Sean’s Notebook: C. virginica MACAU results.
So I finished running MACAU on the C. virginica oil spill samples a couple of different ways. First, I ran all 6 samples, 3 control and 3 oil exposed and secondly, 2 control and 3 oil exposed. The difference being one of the samples had really low read counts from the sequencing facility, and that brought down the number of available loci with a minimum of 10x coverage to look at from ~70,000 to ~17,000
Neither produced many loci that had meaningful priors, the 6 sample run had 4, and the 5 sample had 16. None had meaningful predictor parameters (beta), meaning that oil exposure was not a significant predictor of methylation status. After talking to Steven, this is not surprising, as other recent studies have shown similar lack of differentially methylated regions/loci.
Also, I’ve started revamping the Hyak wiki, trying to chunk it out to make it a little more readable by the average person. It’s on my personal Github wiki at the moment, but I will copy it over once I got some input on formatting and a few things.
Notebook for Methylation stuff: here
Hyak Wiki stuff: here
(1) Oyster seed morphology:
Continuing with measuring the lengths of oyster seed for Rhonda’s project. I think I will have to re-do Silo 3 and 9 from 7/14/16 because I didn’t get 100 seed pictured. Link to spreadsheet here. Link to images here.
(2) Minor revisions on Jake’s paper: Evidence of Ostrea lurida (Carpenter, 1864) population structure in Puget Sound, WA (link to PeerJ Preprint: here.)
Most of the revisions were references and typos. Other revision comments were sent to Steven to be changed for the final resubmission (Due by June 26th).
Day at Manchester:
Helped Laura with her project by screening for oyster larvae! I really enjoyed this task. I screened the larvae at 224, 180, and 100 microns. I then screened a mortality bucket at 100 microns. Will go out again this Thursday.
I was gifted 3,000 extra proteins
When error checking the new Skyline document from the revised “desearlinated” .blib, I noticed that I had 9,047 proteins instead of about 6,000. Where did these 3,000 extra proteins come from? What do they do? To answer this question, I ran the new Skyline output through the same preliminary pipeline I used for my NSA poster.
Videos of my narrating my findings quietly because I’m at my parents’ house and they’re asleep:
Most interesting finding: miRNA inhibition of translation is a significant GO term for biological processes that’s overrepresented across all of my samples!
from the responsible grad student http://ift.tt/2t0A9M6