GcatWiki - User contributions [en]

File:Scripts.txt

2016-05-05T00:19:45Z

Duatchley:

Important Files

2016-05-05T00:19:38Z

Duatchley:

[[File: Approach1.xlsx]]

[[File: Scripts.txt]]

Important Files

2016-05-05T00:19:07Z

Duatchley:

[[File: Approach1.xlsx]]

[[File: Scripts]]

Important Files

2016-05-05T00:19:02Z

Duatchley:

[[File: Approach1.xlsx]]

[[File: Scripts]]

Important Files

2016-05-05T00:18:54Z

Duatchley:

[[File: Approach1.xlsx]]
[[File: Scripts]]

Important Files

2016-05-04T14:26:06Z

Duatchley:

[[File: Approach1.xlsx]]

File:Approach1.xlsx

2016-05-04T14:24:15Z

Duatchley:

Important Files

2016-05-04T14:24:06Z

Duatchley: Created page with "File:approach1.xlsx"

[[File:approach1.xlsx]]

Group 1 intestines

2016-05-04T14:23:28Z

Duatchley:

Hello

[[Important Files]]

[[Dylan Maghini]]

[[Nick Balanda]]

[[Dustin Atchley]]

[[Housekeeping genes for intestines]]

To do list:
*Approach I
*Approach II- tweak
*Unknown proteins -- not gonna happen
*Other GAFs -- also probably not gonna happen
*missed homo sapiens

Current status:
*Cytoscape representation of Gene Ontology, can filter to find groups of GO terms, see how terms are related, etc. Filter based off of scores that we've given them.
*Approach 1: Have looked at most up-regulated and down-regulated genes, and their functions.

Analysis Modules:
[http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/3%20Analysis%20Modules/]

March 24

2016-03-24T18:56:07Z

Duatchley: Created page with "Abandoned the computational matching of all the GO terms in python Started taking the sequences of the most down regulated genes in the genewithzeros.xlsx file Found gene,..."

Abandoned the computational matching of all the GO terms in python

Started taking the sequences of the most down regulated genes in the genewithzeros.xlsx file

Found gene, got protein from sequence, blasted protein sequence and started searching. Two most down regulated genes appear to be related to G protein.

Dustin Atchley

2016-03-24T18:52:53Z

Duatchley:

http://www.example.com link titleHello world!

Username: davidson18

Psswd: http://genius.com/Tyga-good-day-lyrics

----

----

Most common mistake "I will remember how I got here". Take good NOTES!

Harvest .1 g of tissue, 100 micrograms of RNA from RNA isolation kit.
Don't know we didn't sample the wrong part of the organ since the amount is so small.
Be your hardest critic.

Isolated PolyA RNA (Ideally you get 2% of the RNA, protein coding)
Fragment the RNA before synthesizing.
DNA polymerase (reverse transcriptase) goes into mix, made cDNA.
Primers for amplification, you make a hexamer, which is every possible combination so that the complementary bases will bind.
Sequence cDNA that is the template to the RNA you are using. It is more stable.

Don't want full length DNAs, because we want about 75 bases for short reads.
Randomly fragmented which is wonderful.

Gel purified and reamplified (picture in PP)

We are sampling a wadded up tissue looking for proximal and distal end. Could have gotten serosa, need to be aware that we had to leave it frozen in order to not lose the RNA we needed.

Looking for genes involved in the up regulation of the amino acids that were measured in Figure 3 from 1a_pay_B4_pumpping_1995.

Housekeeping genes for liver and intestines (always on genes so we know we got the right cell)
Cell proliferation or cells growing (brush border growth, find these proteins)
Uptake genes

The trim fastQC files trimmed the frist 4 bases that which were the tags.

The Trimmomatic also removed bases with a quality score of less than 30 coming from the 3' end, only removed contiguous bases, and if the length of the read became 30 or less, the read itself was thrown out.

HTML FILES:

Untrimmed:

[[Media:split_1no_i_fastqc.html]]

split_2no_i_fastqc.html

split_3no_i_fastqc.html

split_4fed_i_fastqc.html

split_5fed_i_fastqc.html

split_6fed_i_fastqc.html

Trimmed:

trim_1i_fastqc.html

trim_2i_fastqc.html

trim_3i_fastqc.html

trim_4i_fastqc.html

trim_5i_fastqc.html

trim_6i_fastqc.html

(Blast people found):

Intestines RNA and uptake regulator (80-90% is ribosomal and mitochondrial RNA)

(Online resource people):

Figured out how to download everything, but not the different cluster. There is a sequence searcher for all the genes that they had. No magic list of genes that were differentially regulated.
Found a list in the supplementary materials in one of the papers.

(Quantification normalization)
Library size and read depth varied. Total read count normalization is the typical route. DESeq is the other standard, which we are using today.
Looking for a single number that considers the length and overrepresentation of the number based on that. Normalized for the length of the genes per million reads (Gene_results)

[[Feb_4]]

[[Feb_9]]

[[Feb_11]]

[[Feb_18]]

Normalization in DESeq compares same gene against itself. FPKM normalized number of reads among genes.

[[March 24]]

Dustin Atchley

2016-03-22T17:44:24Z

Duatchley:

Feb 18

2016-02-18T18:48:25Z

Duatchley: Created page with "Dr. Heyer changed the clustering algorithm in heat map and normalized they changed from euclidean distance to correlation. Also writes all gene names to a file Now given a g..."

Dr. Heyer changed the clustering algorithm in heat map and normalized they changed from euclidean distance to correlation.

Also writes all gene names to a file

Now given a gene, we want all the genes that cluster with it at a given p value

Transcription genes aren't highly transcribed, so won't produce it visibly on the heat map necessarily, but need to know whether it is on or off.

Dustin Atchley

2016-02-18T18:43:51Z

Duatchley:

Feb 11

2016-02-11T18:51:56Z

Duatchley: Created page with "We are going to the GO website and see if we can download the GO terms and connect them to our genes. GO terms for all our genes and then upload these. (Practice with a subset..."

We are going to the GO website and see if we can download the GO terms and connect them to our genes. GO terms for all our genes and then upload these. (Practice with a subset of genes).

Dustin Atchley

2016-02-11T18:50:48Z

Duatchley:

Feb 9

2016-02-09T19:54:35Z

Duatchley:

Clustering: Grouping in a particular way based on some sort of algorithm with given parameters

Why cluster? Exploration of huge data, extract patterns and make predictions on these patterns (hypothesis generation and testing)

'''Gene expression data:'''

Induction looks much more dramatic than the repression (be sure and remember this), equivalent on the fold change, but look very dissimilar

A log transformation "normalizing" the way this data looks for fold changes

Negative correlations are as informative as the positive correlations

Scatter/line plots are a different way to represent a heat map

'''Comparing Gene Expression Profiles or Guilt by expression:'''

Co-regulation or directly regulating each other

'''Proximity Measures:'''

Want to understand relationships genes and expression level over time or samples

Correlation, Euclidean distance (distance formula), Inner product x y, Hamming distance, L1 distance, Dissimilarities may or may not be metrics

Correlation is very sensitive to outliners (percent change) so the other measures could be good

'''Linkage Methods:'''

Find some center point in a cluster, treat it as a "gene" and measure it from the gene of interest

Could average all the distances between the gene of interest and all in cluster

Could do the minimum or the maximum distance of a gene in the cluster to the gene of interest

Single linkage, Average Linkage, etc. Each will produce different clusters

'''Hierarchical Clustering'''

Join two most similar genes

Join next two most similar "objects", repeat until all genes have been joined (can never be pulled apart in your cluster once they are joined)

Iterative and stringent

Everybody is included, nobody is left out (starts at positive one correlation and ends at negative one correlation

Cutting the tree: group the things that are still joined at a certain point

'''K-means Clustering'''

Specify how many clusters to form

Randomly assign each gene to one of the k different groups

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

With our data maybe cluster 2 groups based on fed and non fed (then did the data support that)

Really hard to pick clusters

'''Supervised Clustering'''

Find genes in expression file whose patterns are highly similar (close) to desired gene or pattern

Add closest gene first

Then add gene that is closest to all genes already in 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 max or min or avg of all distances to individual members of the set

'''QT Clustering'''

1. Each gene builds a supervised cluster
2. Gene with "best" list, and genes in its list, becomes next cluster (2 rules, how many groups are you in and which group do you choose)
3. Remove these genes from consideration and repeat
4. Stop when all genes are clustered, or largest cluster is smaller than user specified threshold

Will likely stop at supervised clustering (around gene ontology perhaps) in this class, restraint is good, don't just go to a heat map

Completed clustering activities

Feb 9

2016-02-09T19:35:49Z

Duatchley:

Feb 9

2016-02-09T19:35:22Z

Duatchley:

Clustering: Grouping in a particular way based on some sort of algorithm with given parameters

Why cluster? Exploration of huge data, extract patterns and make predictions on these patterns (hypothesis generation and testing)

'''Gene expression data:'''

Induction looks much more dramatic than the repression (be sure and remember this), equivalent on the fold change, but look very dissimilar

A log transformation "normalizing" the way this data looks for fold changes

Negative correlations are as informative as the positive correlations

Scatter/line plots are a different way to represent a heat map

'''Comparing Gene Expression Profiles or Guilt by expression:'''

Co-regulation or directly regulating each other

'''Proximity Measures:'''

Want to understand relationships genes and expression level over time or samples

Correlation, Euclidean distance (distance formula), Inner product x y, Hamming distance, L1 distance, Dissimilarities may or may not be metrics

Correlation is very sensitive to outliners (percent change) so the other measures could be good

'''Linkage Methods:'''

Find some center point in a cluster, treat it as a "gene" and measure it from the gene of interest

Could average all the distances between the gene of interest and all in cluster

Could do the minimum or the maximum distance of a gene in the cluster to the gene of interest

Single linkage, Average Linkage, etc. Each will produce different clusters

'''Hierarchical Clustering'''

Join two most similar genes

Join next two most similar "objects", repeat until all genes have been joined (can never be pulled apart in your cluster once they are joined)

Iterative and stringent

Everybody is included, nobody is left out (starts at positive one correlation and ends at negative one correlation

Cutting the tree: group the things that are still joined at a certain point

'''K-means Clustering'''

Specify how many clusters to form

Randomly assign each gene to one of the k different groups

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

With our data maybe cluster 2 groups based on fed and non fed (then did the data support that)

Really hard to pick clusters

'''Supervised Clustering'''

Find genes in expression file whose patterns are highly similar (close) to desired gene or pattern

Add closest gene first

Then add gene that is closest to all genes already in 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 max or min or avg of all distances to individual members of the set

'''QT Clustering'''

1. Each gene builds a supervised cluster
2. Gene with "best" list, and genes in its list, becomes next cluster (2 rules, how many groups are you in and which group do you choose)
3. Remove these genes from consideration and repeat
4. Stop when all genes are clustered, or largest cluster is smaller than user specified threshold

Will likely stop at supervised clustering in this class, restraint is good, don't just go to a heat map

Feb 9

2016-02-09T19:25:53Z

Duatchley:

Clustering: Grouping in a particular way based on some sort of algorithm with given parameters

Why cluster? Exploration of huge data, extract patterns and make predictions on these patterns (hypothesis generation and testing)

'''Gene expression data:'''

Induction looks much more dramatic than the repression (be sure and remember this), equivalent on the fold change, but look very dissimilar

A log transformation "normalizing" the way this data looks for fold changes

Negative correlations are as informative as the positive correlations

Scatter/line plots are a different way to represent a heat map

'''Comparing Gene Expression Profiles or Guilt by expression:'''

Co-regulation or directly regulating each other

'''Proximity Measures:'''

Want to understand relationships genes and expression level over time or samples

Correlation, Euclidean distance (distance formula), Inner product x y, Hamming distance, L1 distance, Dissimilarities may or may not be metrics

Correlation is very sensitive to outliners (percent change) so the other measures could be good

'''Linkage Methods:'''

Find some center point in a cluster, treat it as a "gene" and measure it from the gene of interest

Could average all the distances between the gene of interest and all in cluster

Could do the minimum or the maximum distance of a gene in the cluster to the gene of interest

Single linkage, Average Linkage, etc. Each will produce different clusters

'''Hierarchical Clustering'''

Join two most similar genes

Join next two most similar "objects", repeat until all genes have been joined (can never be pulled apart in your cluster once they are joined)

Iterative and stringent

Everybody is included, nobody is left out (starts at positive one correlation and ends at negative one correlation

Cutting the tree: group the things that are still joined at a certain point

'''K-means clustering'''

Specify how many clusters to form

Randomly assign each gene to one of the k different groups

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

With our data maybe cluster 2 groups based on fed and non fed (then did the data support that)

Really hard to pick clusters

Feb 9

2016-02-09T19:19:56Z

Duatchley:

Feb 9

2016-02-09T19:11:31Z

Duatchley:

Feb 9

2016-02-09T19:11:19Z

Duatchley:

Feb 9

2016-02-09T19:10:57Z

Duatchley: Created page with "Clustering: Grouping in a particular way based on some sort of algorithm with given parameters Why cluster? Exploration of huge data, extract patterns and make predictions on..."

Dustin Atchley

2016-02-09T18:57:14Z

Duatchley:

File:All fed vs all not.png

2016-02-04T19:56:55Z

Duatchley:

Feb 4

2016-02-04T19:56:20Z

Duatchley:

Research has begun in earnest.

R analysis of the RNASeq data

Questions to answer:

1. What do we want out the research. What is the perfect outcome? How do we get there?

Looking for some sort of G protein? Bottom of the G protein cascade: making proteins, but their could be transcription because they go for so long in between uptake periods. What would trigger this cascade? A transcription activator? Is it there or are we looking for that? All proteins that appear in plasma for uptake, where do the proteins originate from (in literature)? Is lipid uptake involved in the volume growth? Typically a cascade is transcription factor that turns on a whole lot, so if we can find that, that's a good lead. Hormone repsonsive transcription factor. (Extracellular and hormone receptors (goes straight through and binds to a protein to make a transcription factor))
Kegg has an RNA splicing pathway to consult.

What if we went functional first, and used GO terms to find the transcription factors first.

2. What are we going to do with each of our 12 data sets to evaluate it and know how to treat it downstream?

Validate the samples. Use the housekeeping genes in the literature (Serosa won't have any regulation around protein uptake).

Need a quality control on all the samples, before we take off on anything else.

Heat map tells is using the genes that helps us distinguish the fed from the non fed. How are they getting on the list, because that could be vital. If something has a zero value does it get thrown out or kept?
[[File:Screen Shot 2016-02-04 at 2.51.29 PM.png]]

[[File:All_fed_vs_all_not.png]]

File:Screen Shot 2016-02-04 at 2.51.29 PM.png

2016-02-04T19:52:38Z

Duatchley:

Feb 4

2016-02-04T19:52:22Z

Duatchley:

Feb 4

2016-02-04T19:35:23Z

Duatchley:

Feb 4

2016-02-04T19:25:52Z

Duatchley:

Research has begun in earnest.

R analysis of the RNASeq data

Questions to answer:

1. What do we want out the research. What is the perfect outcome? How do we get there?

Looking for some sort of G protein? Bottom of the G protein cascade: making proteins, but their could be transcription because they go for so long in between uptake periods. What would trigger this cascade? A transcription activator? Is it there or are we looking for that? All proteins that appear in plasma for uptake, where do the proteins originate from (in literature)? Is lipid uptake involved in the volume growth? Typically a cascade is transcription factor that turns on a whole lot, so if we can find that, that's a good lead. Hormone repsonsive transcription factor. (Extracellular and hormone receptors (goes straight through and binds to a protein to make a transcription factor))
Kegg has an RNA splicing pathway to consult.

2. What are we going to do with each of our 12 data sets to evaluate it and know how to treat it downstream?

Validate the samples. Use the housekeeping genes in the literature (Serosa won't have any regulation around protein uptake).

Need a quality control on all the samples, before we take off on anything else.

Feb 4

2016-02-04T19:24:44Z

Duatchley:

Research has begun in earnest.

R analysis of the RNASeq data

Questions to answer:

1. What do we want out the research. What is the perfect outcome? How do we get there?

Looking for some sort of G protein? Bottom of the G protein cascade: making proteins, but their could be transcription because they go for so long in between uptake periods. What would trigger this cascade? A transcription activator? Is it there or are we looking for that? All proteins that appear in plasma for uptake, where do the proteins originate from (in literature)? Is lipid uptake involved in the volume growth? Typically a cascade is transcription factor that turns on a whole lot, so if we can find that, that's a good lead. Hormone repsonsive transcription factor. (Extracellular and hormone receptors (goes straight through and binds to a protein to make a transcription factor))
Kegg has an RNA splicing pathway to consult.

2. What are we going to do with each of our 12 data sets to evaluate it and know how to treat it downstream?

Validate the samples. Use the housekeeping genes in the literature (Serosa won't have any regulation around protein uptake).

Feb 4

2016-02-04T19:22:11Z

Duatchley:

Feb 4

2016-02-04T19:02:35Z

Duatchley:

Research has begun in earnest.

R analysis of the RNASeq data

Questions to answer:

1. What do we want out the research. What is the perfect outcome? How do we get there?

2. What are we going to do with each of our 12 data sets to evaluate it and know how to treat it downstream?

Feb 4

2016-02-04T18:47:37Z

Duatchley: Created page with "Research has begun in earnest. R analysis of the RNASeq data Questions to answer: 1. What do we want out the research. What is the perfect outcome? How do we get there? 2...."

Dustin Atchley

2016-02-04T18:45:28Z

Duatchley:

Dustin Atchley

2016-02-02T19:56:51Z

Duatchley:

Dustin Atchley

2016-02-02T18:47:42Z

Duatchley:

Dustin Atchley

2016-01-26T19:54:53Z

Duatchley:

Dustin Atchley

2016-01-26T19:53:49Z

Duatchley:

Dustin Atchley

2016-01-26T19:53:37Z

Duatchley:

Dustin Atchley

2016-01-26T19:53:14Z

Duatchley:

Dustin Atchley

2016-01-26T19:52:46Z

Duatchley:

Hello world!

Username: davidson18

Psswd: http://genius.com/Tyga-good-day-lyrics

----

----

Most common mistake "I will remember how I got here". Take good NOTES!

Harvest .1 g of tissue, 100 micrograms of RNA from RNA isolation kit.
Don't know we didn't sample the wrong part of the organ since the amount is so small.
Be your hardest critic.

Isolated PolyA RNA (Ideally you get 2% of the RNA, protein coding)
Fragment the RNA before synthesizing.
DNA polymerase (reverse transcriptase) goes into mix, made cDNA.
Primers for amplification, you make a hexamer, which is every possible combination so that the complementary bases will bind.
Sequence cDNA that is the template to the RNA you are using. It is more stable.

Don't want full length DNAs, because we want about 75 bases for short reads.
Randomly fragmented which is wonderful.

Gel purified and reamplified (picture in PP)

We are sampling a wadded up tissue looking for proximal and distal end. Could have gotten serosa, need to be aware that we had to leave it frozen in order to not lose the RNA we needed.

Looking for genes involved in the up regulation of the amino acids that were measured in Figure 3 from 1a_pay_B4_pumpping_1995.

Housekeeping genes for liver and intestines (always on genes so we know we got the right cell)
Cell proliferation or cells growing (brush border growth, find these proteins)
Uptake genes

The trim fastQC files trimmed the frist 4 bases that which were the tags.

The Trimmomatic also removed bases with a quality score of less than 30 coming from the 3' end, only removed contiguous bases, and if the length of the read became 30 or less, the read itself was thrown out.

HTML FILES:

Untrimmed:

split_1no_i_fastqc.html

split_2no_i_fastqc.html

split_3no_i_fastqc.html

split_4fed_i_fastqc.html

split_5fed_i_fastqc.html

split_6fed_i_fastqc.html

Trimmed:

trim_1i_fastqc.html

trim_2i_fastqc.html

trim_3i_fastqc.html

trim_4i_fastqc.html

trim_5i_fastqc.html

trim_6i_fastqc.html

Dustin Atchley

2016-01-26T19:50:25Z

Duatchley:

Dustin Atchley

2016-01-26T19:37:24Z

Duatchley:

Dustin Atchley

2016-01-19T18:53:09Z

Duatchley:

Dustin Atchley

2016-01-19T18:46:22Z

Duatchley:

Hello world!

Username: davidson18
----

----

Most common mistake "I will remember how I got here". Take good NOTES!

Harvest .1 g of tissue, 100 micrograms of RNA from RNA isolation kit.
Don't know we didn't sample the wrong part of the organ since the amount is so small.
Be your hardest critic.

Isolated PolyA RNA (Ideally you get 2% of the RNA, protein coding)
Fragment the RNA before synthesizing.
DNA polymerase (reverse transcriptase) goes into mix, made cDNA.
Primers for amplification, you make a hexamer, which is every possible combination so that the complementary bases will bind.
Sequence cDNA that is the template to the RNA you are using. It is more stable.

Don't want full length DNAs, because we want about 75 bases for short reads.
Randomly fragmented which is wonderful.

Gel purified and reamplified (picture in PP)

We are sampling a wadded up tissue looking for proximal and distal end. Could have gotten serosa, need to be aware that we had to leave it frozen in order to not lose the RNA we needed.

Looking for genes involved in the up regulation of the amino acids that were measured in Figure 3 from 1a_pay_B4_pumpping_1995.

Housekeeping genes for liver and intestines (always on genes so we know we got the right cell)
Cell proliferation or cells growing (brush border growth, find these proteins)
Uptake genes

Dustin Atchley

2016-01-19T18:45:29Z

Duatchley:

Hello world!

Username: 18
----

----

Most common mistake "I will remember how I got here". Take good NOTES!

Harvest .1 g of tissue, 100 micrograms of RNA from RNA isolation kit.
Don't know we didn't sample the wrong part of the organ since the amount is so small.
Be your hardest critic.

Isolated PolyA RNA (Ideally you get 2% of the RNA, protein coding)
Fragment the RNA before synthesizing.
DNA polymerase (reverse transcriptase) goes into mix, made cDNA.
Primers for amplification, you make a hexamer, which is every possible combination so that the complementary bases will bind.
Sequence cDNA that is the template to the RNA you are using. It is more stable.

Don't want full length DNAs, because we want about 75 bases for short reads.
Randomly fragmented which is wonderful.

Gel purified and reamplified (picture in PP)

We are sampling a wadded up tissue looking for proximal and distal end. Could have gotten serosa, need to be aware that we had to leave it frozen in order to not lose the RNA we needed.

Looking for genes involved in the up regulation of the amino acids that were measured in Figure 3 from 1a_pay_B4_pumpping_1995.

Housekeeping genes for liver and intestines (always on genes so we know we got the right cell)
Cell proliferation or cells growing (brush border growth, find these proteins)
Uptake genes

Dustin Atchley

2016-01-19T18:45:16Z

Duatchley:

Hello world!

Username = 18
----

----

Most common mistake "I will remember how I got here". Take good NOTES!

Harvest .1 g of tissue, 100 micrograms of RNA from RNA isolation kit.
Don't know we didn't sample the wrong part of the organ since the amount is so small.
Be your hardest critic.

Isolated PolyA RNA (Ideally you get 2% of the RNA, protein coding)
Fragment the RNA before synthesizing.
DNA polymerase (reverse transcriptase) goes into mix, made cDNA.
Primers for amplification, you make a hexamer, which is every possible combination so that the complementary bases will bind.
Sequence cDNA that is the template to the RNA you are using. It is more stable.

Don't want full length DNAs, because we want about 75 bases for short reads.
Randomly fragmented which is wonderful.

Gel purified and reamplified (picture in PP)

We are sampling a wadded up tissue looking for proximal and distal end. Could have gotten serosa, need to be aware that we had to leave it frozen in order to not lose the RNA we needed.

Looking for genes involved in the up regulation of the amino acids that were measured in Figure 3 from 1a_pay_B4_pumpping_1995.

Housekeeping genes for liver and intestines (always on genes so we know we got the right cell)
Cell proliferation or cells growing (brush border growth, find these proteins)
Uptake genes

Housekeeping genes for intestines

2016-01-14T19:55:32Z

Duatchley:

Hello world!

http://www.ncbi.nlm.nih.gov/nucest/

Orthologs:
villin, fabp2, and cof1 in mammals,
PEPT1 protein --> SLC15A1 gene in humans,
PEPT2 protein --> SLC15A2 gene

In google for search: python bivittatus small intestine specific gene expression

Python specific:
http://www.pnas.org/content/110/51/20645.long
Small intestine heat map, figure 1. Investigate further
Genes that undergo large change in expression in small intestine: Pdk4 (mitochondria- increase), Slc35b4 (glycosylation- increase), DOCK7 (development- decrease), PRMT7 (chromatin-increase), DDX21 (transcription- decrease)