Difference between revisions of "February 2, 2016"
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| − | '''''Moving forward, we need to assign gene ontology terms to all genes. | + | '''''Moving forward, we need to assign gene ontology terms to all genes.''''' |
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=== R-Commander Work === | === R-Commander Work === | ||
| − | *Compare gene | + | *Compare gene output for all of liver reads to all of intestine reads. |
*Generate heat map (FIGURE OUT HOW TO ADD SCREENSHOT). | *Generate heat map (FIGURE OUT HOW TO ADD SCREENSHOT). | ||
**Liver and intestine are expressing different genes. | **Liver and intestine are expressing different genes. | ||
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'''''Moving forward, we need to figure out the codes that will allow us to cluster genes in the most logical way.'''''' | '''''Moving forward, we need to figure out the codes that will allow us to cluster genes in the most logical way.'''''' | ||
| − | *Intestines Group 3 is trying to write code to cluster only intestines data for the 3 fed versus the 3 non-fed snakes. | + | *Intestines Group 3 is trying to write a code to cluster only intestines data for the 3 fed versus the 3 non-fed snakes. |
Latest revision as of 15:21, 8 March 2016
Contents
Classwork
1. Blast the overrepresented sequences
- Blast groups found mostly mitochondrial or ribosomal genes.
- Intestine blast group found an amino transferase gene that may be of interest later.
- Liver blast group found an anti-hemorage gene that may be of interest later.
2. Attempt to access the list of genes (SRP-0151827) mentioned in the Andrew et al. (2015) study
- We can blast our sequences against the Andrew et al. (2015) library.
- We have the potential to download the entire library with instructions on the website, but do not understand how to use the software/programs required to do so.
- We found a toolkit that allows us to download one sequence at a time.
- We can blast within a run.
3. Add numbers to label proteins of "unknown function" in our reads
- Dylan and Dustin made a file that removed duplicate names from our runs.
- Dr. Campbell and Dr. Heyer used their file and got gene mapping results!
4.Normalize relative abundance of transcripts
- We used DEseq to normalize data.
- Data can be normalized using a total read count (TC) normalization.
Moving forward, we need to assign gene ontology terms to all genes.
DEseq
R-Commander Work
- Compare gene output for all of liver reads to all of intestine reads.
- Generate heat map (FIGURE OUT HOW TO ADD SCREENSHOT).
- Liver and intestine are expressing different genes.
- Fed-snakes are not clustered in the way we would expect.
- Cluster is based off of every gene.
- Dendrogram shows which samples are more similar to each other.
Moving forward, we need to figure out the codes that will allow us to cluster genes in the most logical way.'
- Intestines Group 3 is trying to write a code to cluster only intestines data for the 3 fed versus the 3 non-fed snakes.
References"
- Andrew, Audra L., et al. “Rapid changes in gene expression direct rapid shifts in intestinal form and function in the Burmese python after feeding.” Physiol Genomics. 47 (2015): 147-157.
