Thinking about SNPs

Steven and I have gone through the BS-Snper code and results multiple times this week to determine how to deal with SNPs that overlapped with DML. We dove into the BS-Snper program information to understand how it could identify C->T SNPs over bisulfite-converted unmethylated CpGs. During bisulfite conversion, unmethylated cytosines are replaced with uracils, then converted to adenines during PCR amplification. The complementary strand for this locus would have a guanine, since the original base pair was a cytosine. If there’s a C->T SNP, then the complementary strand would have an adenine.

Once we clarified the methods, we realized that the SNPs needed to be excluded from any DML characterization: they are false DML! I removed the SNPs from DML using subtractBed in this script. I used my revised DML track with no SNPs to characterize genomic locations. Once I updated the counts, I returned to this R Markdown script to revise my heatmap and chromosome distribution figures. Then, I used the updated counts in this R Markdown script to update my stacked barplot and gene count table.

Figure 1. Heatmap of unique DML

Figure 2. DML distribution over chromosomes

Figure 3. Genomic location of DML

I also went through and removed SNPs from my general methylation characterization. No point characterizing the methylation status of loci that are incorrectly on that list anyways! I used this Jupyter notebook to remove SNPs from the 5x union BEDgraphs I used to separate highly, moderately, and lowly methylated CpGs. I used the updated counts in this R Markdown script to revise my figures and supplementary tables.

Figure 4. Genomic location of CpG categories

Nothing left to do now but put the finishing touches on!

Going forward

1. Revise title
2. Format for submission
3. Submit preprint to bioRXiv
4. Submit paper for publication
5. Report mc.cores issue to methylKit
6. Perform randomization test
7. Determine if larval DNA/RNA should be extracted