Fri. Aug. 16, 2019 Geoduck genome paper BS analysis

Methylkit analysis of steven’s alignments

copy data to emu

 srlab@emu:~/GitHub/Shelly_Pgenerosa/analyses/JuviPgen_ALSL2lowd145/Get_DMRs_for_SR_v074_alignments/$ mkdir dedup_bams
 srlab@emu:~/GitHub/Shelly_Pgenerosa/analyses/JuviPgen_ALSL2lowd145/Get_DMRs_for_SR_v074_alignments/dedup_bams$ cd dedup_bams/
 srlab@emu:~/GitHub/Shelly_Pgenerosa/analyses/JuviPgen_ALSL2lowd145/Get_DMRs_for_SR_v074_alignments/dedup_bams$ wget https://gannet.fish.washington.edu/seashell/bu-mox/scrubbed/0807-004/EPI-205_S26_L004_R1_001_val_1_bismark_bt2_pe.deduplicated.sorted.bam
 srlab@emu:~/GitHub/Shelly_Pgenerosa/analyses/JuviPgen_ALSL2lowd145/Get_DMRs_for_SR_v074_alignments/dedup_bams$ wget https://gannet.fish.washington.edu/seashell/bu-mox/scrubbed/0807-004/EPI-206_S27_L004_R1_001_val_1_bismark_bt2_pe.deduplicated.sorted.bam
 srlab@emu:~/GitHub/Shelly_Pgenerosa/analyses/JuviPgen_ALSL2lowd145/Get_DMRs_for_SR_v074_alignments/dedup_bams$ wget https://gannet.fish.washington.edu/seashell/bu-mox/scrubbed/0807-004/EPI-214_S30_L004_R1_001_val_1_bismark_bt2_pe.deduplicated.sorted.bam
 srlab@emu:~/GitHub/Shelly_Pgenerosa/analyses/JuviPgen_ALSL2lowd145/Get_DMRs_for_SR_v074_alignments/dedup_bams$ wget https://gannet.fish.washington.edu/seashell/bu-mox/scrubbed/0807-004/EPI-215_S31_L004_R1_001_val_1_bismark_bt2_pe.deduplicated.sorted.bam
 srlab@emu:~/GitHub/Shelly_Pgenerosa/analyses/JuviPgen_ALSL2lowd145/Get_DMRs_for_SR_v074_alignments/dedup_bams$ wget https://gannet.fish.washington.edu/seashell/bu-mox/scrubbed/0807-004/EPI-220_S32_L004_R1_001_val_1_bismark_bt2_pe.deduplicated.sorted.bam
 srlab@emu:~/GitHub/Shelly_Pgenerosa/analyses/JuviPgen_ALSL2lowd145/Get_DMRs_for_SR_v074_alignments/dedup_bams$ wget https://gannet.fish.washington.edu/seashell/bu-mox/scrubbed/0807-004/EPI-221_S33_L004_R1_001_val_1_bismark_bt2_pe.deduplicated.sorted.bam
 srlab@emu:~/GitHub/Shelly_Pgenerosa/analyses/JuviPgen_ALSL2lowd145/Get_DMRs_for_SR_v074_alignments/dedup_bams$ wget https://gannet.fish.washington.edu/seashell/bu-mox/scrubbed/0807-004/EPI-226_S34_L004_R1_001_val_1_bismark_bt2_pe.deduplicated.sorted.bam
 srlab@emu:~/GitHub/Shelly_Pgenerosa/analyses/JuviPgen_ALSL2lowd145/Get_DMRs_for_SR_v074_alignments/dedup_bams$ wget https://gannet.fish.washington.edu/seashell/bu-mox/scrubbed/0807-004/EPI-227_S35_L004_R1_001_val_1_bismark_bt2_pe.deduplicated.sorted.bam

Create new methylkit R project and perform analysis
- R project here: Get_DMRs_for_SR_v074_alignments
- R script here: Get_DMRs_for_SR_v074_alignments.Rmd
  - script output here: Get_DMRs_for_SR_v074_alignments.html
  - Table of hypomethylated regions
  - Table of hypermethylated regions
  - Table of both hypo and hyper methylated regions

IGV analysis

Yaamini gave me a quick tutorial about how she used IGV to visualize differential methylation (thanks Yaamini!)
Jupyter notebook for preparation of files to load into IGV here: 20190816_Pgnrv074_DMRs_in_IGV.ipynb
- Summary of analysis: I did a bedtools intersect between hypo- + hyper-DMRs and coverage files (filtering for positions that have 3x coverage).
  - Bedgraph coverage files generated by jupyter notebook are here: 20190816_Pgnrv074_DMRs_in_IGV
- IGV session here: 20190816_Pgnrv074_DMRs.xml
  - I loaded in both percent methylation and number of C’s at each position overlapping with DMRs for samples:
    - 205(amb.-low)
    - 206(amb.-low)
    - 214(Super.low-low)
    - 215(Super.low-low)
    - 220(Super.low-low)
    - 221(Super.low-low)
    - 226(amb.-low)
    - 227 (amb.-low)
  - I loaded in CDS, mRNA, and gene tracks for v074 genome.
  - I loaded in DMRs (as regions)
  - Here’s an examples of what the diff. meth. looks like for scaffold 9 completely zoomed out:
    - the first 8 tracks are the number of C’s at each position. The first 4 tracks are the ambient-low group and the following 4 are the Super.low-low group.
    - The bottom 8 tracks are the percent methylation at each position order the same way as the first 8 tracks.
  - Here is one DMR zoomed in:
    - - This looks like the ambient-low group shows hypermethylation in this DMR compared to the Super.low-low group (see the first 4 of the bottom 8 tracks), HOWEVER there doesn’t seem to be any coverage of this DMR in the Super.low-low group where there is 4x coverage of it in each of the samples from the ambient-low group. So this seems like a false positive since the DMR was called because of lack of coverage. This is an issue that Yaamini encountered in here IGV diff. meth. analysis of C.virginica data
      - To weed out instances like this, I need to look into:
        
        some kind of normalization before comparing reads?
        
        stricter methylkit parameters for calling DMRs
        
        lit on how people do DMR analysis to avoid these false positives