Dinh/Dinh 2014/NOTES/2014-4-1
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DMR finding with MOdel-based Analysis of Bisulfite Sequencing data (MOABS)
- Using only the mcomp module from the MOABS pipeline requires:
1. setting LC_ALL variable with: export LC_ALL=C 2. installing RInside for R. Open R and type: install.packages("RInside")
- The input for mcomp is a G.bed file (ie), more detailed explanation see the MOABS documentations.
#chrom start end ratio totalC methC strand next Plus tcP mcP Minus tcM mcM chr5 3009585 3009587 1 1 1 - G + 0 0 - 1 1 chr5 3011471 3011473 0.711 45 32 B G + 28 18 - 17 14
Analysis of mouse WGBS
- Prepare the G.bed files with methylFreq2Gbed.pl (genome-miner)
/home/dinh/scripts/methylFreq2GBED.pl 1 < [methylFreq] > [BED name]
- We perform 3 tests:
esc versus ipsc esc versus scnt ipsc versus scnt
- MOABS commands:
export LC_ALL=C /home/dinh/softwares/moabs-v1.2.7.src.x86_64_Linux.data/bin/mcomp -r merged.esc.$c.G.bed -r merged.scnt.$c.G.bed -c moabs.esc_scnt.$c -l esc.$c scnt.$c /home/dinh/softwares/moabs-v1.2.7.src.x86_64_Linux.data/bin/mcomp -r merged.esc.$c.G.bed -r merged.ips.$c.G.bed -c moabs.esc_ips.$c -l esc.$c ips.$c /home/dinh/softwares/moabs-v1.2.7.src.x86_64_Linux.data/bin/mcomp -r merged.ips.$c.G.bed -r merged.scnt.$c.G.bed -c moabs.ips_scnt.$c -l ips.$c scnt.$c
Results
- MOABS calculated DMRs using 3 methods. Need to look up what the three methods are..
- Method #1, # DMRs
- call Dmc and Dmr according to method 1: Fisher Exact Test
- Dmc is defined as depth >= minDepth(suggest 10), p<p_fet_cut(suggest 0.05), nMethDif > nominalMethDifCutoff(0.3333)
- Dmr is defined as minC >=3, maxDist 300, p<p_fet_cut(suggest 0.05), nMethDif > nominalMethDifCutoff(0.3333)
22,125 esc_vs_ips File:Dmr M1 esc vs ips.txt 22,135 esc_vs_scnt File:Dmr M1 esc vs scnt.txt 709 ips_vs_scnt File:Dmr M1 ips vs scnt.txt
- Method #2, # DMRs
- call Dmc and Dmr according to method 2: Using the credible methylation difference metric (beta-binomial) distribution.
- Dmc is defined as cMethDif > credibleMethDifCutoff(0.2)
- Dmr is defined as minC >=3, maxDist 300, cMethDif > credibleMethDifCutoff(0.2)
19,799 esc_vs_ips File:Dmr M2 esc vs ips.txt 15,318 esc_vs_scnt File:Dmr M2 esc vs scnt.txt 53 ips_vc_scnt File:Dmr M2 ips vs scnt.txt
- Method #3, # DMRs
- call Dmc according to method 2
- Dmc is defined as cMethDif > credibleMethDifCutoff(0.2)
- call Dmr using Hidden Markov Model
20,124 esc_vs_ips File:Dmr M3 esc vs ips.txt 15,550 esc_vs_scnt File:Dmr M3 esc vs scnt.txt 21 ips_vc_scnt File:Dmr M3 ips vs scnt.txt
- The SCNTs appear to be more similar epigenetically to iPSCs than to ESCs.
- I converted the DMR files to bed format and uploaded them to GREAT for enrichment analysis. For both sets of DMRs between esc and ips/scnt, the pathway that comes up is protein citrullination.
- I looked up the term and found an interesting excerpt from Wikipedia:
- Citrullination is important because it controls the expression of genes, particularly in the developing embryo, and because the immune system often attacks citrullinated proteins, leading to autoimmune diseases such as rheumatoid arthritis and multiple sclerosis.
False discovery rate
- which method have the lowest false discovery rate?
- shuffle sample labels
Next steps
- Determine whether method 1, 2 or 3 is more appropriate.
- Generate plots to see whether the DMRs are also detectable by eye.
- Does the lower depth of coverage for the non-ESC samples (Habibi et al data have 30x average depth of coverage while our data have between 7-10 average depth of coverage) affect the number of DMRs detected for iPSCs versus SCNTs ? I need to downsample the Habibi and Stadler data.
- Use MOABS to find DMRs between 2i ESC and 2i SCNTs