Or…the importance of toll-like receptor signalling in sswd!
All the red boxes are DEGs we have found. They all have increased expression in diseased seastars (fold change next to arrow).
We can tell that the probes were successfully created because the far left pcr product is bigger than its control (second lane) and the third is bigger than its control (fourth lane).
And here’s reyn getting the slides ready for hybridization!
Digging deeper into the seastar transcriptome, we’re realizing (not surprisingly) that not all diseased seastars were created equal…Especially those from FHL.
We see a difference between infected (red) and control (blue) stars, but we also see that the FHL diseased star is further from the other ones.
To get a better idea of the individual to individual variation, I made a heatmap.
Here’s a screenshot of part of it. Interestingly, we see that the DB and PH sites for both diseased and healthy are more similar than the FHL sample. Here, we see that DB diseased and PH diseased are both strongly expressing these genes but FHL diseased is not.
We’ll work on figuring out what this means in the days to come!
After many frustrated hours and one very patient roommate who listened to me talk to myself/her/my boyfriend/my computer, I have uploaded my Rscript to github.
So, if you want to see how I merged files and ran them through DESeq you should be able to download my script here!
Today we added the gene ontology information to the seastar transcriptome contigs.
We ran the count data through deseq2 to find significantly differentially expressed genes.
There were a lot!
Here is today’s notebook:
We then added the uniprot IDs back onto the significantly DEGenes and summarized the resulting information using revigo:
We also ran them through david to find enriched genes:
Interestingly, we see a lot of immune-function related genes! More to come!
First look at the transcriptome!
We failed to reject our null hypothesis.
Today marked the beginning of our transition from conventional labwork into the wonderful and scary world of bioinformatics. We learned how to use Ipython, which is very cool and saves all of your code so you can go back and see what you did! Much better than those text files with random bits copied and pasted that I’ve been “maintaining”. I will definitely be using this! Unfortunately, my notebook from this morning didn’t save…Even though I hit the save button multiple times. I should probably make sure that I have saved all of my hard work next time. In brief, we downloaded the Swissprot information and made it into a blast database. We then blasted our transcriptome against this database, modified the output and uploaded it to sqlshare.
I was also able to look at some histology slides of my study organism, acropora cervicornis with Carolyn. Going to do some In situ hybridization in the near future, very excited!!