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

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>Dinh
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* Methylation plot
* Methylation plot
  [[File:my_methyl_Padi4_graph.pdf]]
  [[File:my_methyl_Padi4_graph.pdf]]
* Labels:
  mES1_serum    B6ES
  mES2_2i        Habibi_ES_2i_adapted
  mES3_2i        Habibi_ES_2i
  mES4_serum    Habibi_ES_serum
  miPS1_serum    miPS_1E12P20
  miPS2_serum    miPS_2A4F1
  miPS3_serum    miPS_2A4F33
  miPS4_serum    miPS_B3
  mSCNT1_serum  SCNT_B12_serum_adapted
  mSCNT2_serum  SCNT_NB3_serum_adapted
  mSCNT3_2i      SCNT_ntesP7C_2i
  mSCNT4_2i      SCNT_ntesP8B_2i
* Plotting script
* Plotting script
  [[File:plot_methyl_graph_PADI4.txt]]
  [[File:plot_methyl_graph_PADI4.txt]]

Revision as of 22:36, 11 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:
 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.
  • GREAT analysis on DMRs using single gene per region association within 10kb extended from midpoint of regulatory domain.
  • 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
        (Pathways common/BioCyc pathways) protein citrullination - 5 genes/15 regions: Padi1, Padi2, Padi3, Padi4, Padi6 
 22,135 esc_vs_scnt File:Dmr M1 esc vs scnt.txt
        (Pathways common/BioCyc pathways) protein citrullination - 5 genes/15 regions: Padi1, Padi2, Padi3, Padi4, Padi6 
    708 ips_vs_scnt File:Dmr M1 ips vs scnt.txt
        No pathway, but there were 11 significant terms for Mouse Phenotype:
        File:Dmr M1 ips vs scnt mousephenotype.png
 
  • 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
        GO Molecular Function
             protein-arginine deiminase activity, 
             hydrolase activity (acting on carbon-nitrogen) but not peptide bonds
             IgG binding
        (Pathways common/BioCyc pathways) protein citrullination - 5 genes/13 regions: Padi1, Padi2, Padi3, Padi4, Padi6
 15,318 esc_vs_scnt File:Dmr M2 esc vs scnt.txt
        GO Molecular function
             protein-arginine deiminase activity
        GO Biological process
             peptidyl-citrulline biosynthetic process from peptidyl-arginine
        (Pathways common/BioCyc pathways) protein citrullination - 5 genes/12 regions: Padi1, Padi2, Padi3, Padi4, Padi6
         BioCyc pathways 
             phosphate acquisition II
     52 ips_vc_scnt File:Dmr M2 ips vs scnt.txt
         No term
  • 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
        GO Molecular function
              protein-arginine deiminase activity
        (Pathways common/BioCyc pathways) protein citrullination - 5 genes/11 regions: Padi1, Padi2, Padi3, Padi4, Padi6
 15,550 esc_vs_scnt File:Dmr M3 esc vs scnt.txt
        GO Molecular function
             protein-arginine deiminase activity
        GO Biological process
             peptidyl-citrulline biosynthetic process from peptidyl-arginine
        (Pathways common/BioCyc pathways) protein citrullination - 5 genes/13 regions: Padi1, Padi2, Padi3, Padi4, Padi6
     21 ips_vc_scnt File:Dmr M3 ips vs scnt.txt
        GO Biological Process
             negative regulation of vascular endothelial growth factor receptor signaling pathway
             negative regulation of cellular response to growth factor stimulus
  • 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

  • A) samples were randomly assigned to either group 1 or group 2.
  • B) samples were randomly assigned to either group and the positions were randomly assigned (while keeping the chromosome assignments constant.)
  • ESC vs IPS
              A       B
 Method #1    56.20%  2.70%
 Method #2    47.90%  2.23%
 Method #3    45.77%  0.92%
  • ESC vs SCNT
              A       B
 Method #1    88.12%  6.84%
 Method #2    75.52%  5.38%
 Method #3    72.32%  2.03%
  • IPS vs SCNT
              A       B
 Method #1    89.69%  2.51%
 Method #2    52.31%  0.19%
 Method #3    45.24%  0%
  • Method #3 have the lowest FDR.

Citrullination: Padi4 gene methylation

  • Methylation plot
File:My methyl Padi4 graph.pdf
  • Labels:
 mES1_serum     B6ES
 mES2_2i        Habibi_ES_2i_adapted
 mES3_2i        Habibi_ES_2i
 mES4_serum     Habibi_ES_serum
 miPS1_serum    miPS_1E12P20
 miPS2_serum    miPS_2A4F1
 miPS3_serum    miPS_2A4F33
 miPS4_serum    miPS_B3
 mSCNT1_serum   SCNT_B12_serum_adapted
 mSCNT2_serum   SCNT_NB3_serum_adapted
 mSCNT3_2i      SCNT_ntesP7C_2i
 mSCNT4_2i      SCNT_ntesP8B_2i
  • Plotting script
File:Plot methyl graph PADI4.txt

Discussion

  • We should proceed with method 3 DMRs as these have the lowest FDR.

Next steps

  • 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