Dinh/Dinh 2014/NOTES/2014-4-1: Difference between revisions

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>Dinh
(Created page with "=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:...")
 
>Dinh
mNo edit summary
Line 16: Line 16:
   esc versus scnt
   esc versus scnt
   ipsc versus scnt
   ipsc versus scnt
* MOABS commands:
  /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:
  Method #1, # DMRs
  22,125 esc_vs_ips
  22,135 esc_vs_scnt
    709 ips_vs_scnt
 
  Method #2, # DMRs
  19,799 esc_vs_ips
  15,318 esc_vs_scnt
      53 ips_vc_scnt
  Method #3, # DMRs
  20,124 esc_vs_ips
  15,550 esc_vs_scnt
      21 ips_vc_scnt
* 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'''. It appears that the DMRs have many overlaps.
* 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.

Revision as of 18:19, 2 April 2014

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:
 /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:
 Method #1, # DMRs
 22,125 esc_vs_ips
 22,135 esc_vs_scnt
    709 ips_vs_scnt
 
 Method #2, # DMRs
 19,799 esc_vs_ips
 15,318 esc_vs_scnt
     53 ips_vc_scnt
 Method #3, # DMRs
 20,124 esc_vs_ips
 15,550 esc_vs_scnt
     21 ips_vc_scnt
  • 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. It appears that the DMRs have many overlaps.
  • 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.