GcatWiki - User contributions [en]

File:First attempt at new cluster.png

2016-03-08T19:09:03Z

Ellankiewicz:

Elise

2016-03-08T19:07:48Z

Ellankiewicz:

THIS IS ELISE.

.1g of organ used, we are not sure that it's the right part- could be connective tissue etc.
Anything in cDNA, by definition is transcribed mRNA, more stable as DNA
mRNA to cDNA using reverse transcriptase
Advantage to fragmenting RNA prior to cDNA synthesis-random fragmentation allows for a lot more reads, because I long strand of RNA would just have resulted in short reads from the two ends
Every possible hexamer is generated is used at end of primer in order for synthesis to occur

Research
Our results indicate that 􏰀2,000 genes show significant changes in expression in the small intestine following feeding, includ- ing genes involved in intestinal morphology and function (e.g., hy- drolases, microvillus proteins, trafficking and transport proteins), as well as genes involved in cell division and apoptosis.
Mass-specific rates of D-glucose, L-proline, and L-leucine uptake varied significantly among sampling times (P values 􏰈 0.0009), as each nutrient significantly increased uptake within 12–24 h of feeding (Fig. 5, D–F, respectively).
(http://www.bio.davidson.edu/courses/Bio343/2016/4a_Todd%20Castoe%20Python%20RNAseq_2015.pdf)

Trying to access full article-related to SGLT1 gene and glucose uptake
http://www.ncbi.nlm.nih.gov/pubmed/1616036

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2/25/16

2016-02-25T18:55:43Z

Ellankiewicz: Created page with "Class Notes 2/25/16 Continuing to use supervised clustering of genes of interest to focus in, transporters, anything in the signaling cascade"

Class Notes 2/25/16

Continuing to use supervised clustering of genes of interest to focus in, transporters, anything in the signaling cascade

Elise

2016-02-25T18:36:59Z

Ellankiewicz:

2/23/16

2016-02-23T19:53:57Z

Ellankiewicz:

Class Notes
FPKM-->should be entering .expected_count

Dr. Heyer finished code to get supervised clustering, larger data set is not really graphing, Dr. Heyer set up a way to filter out if row for gene if average of six genes is greater than 10

From missed class

Looking for housekeeping intestine genes so that we can make sure our samples are accurate
over-expressed sequences match:

Snake 1: matches with an mRNA sequence from an intestine sample of an elephant fish, SLC15A1 (FPKM 14366.5) , SLC6A19 (FPKM 7306.48), Aminopeptidase (FPKM 494.97) sucrase/maltase (FPKM 935.83)

Snake 2: SLC15A1 (FPKM 13466.62), SLC6A19 (FPKM 4132.78), sucrase/maltase (FPKM 1133.27), Aminopeptidase (FPKM 432.93)

Snake 3: SLC15A1 (FPKM 14322.98), SLC6A19 (FPKM 5695.09), sucrase/maltase (FPKM 1430.56), Aminopeptidase (FPKM 426.59)

Snake 4: SLC6A19 (FPKM 13213.57), SLC15A1 (FPKM 14316.17), maltase/sucrase (FPKM 747.19) aminopeptidase (FPKM 556.07)

Snake 5: SLC15A1 (FPKM 11347.62), SLC6A19 (FPKM 4130.4), sucrase/maltase (FPKM 1054.27) , aminopeptidase (FPKM 513.37)

Snake 6: SLC15A1 (FPKM 9310.97), SLC6A19 (FPKM 6632.26), sucrase/maltase (FPKM 735.21), aminopeptidase (FPKM 488.85)

Housekeeping genes

BOAT1 (SLC6A19) solute carrier family 6 (neutral amino acid transporter), member 19, mRNA (housekeeping gene BOAT1) found in intestine and kidney

SLC15A1 protein coding gene: encodes an intestinal hydrogen peptide cotransporter that is a member of the solute carrier family 15

Amino peptidase (Aspartyl_aminopeptidase_Homo_sapiens): source small intestine, products are amino acids and peptides

maltase/sucrose (Si_Sucrase-isomaltase,_intestinal_Rattus_norvegicus) : source small intestine, products are glucose and fructose

Housekeeping genes that weren't highly expressed

Important to note than many housekeeping genes for intestines were not extremely highly expressed at all in our samples, not as great evidence as was found for the liver samples

GATA6 (Regulates proximal-distal identity in the intestines) 1) 32.2 2) 33.96 3) 19.17 4) 37.87 5) 31.67 6) 31.98

MYBL2 (Regulates commitment of colon stem cells to differentiate) 1) 0.04 2) 0 3) 0.05 4) 0 5) 0 6) 0

ASCT2 (SLC1A5 expressed in the kidney but expression is high in the jejunum and colon but lower in duodenum and ileum) 1) 2.29 2) 2.96 3) 2.65 4) 1.39 5) 3.75 6) 3.07

STX2, COL3A1, GPBAR1: all from Castoe et al paper; examples of genes that have experienced positive selection (P < 0.001) on snake lineages and are related to prominent phenotypic or cellular traits of snakes- HOWEVER NOT HIGHLY EXPRESSED IN ANY OF OUR SNAKES (range of
FPKM from 0 - 4)

Began process of creating new supervised clusters using R code

Here's some interesting info:
Total Reads-->4i 7,061,976
5i 11,282,296
6i 11,912,035

We're having trouble with the supervised clustering because of the fewer reads in Snake 4, tried again using clustering

2/23/16

2016-02-23T19:19:45Z

Ellankiewicz:

2/23/16

2016-02-23T18:52:10Z

Ellankiewicz:

Class Notes
FPKM-->should be entering .expected_count

Dr. Heyer finished code to get supervised clustering, larger data set is not really graphing, Dr. Heyer set up a way to filter out if row for gene if average of six genes is greater than 10

From missed class

Looking for housekeeping intestine genes so that we can make sure our samples are accurate
over-expressed sequences match:
Snake 1: matches with an mRNA sequence from an intestine sample of an elephant fish, SLC15A1 (FPKM 14366.5) , SLC6A19 (FPKM 7306.48), Aminopeptidase (FPKM 494.97) sucrase/maltase (FPKM 935.83)
Snake 2: SLC15A1 (FPKM 13466.62), SLC6A19 (FPKM 4132.78), sucrase/maltase (FPKM 1133.27), Aminopeptidase (FPKM 432.93)
Snake 3: SLC15A1 (FPKM 14322.98), SLC6A19 (FPKM 5695.09), sucrase/maltase (FPKM 1430.56), Aminopeptidase (FPKM 426.59)
Snake 4: SLC6A19 (FPKM 13213.57), SLC15A1 (FPKM 14316.17), maltase/sucrase (FPKM 747.19) aminopeptidase (FPKM 556.07)
Snake 5: SLC15A1 (FPKM 11347.62), SLC6A19 (FPKM 4130.4), sucrase/maltase (FPKM 1054.27) , aminopeptidase (FPKM 513.37)
Snake 6: SLC15A1 (FPKM 9310.97), SLC6A19 (FPKM 6632.26), sucrase/maltase (FPKM 735.21), aminopeptidase (FPKM 488.85)
Housekeeping genes
BOAT1 (SLC6A19) solute carrier family 6 (neutral amino acid transporter), member 19, mRNA (housekeeping gene BOAT1) found in intestine and kidney
SLC15A1 protein coding gene: encodes an intestinal hydrogen peptide cotransporter that is a member of the solute carrier family 15
Amino peptidase (Aspartyl_aminopeptidase_Homo_sapiens): source small intestine, products are amino acids and peptides
maltase/sucrose (Si_Sucrase-isomaltase,_intestinal_Rattus_norvegicus) : source small intestine, products are glucose and fructose
Housekeeping genes that weren't highly expressed
Important to note than many housekeeping genes for intestines were not extremely highly expressed at all in our samples, not as great evidence as was found for the liver samples
GATA6 (Regulates proximal-distal identity in the intestines) 1) 32.2 2) 33.96 3) 19.17 4) 37.87 5) 31.67 6) 31.98
MYBL2 (Regulates commitment of colon stem cells to differentiate) 1) 0.04 2) 0 3) 0.05 4) 0 5) 0 6) 0
ASCT2 (SLC1A5 expressed in the kidney but expression is high in the jejunum and colon but lower in duodenum and ileum) 1) 2.29 2) 2.96 3) 2.65 4) 1.39 5) 3.75 6) 3.07
STX2, COL3A1, GPBAR1: all from Castoe et al paper; examples of genes that have experienced positive selection (P < 0.001) on snake lineages and are related to prominent phenotypic or cellular traits of snakes- HOWEVER NOT HIGHLY EXPRESSED IN ANY OF OUR SNAKES (range of FPKM from 0 - 4)

2/23/16

2016-02-23T18:50:03Z

Ellankiewicz:

2/23/16

2016-02-23T18:44:00Z

Ellankiewicz: Created page with "Class Notes FPKM-->should be entering .expected_count Dr. Heyer finished code to get supervised clustering, larger data set is not really graphing, Dr. Heyer set up a way to ..."

Elise

2016-02-23T18:38:29Z

Ellankiewicz:

2/11/16

2016-02-11T19:53:11Z

Ellankiewicz:

Notes 2/11/16
Investigating DAVID

Similar programs: GoMiner4, GOstat5, Onto-express6, GoToolBox7, FatiGO8, GFIN- Der9, GOBar10 and GSEA11

DAVID is a dead end-we would have needed accession numbers or a usable form of ID for each of the genes identified in our data-just not possible, looks as though it's designed for model organism, basically just humans

2/11/16

2016-02-11T19:52:28Z

Ellankiewicz: Created page with "Notes 2/11/16 Investigating DAVID Similar programs: GoMiner4, GOstat5, Onto-express6, GoToolBox7, FatiGO8, GFIN- Der9, GOBar10 and GSEA11 DAVID is a dead end-we would have ne..."

Notes 2/11/16
Investigating DAVID
Similar programs: GoMiner4, GOstat5, Onto-express6, GoToolBox7, FatiGO8, GFIN- Der9, GOBar10 and GSEA11
DAVID is a dead end-we would have needed accession numbers or a usable form of ID for each of the genes identified in our data-just not possible, looks as though it's designed for model organism, basically just humans

Elise

2016-02-11T18:37:10Z

Ellankiewicz:

2/09/16

2016-02-09T19:53:52Z

Ellankiewicz: Created page with "Class Notes 2/09/16 Clustering-Grouping genes and samples based on similarity. If we don't like the way it's being grouped there's a lot different algorithms we can use. Eas..."

Class Notes 2/09/16

Clustering-Grouping genes and samples based on similarity. If we don't like the way it's being grouped there's a lot different algorithms we can use.
Easy way to pull out patterns and make predictions from big data sets.
Microarrays is similar technology used for RNA analysis
Induction looks much more dramatic, log scales helps see patterns more clearly
Need to watch out for negative correlations, they may be just as interesting as positive correlations but harder to detect
But we're looking at counts, not ratios, so we shouldn't ever be looking at negative values if using log scale
How to compare one thing to a group of genes? Linkage methods . . .
Create a value for a group and then treat that cluster as an individual gene, or average all the distances, more relaxed, you'd let it in if close to one member of the cluster, more stringent if you say it has to be this close to all the genes

Hierarchical Clustering
Joins two most similar genes, then next two most similar objects, repeat until all have been joined
No gene can be left out, starts at +1 correlation, end at -1
Cutting the tree-group together all the things that are still joined when line is drawn down

K-means clustering
Specify how many clusters to form, randomly assign each gene to one of k different clusters, average expression of all genes in each cluster to create k pseudo genes, rearrange genes by assigning each one to the cluster represented by the pseudo gene to which it is most similar, repeat until convergence

Supervised Clustering
find genes in expression file whose patterns are highly smilier to desired gene or pattern
Add closest gene first, then add gene that is closest to genes already in the cluster, repeat as long as added gene is within specified distance of genes already in cluster, distance from one gene to a set of genes defined to be maximum 9or minimum, or acreage) of all distances to individual members of the set (complete, single, and average linkage, respectively)

QT Clust is available in R, each gene builds supervised cluster, gene with "best" list, and genes in its list becomes next cluster, remove these genes from consideration, and repeat, stop when all genes are clustered
we determine what the rule is that makes the "best" cluster (could just be group with most genes in it)

Elise

2016-02-09T18:41:38Z

Ellankiewicz:

2/04/16

2016-02-04T19:35:40Z

Ellankiewicz:

Notes 2/04/16

What do we want out of our research? What is the perfect outcome?

We want to find the genes that are differentially expressed between unfed and fed snakes and figure out their role in organ growth. The perfect outcome would be to find the gene that starts the cascade.

How do we get there?

First, we need to actually look at the snakes to decide if it’s reasonable to group fed and unfed because form preliminary data that may not make sense. If we can’t group in this manner, we need to find a new way or grouping similar data. We need to figure out how to run a program to see a list of the genes that are differentially expressed. We also need to figure out a way to prioritize (threshold) the differentially expressed genes that will focus further on. From here, we need to research these genes further to attempt to place them within their physiological function or pathway.

What are we going to do with each of our 12 data sets? To evaluate it in order to know how to treat it downstream

First, we only care about our six intestine data sets. Focusing in on our intestine genes, we’d first like to try to find a gene unique to the intestine in order to verify our tissue sample. Using our data that includes each snake, gene, and number of normalized transcripts we would do the following:
• Average (maybe, have to look at grouping) across unfed and fed expression, compare those averages and set a percent difference to determine significant differential gene expression
• A good picture for our presentation would be a heat map of just these genes
• What to do with protein of unknown function?? Maybe first run through discard and see if we can get significant results with known genes
• Grouping: hopefully we may be able to discount something based on findings from our intestine housekeeping gene investigation

Notes from discussion:
How will we define a biological function that fits the definition of starting the cascade? Genes involved in amino acid uptake, could be transporter, (this isn’t really what we think of when thinking of cascades) maybe a g-protein that later on activates growth protein (we should potentially see transcript production here unless its already there) !A transcription factor (a transcript for a transcription factor would be ideal)! Go back and try to find source of proteins!! Cell division or loosely fitting in category of hypertrophy would also be an ok thing to look for

Under-expression might be relevant (we should cover this if we have a percent difference threshold)
Serosa v. mucosa, find housekeeping gene specific not only to small intestine but to a particular layer of the small intestine (sugar/amino acid uptake should be found in mucosa)

Potentially use dendograms to figure out threshold on heatmaps we’ve done so far

Separate out statistically what is interesting v. biologically

Second route: pull out all genes that are transcription factors then see if there is any difference between fed and unfed within this smaller grouping

2/04/16

2016-02-04T18:46:30Z

Ellankiewicz: Created page with "Notes 2/04/16"

Notes 2/04/16

Elise

2016-02-04T18:45:39Z

Ellankiewicz:

1/04/16

2016-02-04T18:45:01Z

Ellankiewicz: Created page with "Notes 2/04/16"

Notes 2/04/16

Elise

2016-02-04T18:44:40Z

Ellankiewicz:

Elise

2016-01-14T19:55:00Z

Ellankiewicz:

Elise

2016-01-12T19:52:33Z

Ellankiewicz:

Elise

2016-01-12T19:42:11Z

Ellankiewicz:

THIS IS ELISE.

.1g of organ used, we are not sure that it's the right part- could be connective tissue etc.
Anything in cDNA, by definition is transcribed mRNA, more stable as DNA

Elise

2016-01-12T19:40:37Z

Ellankiewicz:

THIS IS ELISE.

.1g of organ used, we are not sure that it's the right part?

Elise

2016-01-12T19:20:41Z

Ellankiewicz: Created page with "THIS IS ELISE."

THIS IS ELISE.