GcatWiki - User contributions [en]

Notes 3/17/16

2016-03-17T18:24:17Z

Kathryn

2016-03-17T17:48:24Z

Ktsmith:

THIS IS KATHRYN'S PAGE

[[Notes 3/17/16]]

[[Notes 3/8/16]]

[[Notes 2/25/16]]

[[Notes 2/23/16]]

[[Notes 2/18/16]]

[[Notes 2/16/16]]

[[Notes 2/11/16]]

[[Notes 2/9/16]]

'''Notes 2/4/16'''
What are do we want from our research?

How do we get there?

What are we going to do with each of our 12 data sets? What do we need to do to evaluate these?

----

'''Notes 1/28/16 - 2/2/16'''
What are we doing?

Need to match each script to a gene and figure out how many times that is expressed.

How do we normalize that gene expression given that we don't get the same number of reads from each sample?

1) use DESeq which is a program that is able to normalize for the length of the gene per million reads so that we are able to compare across samples, good to normalize for the length as well as the #,

this is what we did using R, were able to compare across organs and then individually for intestine with fed vs unfed

2)benchmark to a housekeeping gene?

How do we know we have the right tissue?

1)look at the over represented sequences and blast this to see if we get a match?

- Did this and mostly was rRNA which means that cleaning up didn't go as well as we thought it did

'''Notes 1/12/16'''

Only .1 g of organ taken: possibility that connective tissue was taken, not representative of entire organ possibly
total RNA > mRNA using beads that attach to polyA tails of mRNA
- randomly fragment mRNA (since you can only read from an end to 75 base pairs) now get a lot more accurate reads, now know more about entire sequence
mRNA > CDNA: using reverse transcriptase, dNTP, use primers that has every possibly combination of 6 nucleotides so all mRNA is transcribed
CDNA: has been transcribed as mRNA and then changed into DNA to make more stable form

----

'''Research'''

BoAT1:

The molecular correlate of system B0, the major apical neutral amino acid transporter in kidney and intestine, is B0AT1 (SLC6A19) (46), a protein of 634 amino acids. Currently, no splice variants of the transporter have been reported. The human SLC6A19 shows very little activity in heterologous expression systems; hence, the mouse transporter has been characterized in more detail. In agreement with functional studies, B0AT1 transports all neutral amino acids, albeit to a varying extent. Vmax values appear to be fairly similar, but affinities are different for each amino acid. The order of preference is Met = Leu = Ile = Val > Gln = Asn = Phe = Cys = Ala > Ser = Gly = Tyr = Thr = His = Pro > Trp > Lys. This order is in partial agreement with studies in the intestine (280). The transporter shows some affinity for lysine;
In situ hybridization and immunocytochemical analysis showed that the transporter is expressed in the kidney proximal convoluted tubule (46, 297) and in all parts of the small intestine but not in the colon (Fig. 1). Expression increases from the duodenum to the ileum (297, 373). The transporter is confined to the apical membrane. The signal was more intense towards the tip of the villi
it has been reported that expression of the B0AT1 protein in the brush-border membrane requires coexpression of collectrin '''Find Collection gene?'''

Collectrin-deficient mice had low levels of B0AT1 and other members of the SLC6 family in the brush-border membrane. Transcript levels, in contrast, were unaltered, '''suggesting a posttranscriptional mechanism'''

Three different Na+-dependent components were identified, namely, a system A-like activity, a system ASC-like activity, and a novel activity. All Na+-dependent transporters have affinities in the micromolar range (356). They most likely serve to recruit nutritional amino acids when the intestine is inactive or starved. System ASC activity was also detected in the brush-border membrane in both kidney and intestine
In summary, there is strong evidence for the presence of system B0 in the intestine; the role of system ASC in amino acid absorption remains to be determined

ASCT2:

The ASCT2 transporter is Na+ dependent but not electrogenic. This apparent discrepancy is explained by the mechanism of ASCT2, which involves an obligatory exchange of substrate amino acids against each other and a nonproductive Na+/Na+ exchange (48). It appears that there is no fixed ratio between the number of Na+ exchanged and the number of amino acids exchanged (176). Because of its antiport mechanism, ASCT2 cannot contribute to net transport of neutral amino acids across the apical membrane. ASCT2 transports small neutral amino acids with Km values of ∼20 μM; glycine, leucine and methionine are transported with Km values of 300–500 μM (387). Immunohistochemical analysis and reconstitution experiments suggest its presence in the apical membrane in the kidney and intestine (13, 251). In the kidney, expression is confined to the proximal tubule; in the intestine, expression is high in the jejunum and colon but lower in duodenum and ileum

Other Possible Genes:

Cationic amino acids : rBAT 4F2/LAT2, 4F2/y+LAT1

Anionic amino acids: ASCT2, EAAT3, EAAT2

Proline and Glycine: PAT1 (rat), IMINO (rabbit) BOAT1

Beta-amino acids: TauT, PAT1

- http://physrev.physiology.org/content/88/1/249#sec-45

Notes 3/8/16

2016-03-08T19:55:44Z

Ktsmith:

Dr. Campbell gave us a new R code that was essentially the same but included normalized aspect that was supposed to help with clustering. When I included qval < .05 showed correct clustering but list of genes was very long.
[[File:Normalized z scale qvals , .05.png ]]

Decided to do qval < .01 like I had before. found clustering was not exact but looked pretty good using z score (KEPT THESE 40 GENES TO LOOK AT)
[[File:Normalized_z_scal_qvals_-_.01.png‎]]

Doing the same thing but using number scale saw that there wasn't huge differences in the expression of the genes in the two groups.
[[File:Normalized_num_scale_qvals_-_.01.png‎]]

== MOVING FORWARD ==

Moving forward decided to use the list of 40 genes. Decided that we would cross reference these 40 genes with the genes found in Castoe at 6 hours
* genes on both would show differential expression early on and throughout the digestion process. List from Castoe is all unregulated so would need to see if ours were also unregulated or down regulated in the differential expression
* genes on only his list would not be interesting
* genes found only on our list might possibly be interesting because they could be involved in initiation and be back to regular levels by 6 hours

After comparing will look first at the genes on both lists to see their function and if they might be interesting, then will look at functions on the genes on our lists.

Later will run supervised clustering with these genes of interest.

Notes 3/8/16

2016-03-08T19:54:52Z

Ktsmith:

Dr. Campbell gave us a new R code that was essentially the same but included normalized aspect that was supposed to help with clustering. When I included qval < .05 showed correct clustering but list of genes was very long.
[[File:Normalized z scale qvals , .05.png ]]

Decided to do qval < .01 like I had before. found clustering was not exact but looked pretty good using z score (KEPT THESE 40 GENES TO LOOK AT)
[[File:Normalized_z_scal_qvals_-_.01.png‎]]

Doing the same thing but using number scale saw that there wasn't huge differences in the expression of the genes in the two groups.
[[File:Normalized_num_scale_qvals_-_.01.png‎]]

Moving forward decided to use the list of 40 genes. Decided that we would cross reference these 40 genes with the genes found in Castoe at 6 hours
* genes on both would show differential expression early on and throughout the digestion process. List from Castoe is all unregulated so would need to see if ours were also unregulated or down regulated in the differential expression
* genes on only his list would not be interesting
* genes found only on our list might possibly be interesting because they could be involved in initiation and be back to regular levels by 6 hours

After comparing will look first at the genes on both lists to see their function and if they might be interesting, then will look at functions on the genes on our lists.

Later will run supervised clustering with these genes of interest.

File:Normalized num scale qvals - .01.png

2016-03-08T19:54:26Z

Ktsmith:

Notes 3/8/16

2016-03-08T19:53:57Z

Ktsmith:

Dr. Campbell gave us a new R code that was essentially the same but included normalized aspect that was supposed to help with clustering. When I included qval < .05 showed correct clustering but list of genes was very long.
[[File:Normalized z scale qvals , .05.png ]]

Decided to do qval < .01 like I had before. found clustering was not exact but looked pretty good using z score (KEPT THESE 40 GENES TO LOOK AT)
[[File:Normalized_z_scal_qvals_-_.01.png‎]]

Doing the same thing but using number scale saw that there wasn't huge differences in the expression of the genes in the two groups.

Moving forward decided to use the list of 40 genes. Decided that we would cross reference these 40 genes with the genes found in Castoe at 6 hours
* genes on both would show differential expression early on and throughout the digestion process. List from Castoe is all unregulated so would need to see if ours were also unregulated or down regulated in the differential expression
* genes on only his list would not be interesting
* genes found only on our list might possibly be interesting because they could be involved in initiation and be back to regular levels by 6 hours

After comparing will look first at the genes on both lists to see their function and if they might be interesting, then will look at functions on the genes on our lists.

Later will run supervised clustering with these genes of interest.

Ktsmith:

Last class we realized that the fed and not fed were not clustering correctly

Could possibly get them to cluster by filtering out extremely high values or change the correlation for FPKM. However, Dr. Heyer pointed out that we already found genes that had differential expression among the two groups so clustering doesn't matter too much. Will still be important to try to fix however for the pictures that we will use.

Today we will continue to look for genes. Look mainly at genes that are transcription factors or kinases, things that could be in charge of amplification of the cycle to cause cell growth (List of these genes on google doc)

Because Dr. Heyer said that the genes were already separated among the two groups I decided to write deOut to a file to see the data with the hopes that i would be able to clearly see that fed and not fed were different from each other, but was not able to understand exactly what it was saying. Saw some differential expression but the numbers were pretty close to each other, assume the the program is working like it is supposed to.

Dr. Campbell told us to remove mean expression values that were greater than 8000. This allows us to correlate genes better. However still not able to cluster fed and not fed with each other (example below using Contig8459_SVS1_Protein_SVS1_Saccharomyces_cerevisiae_strain_ATCC_204508_/_S288c gene

[[File:Not_correct_clustering.png‎]]

'''New Direction'''

Decided to use correlation clustering to find a list of genes and try to get the clusters to be correct. Decided to get rid of values of 5000 then used correlation clustering. Saved the list of genes that came from correlation into a file. will use these later to do supervised clustering. (Heat map using correlation clustering below)

[[File:Screen_Shot_2016-02-25_at_2.53.50_PM.png]]

File:Screen Shot 2016-02-25 at 2.53.50 PM.png

2016-02-25T19:56:24Z

Ktsmith:

Notes 2/25/16

2016-02-25T19:56:06Z

2016-02-25T18:48:22Z

Ktsmith:

THIS IS KATHRYN'S PAGE

[[Notes 2/25/16]]

[[Notes 2/23/16]]

[[Notes 2/18/16]]

[[Notes 2/16/16]]

[[Notes 2/11/16]]

[[Notes 2/9/16]]

'''Notes 2/4/16'''
What are do we want from our research?

How do we get there?

What are we going to do with each of our 12 data sets? What do we need to do to evaluate these?

----

'''Notes 1/28/16 - 2/2/16'''
What are we doing?

Need to match each script to a gene and figure out how many times that is expressed.

How do we normalize that gene expression given that we don't get the same number of reads from each sample?

1) use DESeq which is a program that is able to normalize for the length of the gene per million reads so that we are able to compare across samples, good to normalize for the length as well as the #,

this is what we did using R, were able to compare across organs and then individually for intestine with fed vs unfed

2)benchmark to a housekeeping gene?

How do we know we have the right tissue?

1)look at the over represented sequences and blast this to see if we get a match?

- Did this and mostly was rRNA which means that cleaning up didn't go as well as we thought it did

'''Notes 1/12/16'''

Only .1 g of organ taken: possibility that connective tissue was taken, not representative of entire organ possibly
total RNA > mRNA using beads that attach to polyA tails of mRNA
- randomly fragment mRNA (since you can only read from an end to 75 base pairs) now get a lot more accurate reads, now know more about entire sequence
mRNA > CDNA: using reverse transcriptase, dNTP, use primers that has every possibly combination of 6 nucleotides so all mRNA is transcribed
CDNA: has been transcribed as mRNA and then changed into DNA to make more stable form

----

'''Research'''

BoAT1:

The molecular correlate of system B0, the major apical neutral amino acid transporter in kidney and intestine, is B0AT1 (SLC6A19) (46), a protein of 634 amino acids. Currently, no splice variants of the transporter have been reported. The human SLC6A19 shows very little activity in heterologous expression systems; hence, the mouse transporter has been characterized in more detail. In agreement with functional studies, B0AT1 transports all neutral amino acids, albeit to a varying extent. Vmax values appear to be fairly similar, but affinities are different for each amino acid. The order of preference is Met = Leu = Ile = Val > Gln = Asn = Phe = Cys = Ala > Ser = Gly = Tyr = Thr = His = Pro > Trp > Lys. This order is in partial agreement with studies in the intestine (280). The transporter shows some affinity for lysine;
In situ hybridization and immunocytochemical analysis showed that the transporter is expressed in the kidney proximal convoluted tubule (46, 297) and in all parts of the small intestine but not in the colon (Fig. 1). Expression increases from the duodenum to the ileum (297, 373). The transporter is confined to the apical membrane. The signal was more intense towards the tip of the villi
it has been reported that expression of the B0AT1 protein in the brush-border membrane requires coexpression of collectrin '''Find Collection gene?'''

Collectrin-deficient mice had low levels of B0AT1 and other members of the SLC6 family in the brush-border membrane. Transcript levels, in contrast, were unaltered, '''suggesting a posttranscriptional mechanism'''

Three different Na+-dependent components were identified, namely, a system A-like activity, a system ASC-like activity, and a novel activity. All Na+-dependent transporters have affinities in the micromolar range (356). They most likely serve to recruit nutritional amino acids when the intestine is inactive or starved. System ASC activity was also detected in the brush-border membrane in both kidney and intestine
In summary, there is strong evidence for the presence of system B0 in the intestine; the role of system ASC in amino acid absorption remains to be determined

ASCT2:

The ASCT2 transporter is Na+ dependent but not electrogenic. This apparent discrepancy is explained by the mechanism of ASCT2, which involves an obligatory exchange of substrate amino acids against each other and a nonproductive Na+/Na+ exchange (48). It appears that there is no fixed ratio between the number of Na+ exchanged and the number of amino acids exchanged (176). Because of its antiport mechanism, ASCT2 cannot contribute to net transport of neutral amino acids across the apical membrane. ASCT2 transports small neutral amino acids with Km values of ∼20 μM; glycine, leucine and methionine are transported with Km values of 300–500 μM (387). Immunohistochemical analysis and reconstitution experiments suggest its presence in the apical membrane in the kidney and intestine (13, 251). In the kidney, expression is confined to the proximal tubule; in the intestine, expression is high in the jejunum and colon but lower in duodenum and ileum

Other Possible Genes:

Cationic amino acids : rBAT 4F2/LAT2, 4F2/y+LAT1

Anionic amino acids: ASCT2, EAAT3, EAAT2

Proline and Glycine: PAT1 (rat), IMINO (rabbit) BOAT1

Beta-amino acids: TauT, PAT1

- http://physrev.physiology.org/content/88/1/249#sec-45

Notes 2/23/16

2016-02-23T19:21:04Z

Ktsmith:

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

work on ways to get program to work, looking at different genes of interest and clustering genes that are similar to those

'''Genes of Possible Interest'''
*Contig1_DNPEP_Aspartyl_aminopeptidase_Homo_sapiens
** using this as a gene of interest created a cluster of 4550, showed increased activity in fed with somewhat decreased in nonfed
** from this found next gene of interest that had ~2500 expression in fed ~1500 in non-fed
*Contig2969_KRT18_Keratin,_type_I_cytoskeletal_18_Homo_sapiens

Notes 2/23/16

2016-02-23T19:17:50Z

Ktsmith:

2016-02-11T18:50:01Z

Ktsmith:

THIS IS KATHRYN'S PAGE

[[Notes 2/11/16]]

[[Notes 2/9/16]]

'''Notes 2/4/16'''
What are do we want from our research?

How do we get there?

What are we going to do with each of our 12 data sets? What do we need to do to evaluate these?

----

'''Notes 1/28/16 - 2/2/16'''
What are we doing?

Need to match each script to a gene and figure out how many times that is expressed.

How do we normalize that gene expression given that we don't get the same number of reads from each sample?

1) use DESeq which is a program that is able to normalize for the length of the gene per million reads so that we are able to compare across samples, good to normalize for the length as well as the #,

this is what we did using R, were able to compare across organs and then individually for intestine with fed vs unfed

2)benchmark to a housekeeping gene?

How do we know we have the right tissue?

1)look at the over represented sequences and blast this to see if we get a match?

- Did this and mostly was rRNA which means that cleaning up didn't go as well as we thought it did

'''Notes 1/12/16'''

Only .1 g of organ taken: possibility that connective tissue was taken, not representative of entire organ possibly
total RNA > mRNA using beads that attach to polyA tails of mRNA
- randomly fragment mRNA (since you can only read from an end to 75 base pairs) now get a lot more accurate reads, now know more about entire sequence
mRNA > CDNA: using reverse transcriptase, dNTP, use primers that has every possibly combination of 6 nucleotides so all mRNA is transcribed
CDNA: has been transcribed as mRNA and then changed into DNA to make more stable form

----

'''Research'''

BoAT1:

The molecular correlate of system B0, the major apical neutral amino acid transporter in kidney and intestine, is B0AT1 (SLC6A19) (46), a protein of 634 amino acids. Currently, no splice variants of the transporter have been reported. The human SLC6A19 shows very little activity in heterologous expression systems; hence, the mouse transporter has been characterized in more detail. In agreement with functional studies, B0AT1 transports all neutral amino acids, albeit to a varying extent. Vmax values appear to be fairly similar, but affinities are different for each amino acid. The order of preference is Met = Leu = Ile = Val > Gln = Asn = Phe = Cys = Ala > Ser = Gly = Tyr = Thr = His = Pro > Trp > Lys. This order is in partial agreement with studies in the intestine (280). The transporter shows some affinity for lysine;
In situ hybridization and immunocytochemical analysis showed that the transporter is expressed in the kidney proximal convoluted tubule (46, 297) and in all parts of the small intestine but not in the colon (Fig. 1). Expression increases from the duodenum to the ileum (297, 373). The transporter is confined to the apical membrane. The signal was more intense towards the tip of the villi
it has been reported that expression of the B0AT1 protein in the brush-border membrane requires coexpression of collectrin '''Find Collection gene?'''

Collectrin-deficient mice had low levels of B0AT1 and other members of the SLC6 family in the brush-border membrane. Transcript levels, in contrast, were unaltered, '''suggesting a posttranscriptional mechanism'''

Three different Na+-dependent components were identified, namely, a system A-like activity, a system ASC-like activity, and a novel activity. All Na+-dependent transporters have affinities in the micromolar range (356). They most likely serve to recruit nutritional amino acids when the intestine is inactive or starved. System ASC activity was also detected in the brush-border membrane in both kidney and intestine
In summary, there is strong evidence for the presence of system B0 in the intestine; the role of system ASC in amino acid absorption remains to be determined

ASCT2:

The ASCT2 transporter is Na+ dependent but not electrogenic. This apparent discrepancy is explained by the mechanism of ASCT2, which involves an obligatory exchange of substrate amino acids against each other and a nonproductive Na+/Na+ exchange (48). It appears that there is no fixed ratio between the number of Na+ exchanged and the number of amino acids exchanged (176). Because of its antiport mechanism, ASCT2 cannot contribute to net transport of neutral amino acids across the apical membrane. ASCT2 transports small neutral amino acids with Km values of ∼20 μM; glycine, leucine and methionine are transported with Km values of 300–500 μM (387). Immunohistochemical analysis and reconstitution experiments suggest its presence in the apical membrane in the kidney and intestine (13, 251). In the kidney, expression is confined to the proximal tubule; in the intestine, expression is high in the jejunum and colon but lower in duodenum and ileum

Other Possible Genes:

Cationic amino acids : rBAT 4F2/LAT2, 4F2/y+LAT1

Anionic amino acids: ASCT2, EAAT3, EAAT2

Proline and Glycine: PAT1 (rat), IMINO (rabbit) BOAT1

Beta-amino acids: TauT, PAT1

- http://physrev.physiology.org/content/88/1/249#sec-45