Killifish Hypoxia RRBS Part 19

Wrapping up the initial methylation analysis

I have a few loose ends to tie up before finishing my preliminary methylation analysis: methylation landscape information and an overview of the closest annotated genes.

Methylation landscape analysis

I tried looking through the output to figure out if this information was listed in a file anywhere. I didn’t have the log files anymore, but I don’t think it was listed there. In any case, if someone else isn’t able to access the information I used, then it wasn’t reproducible! So I returned to this Jupyter notebook to revise the methylation landscape analysis. Neel said that I should recalculate my statistics using 0% for the cutoff for methylation (i.e. a CpG was either methylated or unmethylated, and it didn’t matter how intensely it was methylated).

**Table 1. Methylation landscape information calculated using all common CpGs for each specific contrast.

Contrast Methylated CpGs (%) Unmethylated CpGs (%) Average Methylation
All samples 7275 (48.8%) 7620 (51.2%) 20.7%
All NBH 12216 (82.0%) 2679 (18.0%) 20.6%
All SC 12536 (84.2%) 2359 (15.8%) 20.7%
Hypoxic NBH 96857 (54.9%) 79429 (45.1%) 22.1%
Normoxic NBH 93895 (53.3%) 82391 (46.7%) 20.4%
Hypoxic SC 22694 (27.6%) 59611 (72.4%) 16.4%
Normoxic SC 45661 (55.5%) 36644 (44.5%) 16.2%

Compared to what I have seen with other fishes, general methylation seems to be low. Interestingly, the tolerant NBH population doesn’t have much of a difference between the methylated:unmethylated ratios after hypoxia exposure, but the sensitive SC population does (~70% unmethylated in hypoxia vs. ~45% in normoxia). Average percent methylation is also lower in the SC population. Seeing population differences makes me think more about identifying SNPs between these populations and trying to identify mQTLs.

Closest genes to DMR

The next thing I wanted to do was dig into the functions of the closest genes to DMR. I used the ENSEMBL and NCBI databases to find this informaiton.

Overlapping genes:

Closest genes:

The fact that one of the DMR was close to a gene not present in the latest mummichog annotation supports the idea of re-doing RRBS and RNA-Seq alignment with the new genome! Something to consider after doing an initial pass of the data.

Going forward

  1. Revise methylation landscape information
  2. Update methods and results
  3. Match DMR with RNA-Seq information
  4. Start mapping with new genome
  5. Try DMR identification with bismark and methylKit
  6. Create OSF repository for all intermediate files
Written on June 9, 2022