Yaamini’s Notebook: Gonad Methylation Analysis Part 18

Preparing for discoveries

Before I can understand where differentially methylated loci (DML) are located within the C. virginica genome, I first need to identify DMLs! I used this R script to identify DMLs that were at least 50% different between control and treatment (high pCO2) samples (.csv here). Looking at the PCA was interesting, as the clustering was not as tight as I expected.

PCA

Figure 1. Principal Components Analysis of methylated regions in samples.

O2-5 (ambient conditions) are more closely clustered than any of the oysters from treatment conditions. It’s possible that there are organismal differences in methylation responses, or that we just didn’t have a large enough sample size to deal with this variation.

In the last part of the script, I saved my DML information as a BED file. I mimicked Steven’s code to do this. BED files have chromosome ID, start, and stop positions that I can use to pare down information about my DMLs. I can then compare the DML location with other important genomic features using bedtools. The intersect tool seems especially useful. I know we covered bedtools in the 2016 Bioinformatics class, so I’ll review those notes!

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Yaamini’s Notebook: Gonad Methylation Analysis Part 17

Note to self: Always double check things

  • I forgot to change the code in my subset and full sample notebooks so that bismark_methylation_extractor ran on the files I produced instead of those in the dignore folder. I switched the code and everything still works!
  • I thought I double checked what bismark_methylation_extractor outputs needed to be in the .gitignore but I left out several *deduplicated.txt files that were well over 100 MB. My mistake took me three days and one Github issue to figure out. Whoops. Now I know how to use the Github command line, add things to my .gitignore, and effectively undo commits to make Github Desktop happy.

I finished up the methylation extraction, HTML report, and summary report steps! I then started methylKit on the full samples to ensure reproducibility. When I’m finished, I’ll create BEDfiles and start to understand where differentially methylated loci are located and waht the gene functions are.

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