Dinh/Dinh 2014/NOTES/2014-4-1
Jump to navigation
Jump to search
DMR finding with MOdel-based Analysis of Bisulfite Sequencing data (MOABS)
- Data mapping statistics: File:Mouse WGBS DMR mapping.xls
- We obtained additional ESCs data from 3 sources:
TO.mESC http://www.ncbi.nlm.nih.gov/biosample/765198 C57BL/6J Jcl ST.mESC http://www.ncbi.nlm.nih.gov/biosample/SAMN00631233 Mixed (129-C57Bl/6) HA.mESC http://www.ncbi.nlm.nih.gov/biosample/SAMN01797652 129/Ola
- 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.
Citrullination: Padi4 gene methylation
- Genome coordinate of Padi4: chr4:140,301,423-140,330,118 (anti-sense strand)
- 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
- By visual inspection, we saw multiple DMRs associated with this gene. SCNTs in serum condition is epigenetically different from ESC/IPS.
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.
Down-sampling of the Habibi and Stadler ESC data
- From visual inspection, Alice noticed that many ESC vs SCNT/IPS DMRs have coverage only in Habibi/Stadler data.
- Downsample Habibi data to 30% and Stadler data to 70%.
- ESC vs SCNT
# DMRs %of previous total DMR called M1 10720 48% M2 5049 33% M3 4904 32%
- ESC vs IPS
# DMRs %of previous total DMR called M1 11542 52% M2 7622 38% M3 8632 43%
- From this, we decided that since currently MOABs does not consider replicates information, we need to do individual ESCs comparisons
A) AL.Indx11 vs SCNT/IPS B) TO.mESC vs SCNT/IPS C) ST.mESC vs SCNT/IPS
MOABS DMRs: individual ESCs versus SCNT/IPS, IPSCs versus SCNTs, SCNTs in 2i versus serum
- After the first run, there were very few DMRs since AL.Indx11 have low coverage, so I reduced the minCredibleDiff from 0.2 to 0.15
- These DMRs are generated from Method #2 (M2)
- AL.Indx11 (B6)
ESC vs IPS 218 (3.12% FDR) File:AL-Indx11 vs IPS M2 MOABS-dmrs.txt ESC vs SCNT 204 (1.13% FDR) File:AL-Indx11 vs SCNT M2 MOABS-dmrs.txt
- ST.mESC (mixed strain)
ESC vs IPS 19431 (1.59% FDR) File:ST-mESC vs IPS M2 MOABS-dmrs.txt ESC vs SCNT 14580 (3.09% FDR) File:ST-mESC vs SCNT M2 MOABS-dmrs.txt
- HA.mESC (completely unrelated mouse strain)
ESC vs IPS 43553 (4.31% FDR) File:HA-mESC vs IPS M2 MOABS-dmrs.txt ESC vs SCNT 38556 (8.83% FDR) File:HA-mESC vs SCNT M2 MOABS-dmrs.txt
- TO.mESC (B6, This data was initially not used in previous analysis because it clusters with 2i condition, and was not cultured in serum. We're not sure if cultured in 2i)
ESC vs IPS 5792 (0.65% FDR) File:TO-mESC vs IPS M2 MOABS-dmrs.txt ESC vs SCNT 6484 (0.86% FDR) File:TO-mESC vs SCNT M2 MOABS-dmrs.txt
- IPS vs SCNT (data from 4 iPSCs and 2 SCNTs were merged)
IPS vs SCNT 192 (0.73% FDR) File:IPS vs SCNT M2 MOABS-dmrs.txt
- SCNT 2i versus SCNT serum
2i vs Serum 91,304 (10.3% FDR) File:SCNT 2i vs Serum M2 MOABS-dmrs.txt
Overlapping DMRs
- All 4 ESCs vs IPS comparisons overlap at chr19:53899387-53899586 (Rbm20 gene)
- All 4 ESCs vs SCNT comparisons overlap at 3 locus on chr2
chr2 152511745 152513298 chr2 157385983 157386350 chr2 174122529 174122765
- Three serum ESCs vs IPS comparisons overlap at 10 locus: (4 overlaps with shared ESCvsIPS DMRs below)
chr10 68732965 68732991 chr12 55302415 55302447 chr12 55302784 55303208 chr15 94686100 94686227 (Tmem117) chr19 53899387 53899586 (Rbm20) chr19 57370738 57370842 chr2 152513152 152513298 (H13) chr4 46681920 46682134 chr6 28879426 28879441 (Snd1) chr6 93597488 93597639
- Three serum ESCs vs SCNT comparisons overlap at 11 locus:
chr14 13174572 13174598 chr15 53006501 53006543 chr15 94686186 94686204 chr16 91496671 91496779 chr19 53898966 53899834 chr2 152511745 152513298 chr2 157385983 157386350 chr2 157386802 157387020 chr2 174122529 174122765 chr6 28879426 28879460 chr7 6682306 6682455