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=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: AL.mESC (Alice's mESC, same strain as IPS and SCNT in dataset)C57BL/6J Jcl 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 | 300px]] *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 hidden markov model with 3 hidden states: hyper/hypo/no_diff. **the states are determined by the difference of true methylation ratios bigger than some value. The default value is 0.2 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), the minimum depth requirement is set at default (=3), and is recommended for method 2. ** We found out that since the replicates for SCNT/IPS are merged, there minimum depth per sample is not 3, so to have greater confidence for the DMRs, we filtered each GBED file to allow only minimum 5x depth of coverage per site. * AL.Indx11 (B6) ESC vs IPS 218 (3.12% FDR) [[File:AL-Indx11_vs_IPS_M2_MOABS-dmrs.txt]] ESC vs IPS 153 (4.64% FDR) [[File:AL-Indx11_vs_IPS_M2_MOABS-dmrs-min5depth.txt]] ESC vs SCNT 204 (1.13% FDR) [[File:AL-Indx11_vs_SCNT_M2_MOABS-dmrs.txt]] ESC vs SCNT 151 (0.99% FDR) [[File:AL-Indx11_vs_SCNT_M2_MOABS-dmrs-min5depth.txt]] * ST.mESC (mixed strain) ESC vs IPS 19431 (1.59% FDR) [[File:ST-mESC_vs_IPS_M2_MOABS-dmrs.txt]] ESC vs IPS 16771 (1.83% FDR) [[File:ST-mESC_vs_IPS_M2_MOABS-dmrs-min5depth.txt]] ESC vs SCNT 14580 (3.09% FDR) [[File:ST-mESC_vs_SCNT_M2_MOABS-dmrs.txt]] ESC vs SCNT 11899 (2.69% FDR) [[File:ST-mESC_vs_SCNT_M2_MOABS-dmrs-min5depth.txt]] * HA.mESC (different mouse strain) ESC vs IPS 43553 (4.31% FDR) [[File:HA-mESC_vs_IPS_M2_MOABS-dmrs.txt]] ESC vs IPS 39121 (5.52% FDR) [[File:HA-mESC_vs_IPS_M2_MOABS-dmrs-min5depth.txt]] ESC vs SCNT 38556 (8.83% FDR) [[File:HA-mESC_vs_SCNT_M2_MOABS-dmrs.txt]] ESC vs SCNT 32806 (8.52% FDR) [[File:HA-mESC_vs_SCNT_M2_MOABS-dmrs-min5depth.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 IPS 4751 (0.50% FDR) [[File:TO-mESC_vs_IPS_M2_MOABS-dmrs-min5depth.txt]] ESC vs IPS 5792 (0.65% FDR) [[File:TO-mESC_vs_SCNT_M2_MOABS-dmrs.txt]] ESC vs IPS 5122 (0.82% FDR) [[File:TO-mESC_vs_SCNT_M2_MOABS-dmrs-min5depth.txt]] * IPS vs SCNT (data from 4 iPSCs were merged and data from 2 SCNTs were merged) IPS vs SCNT 192 (0.73% FDR) [[File:IPS_vs_SCNT_M2_MOABS-dmrs.txt]] IPS vs SCNT 173 (0.64% FDR) [[File:IPS_vs_SCNT_M2_MOABS-dmrs-min5depth.txt]] * SCNT 2i versus SCNT serum ** The number of DMRs is very high, is this expected because Habibi et al found striking differences between the two culture conditions as well. ** The FDR rate is high for 0.15diff so I increased the minimum to 0.20diff to lower FDR. 2i vs Serum 91304 (10.30% FDR) [[File:SCNT_2i_vs_Serum_M2_MOABS-dmrs.txt]] 2i vs Serum 83247 (12.03% FDR) [[File:SCNT_2i_vs_Serum_M2_MOABS-dmrs-min5depth.txt]] 2i vs Serum 44308 (5.15% FDR) [[File:SCNT_2i_vs_Serum_M2_MOABS-dmrs-min5depth-0.20diff.txt]] === PAVIS - Genes region annotation pie charts === * Upload the files to PAVIS, 0 bp extension on both sides * AL mESC vs IPS [[File:pavis_ALmESC_vs_IPS_dmrs.png |300px]] * AL mESC vs SCNT [[File:pavis_ALmESC_vs_SCNT_dmrs.png|300px]] * AL IPS vs SCNT [[File:pavis_AL_IPS_vs_SCNT_dmrs.png|300px]] === Discussion === * Analysis hierarchy: **We clustered all of the samples together (including 2i, Serum, and different strains) and found that there are more strain specific differences then method differences at the top level. We also saw 2i versus Serum differences. **We decided that to detect method specific differences, we need to use cell lines from the same strain and same growth conditions ***(We will include the analysis using the difference strains in the supplementary information) **Our analysis revealed method specific DMRs (at < 5% FDR) that we will go on to characterize. *** First, we looked at the genes-region association, and determined whether the DMRs are associated with specific pathways *** Second, we looked at the DMRs in each comparison to see whether they are in promoter, introns, exons, or intergenic *** Third, we found X, and Y% of DMRs are hypermethylated and hypomethylated respectively, and show the heat map of those DMRs. *** Fourth, we perform bisulfite sequencing of the samples to validate a small number of DMRs. **We have SCNT in both 2i and Serum conditions, and found a large number of DMRs between them. *** We characterize these DMRs, and compare our results with the results from Habibi et al. (which compared ESC in 2i versus Serum)
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