a loss in methylation, the opposite is rarely seen. hypomethylation at specific loci are important in cell lineage fate<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109279/ Sex Specific Effects on ESC transcriptome and epigenome] -hormonal influence on epigenetic regulation, can be carried on even with new neuronal genesis<br />
<br />
<br />
These genes were differentially expressed between maternal and paternal trisomic (MTvsPT):<br />
Prmt9<br />
Sumo2<br />
Flot2<br />
Aftph<br />
Gsn<br />
Dab2ip<br />
Chm<br />
Syncrip<br />
Rprd2<br />
Wipf3<br />
Stag1<br />
Prmt5<br />
Col3a1<br />
Snrnp48<br />
Wdr19<br />
Zdhhc4<br />
Ipo9<br />
Zgpat<br />
Sh3tc1<br />
Cib1<br />
Arpp19<br />
Cse1l<br />
Zfp207<br />
Dolpp1<br />
Slc38a3<br />
Ccnd3<br />
Gm10222<br />
Pmvk<br />
Rab6b<br />
Sohlh2<br />
Elavl4<br />
Cep85<br />
Serpinb6a<br />
Matr3<br />
Prdm5<br />
Gas7<br />
Shroom4<br />
Tom1l1<br />
Nop56<br />
Kank1<br />
Mrps10<br />
Baz2b<br />
Wnk1<br />
Ubap2l<br />
Eloc<br />
Prrc1<br />
<br />
mt-Atp6<br />
Fubp1<br />
Mcm10<br />
Chm<br />
Srsf5<br />
Ezh2<br />
Cyb5b<br />
Zkscan5<br />
Hmgn2<br />
Mark2<br />
Matr3<br />
Zfp207<br />
Vps51<br />
Gstm1<br />
Zfp12<br />
Fubp3<br />
Slit2<br />
Ube2k<br />
Gatb<br />
Apbb2<br />
Rangrf<br />
Acox1<br />
Spata5<br />
Senp6<br />
Rps13<br />
Zc3h11a<br />
Dohh<br />
Dnajc11<br />
Actn4<br />
Gcat<br />
Hnrnpdl<br />
Elavl4<br />
Fgfr1<br />
Mtx1<br />
Bptf<br />
Ccdc136<br />
Tbc1d24<br />
Ptp4a3<br />
Psmd2<br />
Rcor3<br />
Esrrb<br />
Kif20a<br />
Zfp993<br />
Crocc<br />
D930048N14Rik<br />
Fus<br />
Ikbkg<br />
Relb<br />
Ing5<br />
Asap1<br />
Cryz</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18965Eric2017-03-23T14:52:22Z<p>Erhaas: /* Information about ESCs */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]-implies that this might not be the best model due to epigenetic changes that would occur in these cells down the line in development<br />
-gain of histone modification is tissue specific and associated with a loss in methylation, the opposite is rarely seen. hypomethylation at specific loci are important in cell lineage fate<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109279/ Sex Specific Effects on ESC transcriptome and epigenome] -hormonal influence on epigenetic regulation, can be carried on even with new neuronal genesis</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18964Eric2017-03-23T14:43:03Z<p>Erhaas: /* Information about ESCs */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]-implies that this might not be the best model due to epigenetic changes that would occur in these cells down the line in development<br />
-gain of histone modification is tissue specific and associated with a loss in methylation, the opposite is rarely seen. hypomethylation at specific loci are important in cell lineage fate<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109279/ Sex Specific Effects on ESC transcriptome and epigenome]</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18963Eric2017-03-23T13:54:20Z<p>Erhaas: /* Information about ESCs */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]-implies that this might not be the best model due to epigenetic changes that would occur in these cells down the line in development</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18962Eric2017-03-23T13:46:21Z<p>Erhaas: /* Information about ESCs */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ Changing Epigenetics during differentiation]</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18961Eric2017-03-23T13:46:06Z<p>Erhaas: </p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]<br />
<br />
==Information about ESCs==<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336297/ "Changing Epigenetics during differentiation"]</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18950Eric2017-03-14T14:57:05Z<p>Erhaas: /* Genes in depth */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
[https://en.wikipedia.org/wiki/DYRK1A '''Dyrk1a''']-related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18949Eric2017-03-14T14:54:48Z<p>Erhaas: /* Genes in depth */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
Dyrk1a- related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28031411 cancer resistance]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28206758 target for chemotherapy]<br />
[https://www.ncbi.nlm.nih.gov/pubmed/28137862 growth issues in DS]</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18947Eric2017-03-14T14:45:40Z<p>Erhaas: /* Genes in depth */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
Dyrk1a- related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate EGCG]</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18946Eric2017-03-14T14:45:15Z<p>Erhaas: /* Genes in depth */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
Dyrk1a- related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate treatment to correct some craniofacial features] [https://en.wikipedia.org/wiki/Epigallocatechin_gallate]</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18945Eric2017-03-14T14:44:44Z<p>Erhaas: /* Genes in depth */</p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
Dyrk1a- related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]<br />
[https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddw309 Using Epigallocatechin-3-gallate[https://en.wikipedia.org/wiki/Epigallocatechin_gallate] treatment to correct some craniofacial features]</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18943Eric2017-03-14T14:38:43Z<p>Erhaas: </p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data<br />
<br />
==Genes in depth==<br />
Dyrk1a- related to many down syndrome symptoms and phenotypes <br><br />
[https://www.ncbi.nlm.nih.gov/pubmed/28250274 Alzheimer's]</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18942Eric2017-03-14T13:52:48Z<p>Erhaas: </p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|1000px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=File:Genes_of_interest.png&diff=18941File:Genes of interest.png2017-03-14T13:51:48Z<p>Erhaas: </p>
<hr />
<div></div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18940Eric2017-03-14T13:51:33Z<p>Erhaas: </p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1<br />
<br />
<center><br />
[[File:genes of interest.png|300px]]<br />
</center><br />
<br />
3/14/17-<br />
for individual status report: rewrite intro based on feedback, write what methods you've done, dont focus on discussion, discuss results you have. lots of visuals and data and be able to explain figures and data</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18938Eric2017-03-01T21:30:18Z<p>Erhaas: </p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that were found in all 3: <br />
Brwd1<br />
Cct8<br />
Cryz<br />
Dyrk1a<br />
Gabpa<br />
Jam2<br />
Ltn1<br />
Urb1</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18937Eric2017-02-28T15:58:42Z<p>Erhaas: </p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
filter 1:c6<0.0001<br />
filter 2: c3>=0.5 or c3<=-0.5<br />
trim: column 1 everything to 18 <br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that are differentially expressed between trisomic and disomic individuals:<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2903219/ Ets2]<br />
<br />
[http://www.genecards.org/cgi-bin/carddisp.pl?gene=WRB /wrb]<br />
<br />
[http://www.genecards.org/cgi-bin/carddisp.pl?gene=ATXN1 Atxn1]</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18906Eric2017-02-23T17:31:31Z<p>Erhaas: </p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
could use two sample t test or one sample to explore stats of these differences between trisomic maternal and paternal and trisomic vs disomic<br />
<br />
Genes that are differentially expressed between trisomic and disomic individuals:<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2903219/ Ets2]<br />
<br />
[http://www.genecards.org/cgi-bin/carddisp.pl?gene=WRB /wrb]<br />
<br />
[http://www.genecards.org/cgi-bin/carddisp.pl?gene=ATXN1 Atxn1]</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18905Eric2017-02-23T15:57:08Z<p>Erhaas: </p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
<br />
<br />
Genes that are differentially expressed between trisomic and disomic individuals:<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2903219/ Ets2]<br />
<br />
[http://www.genecards.org/cgi-bin/carddisp.pl?gene=WRB /wrb]<br />
<br />
[http://www.genecards.org/cgi-bin/carddisp.pl?gene=ATXN1 Atxn1]</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18904Eric2017-02-23T15:50:27Z<p>Erhaas: </p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal<br />
<br />
<br />
<br />
Genes that are differentially expressed between trisomic and disomic individuals:<br />
<br />
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2903219/ Ets2]</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18901Eric2017-02-21T15:46:29Z<p>Erhaas: </p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737<br />
<br />
<br />
Compared Trisomy maternal vs trisomy paternal, this resulted in 99 differently expressed genes, 10 of which were also differently expressed in the trisomy maternal vs disomic maternal. The whole dataset of trisomic paternal and maternal was run against the disomic maternal and paternal</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18895Eric2017-02-21T14:44:18Z<p>Erhaas: </p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
[http://gcat.davidson.edu/mediawiki-1.27.1/index.php/Mouse_Down_Syndrome_ES_RNAseq_Project#Lab_Methods_in_Genomics.2C_Spring_2017 Main Page]<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18883Eric2017-02-16T15:57:27Z<p>Erhaas: </p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27243896<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27538963<br />
<br />
https://www.ncbi.nlm.nih.gov/pubmed/27029737</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18882Eric2017-02-16T15:45:43Z<p>Erhaas: </p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal<br />
<br />
We are comparing Disomic maternal vs disomic paternal first to get a baseline difference in source dna and expression levels. Then we are comparing Trisomic maternal vs trisomic paternal to find differently expressed genes between these two. The next comparison will be between the Trisomic comp vs disomic comp to eliminate any differently expressed genes due to simply maternal vs paternal sources. setting our baseline from the disomic comparisons. We also will compare trisomic maternal to disomic maternal and trisomic paternal to disomic paternal</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18874Eric2017-02-16T15:22:42Z<p>Erhaas: </p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.<br />
<br />
2/16-working with visualizations in galaxy and after having found genes of interest, we are looking through these genes, trying to hone in on a few. We are using the p-values and fold change to determine which genes are expressed differently in the trisomic vs disomic<br />
<br />
also, how do we get rid off the control variables between maternal and paternal</div>Erhaashttp://gcat.davidson.edu/GcatWiki/index.php?title=Eric&diff=18853Eric2017-02-14T15:22:32Z<p>Erhaas: </p>
<hr />
<div>January 19th Lab Methods in Genomics<br />
<br />
http://bio.davidson.edu/Courses/Bio343/LabMethods_2017.html<br />
<br />
[http://www.ds-health.com/trisomy.htm Story of Trisomy 21]<br />
<br />
<center><br />
[[File:tester.png|300px]]<br />
</center><br />
<br />
<br />
Ts65Dn[https://www.jax.org/strain/001924 Down Syndrome mouse model] <br><br />
T1DS/T2N -twin 1 down syndrome/twin 2 normal<br><br />
MZ1/MZ2<br><br />
-supernumerary<br><br />
RPKM-reads per kilobase million -gives you a normal for comparison <br />
<br />
LADs are overtranscribed in down syndrome when they should have low expression<br />
<br />
Think replication time and transcription levels are correlated, genes transcribed earlier are expressed lower and later transcribed are more expressed<br />
<br />
Not just the overexpression of chromosome 21. It is the consequence of these products causing the LADs to be expressed higher. Effects of changing chromatin structure<br />
<br />
<br />
1/31<br />
<br />
[http://bio.davidson.edu/Courses/Bio343/2017/4_16break_point.pdf Molecular characterization of Ts65Dn] <br><br />
<br />
<br />
Ts65Dn is the mouse model for trisomy 21 in humans<br />
<br />
distal= further away from the centromere<br />
<br />
Think that chromosome 16 and 17 were combined via NHEJ but there is a small overlap region of 7 bp that might have had a role in determining the 16/17 breakpoint<br />
<br />
<br />
writing as an introduction, not necessarily concerned with what we're going to do in the future<br />
<br />
<br />
2/2<br />
We are examining cDNA from the mouse models ~20 million reads per file<br />
<br />
Separate each read based on barcode=3x nucleotide at end of chain<br />
<br />
Trimmomatic used to delete barcode<br />
<br />
use RSEM to map reads to genes and counts how many reads an identified gene is counted<br />
<br />
DEseq2- enter name of data then click which data you want and run by execute<br />
<br />
2/7<br />
<br />
Use gene plot in galaxy after running DEseq 2. look for outliers and to search genes, use http://www.informatics.jax.org/batch/summary and delete decimals from gene number<br />
<br />
disomic vs trisomic-looking for differences in expression, gene names, fold change=look at log2(FC), wald stats used to sort, base mean=reads per kilobase million<br />
<br />
Which dataset is numerator?-first data set is numerator and second is denominator <br />
<br />
Want to generate KEGG pathway maps for genes of interest, not looking at methylation differences, cant do with this data. We are looking at differential expression. Maternal Cont compared to Maternal Ts65Dn<br />
<br />
to remove decimals in excel =left(A1, 18)<br />
<br />
Save tab files as opening in excel and use excel to sort and whatnot. p-value cutoff=0.05 adjusted p-values are adjusted for the multiple tests that are performed<br />
<br />
We will use the p-values with a value of less than 0.0001. This provides 105 genes of interest in the dataset of maternal trisomic vs maternal disomic<br />
<br />
We took the genes and ran them through the naming program and export to excel<br />
<br />
<br />
2/9/17<br />
[http://fntm.princeton.edu/ gene interaction tool] <br><br />
<br />
[http://string-db.org/ Protein map] <br><br />
<br />
Sorted genes of interest by chromosome location<br />
<br />
log2(FC)= 2^that number = how much more expressed gene is<br />
<br />
Filtered dataset by p-value of less than 0.0001 using workflow in galaxy, then sorted these data on log2(FC) greater than abs(.5)-using workflow to add filters and trims, etc.</div>Erhaas