Looking at preliminary DML

I have DML!. To clarify, I have 15,386 DML when filtering my data for 5x coverage. That’s more than twice as many DML as I got for my C. virginica data. Before I characterize the location of my DML in the C. gigas genome, I’m going to do two different things…

Trying different coverage in methylKit

Since I used WGBS, there’s a chance that I can use a more stringent coverage filter instead of 5x coverage. I returned to my R Markdown script and reprocessed the data using 10x coverage. I somehow ended up with more DML using 10x coverage! The files I generated can be found below:

• All DML: BEDfile, csv
• Hypermethylated DML: BEDfile, csv
• Hypomethylated DML: BEDfile, csv

Looks like the reason why I have more DML when using 10x coverage is because I gained 300 hypermethylated and 600 hypomethylated DML. The increase in hypomethylated DML is interesting. My C. virginica data had a pretty even split of hyper- and hypomethylated DML, whereas with 5x coverage I have 700 more hypomethylated DML and 1,000 more hypomethylated DML with 10x coverage. I don’t entirely know why this is happening so good thing I already planned on checking things out in IGV.

Table 1. Number of CpG loci in various categories

Coverage	DML Background	All DML	Hypermethylated DML	Hypomethylated DML
5	11,285,932	15,385	7,384	8,001
10	5,210,744	16,324	7,669	8,665

Verifying DML in IGV

I need to confirm that my DML are real, and the best way to do that is in IGV. I realized my IGV veresion was outdated, so I downloaded version 2.6.3 from the website. Since I’m looking at different coverage metrics, I also wanted to generate some bedgraphs that matched the coverage settinsg I used. I created this Jupyter notebook by copying my C. virginica equivalent and created 5x and 10x begraphs for each sample. After moving the contents of my repository to gannet, I used URLs to add the following tracks to a new IGV session:

• C. gigas genome
• YRVA 5x bedgraph
• YRVA 10x bedgraph
• YRVL 5x bedgraph
• YRVL 10x bedgraph
• DML background 5x coverage
• DML background 10x coverage
• DML 5x coverage
• DML 10x coverage

I actually struggled quite a bit to load all of the tracks into the IGV session. I made the mistake of adding the full bedgraphs in the IGV, but couldn’t open the file to delete the tracks. When I tried editing the xml file itself, I could successfully add tracks but not remove them. I ended up deleting my session and starting a new one. Once I created a new session, I could do what I needed!

Figures 1-6. DML on various chromosomes.

Looking at the DML, I can see that I didn’t export my BEDfiles correctly. The DML background and DML themselves don’t overlap neatly with CpGs (they’re 1 bp off). I need to change how I modify the base pairs in methylKit and resave the tracks.

I also don’t trust the 10x DML that were not in 5x DML. I don’t know what’s going on, but it’s flaming hot garbage. The other DML in the 10x track that were common to the 5x track looked good, so I think I want to remove all 10x DML that are not common to the 5x track. I will figure out a way to do that.

Quick bismark update

The alignments went well! My first file deduplicated successfully, but my second file was saved with the wrong filename. I realized my wildcard was not specific enough, so I created a modified script from deduplication onwards and saved it here. I moved the script to Mox and started the job. Once I get the files reprocssed I’ll not only run through my analyses, but also create one master script so I don’t have to piecemeal anymore.

Going forward

1. Get revised bismark output from Mox and run through analysis pipeline
2. Fix DML background and DML BEDfiles in methylKit
3. Remove 10x DML that are not present in 5x DML track
4. Obtain C. gigas feature tracks
5. Characterize locations of DML
6. Conduct a gene enrichment and/or identify genes with DML and discern what it means