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==DMR finding using DSS== *Yupeng (Joe Ecker group student) suggested to try DSS for DMR finding, as it was used by the Ecker group on the human SCNT paper. (DSS is an R library performing differntial analysis for count-based sequencing data. It detectes differentially expressed genes (DEGs) from RNA-seq, and differentially methylated loci or regions (DML/DMRs) from bisulfite sequencing (BS-seq). The core of DSS is a new dispersion shrinkage method for estimating the dispersion parameter from Gamma-Poisson or Beta-Binomial distributions. [http://www.bioconductor.org/packages/release/bioc/manuals/DSS/man/DSS.pdf LINK]) *Shicheng helped with modifying and writing script 1. run DSS (defined fdr_cutoff=0.05) between 2 groups: group1=miPS_B3, miPS_1E12P20; group2=SCNT_NB3, SCNT_B12 2. DMS (differentially methylated sites) output from DSS: chr; pos; mu1 (mean methylation level of group1); mu2 (mean methylation level of group2); diff (difference between the means); diff.se (diff.se - standard error of the difference); stat (stat - test statistic); phi1 (phi of group1); phi2 (phi of group2); pval (p-value); fdr (false discovery rate); postprob.overThreshold (reprsenting the posterior probability of the difference in methylation greater than delta) 3. download annotated CpG island-related (CpGi, CpG shore, CpG shelf) and repeat-related (LINE, SINE, LTR, SimpleRepeat) files from UCSC 4. use bedtools to intersect DSS output DMS with annotated functional regions 5. record output # of hits 6. perform random permutation (n=100,000) to detect the significance of the enrichment at those regions (1) generate a tmp file with # of regions (each have random 2bp overlap within the mouse genome) matching with # of DMR calculated by DSS (2) intersect the tmp file with each functional region of interest (e.g. CpGi, CpG shore, etc...) (3) do a word count of the # lines that would show up in the functional regions (4) repeat for 100,000 times (5) assuming data from random permutation is normally distributed, then calculate the p-value for the level of enrichment in our DMR set ===Results=== *total # of DMS between human SCNT and iPSC (hg19, Ma et al): 111 *Here are the # of DMRs we found that overlapped with different functional regions within the human genome (annotation downloaded from UCSC table browser): **CpG island: 7 (2bp DMS p-value: <10^-6) **CpG shore: 12 (2bp DMS p-value: 0.00063, fold of enrichment: 3.02 ) **CpG shelf: 10 (2bp DMS p-value: 0.00524 *total # of DMS between mouse SCNT (2 lines cultured with serum) and 4 iPSC lines: 489 (0.05 FDR) *Here are the # of DMRs we found that overlapped with different functional regions within the mouse genome (annotation downloaded from UCSC table browser): **CpG island: (2bp DMS p-value: ) **CpG shore: 28 (2bp DMS p-value: 5.214671e-05; fold of enrichment:) **CpG shelf: (2bp DMS P-value: ) **DNase I hypersensitive sites (UCSC ENCODE data from UW): ~88 (2bp DMS, p-value: <10^-6) **Enhancer for Oct4, Sox2, Nanog, Med1, H3K27Ac, Klf4, and Esrrb in mESC: 10 (2bp DMS, p-value: 0.00053) (reference: Master Transcription Factors and Mediator Establish Super-Enhancers at Key Cell Identity Genes. Whyte et al. Cell. 2013) **superenhancer and typical enhancer ChiP-Seq data: (reference: Super-Enhancers in the Control of Cell Identity and Disease, doi:10.1016/j.cell.2013.09.053) *total # of DMR we identified overlapped with topological domains that were previously published by Ren lab (source: Dixon JR et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature. 2012;485:376-380) **J1 mESC data (HindIII_combined,mm9) **total # of DMS overlapped with topological domains: 457 (2bp DMS, p-value: 0.013291) ===Discussion=== *We have found significantly level of differences between iPSC and SCNT at CpG shore and shelf *Although we have confirmed some DMS by eye balling, however, we can't rule out the possibility of epigenetic memory from progenitor, sample-specific phenomenon, etc. *we will perform the same analysis on human SCNT, iPSC sample sets to determine whether we can find the consistent pattern. *the discrepancy does not mean what we found were not biologically meaningful. hESCs and mESCs are very different in terms of DNA methylation. For example, abundant non-CG methylation does not show up (or appear at extremely low level) in mESCs. Thus, discrepancy in methylation patterns between hESCs and mESCs is expected. We will keep looking at overlap of these DMRs with DHSs (from Dnase-seq), putative enhancers (based on chromatin data) and transcription factor binding sites (TF ChIP-seq data).
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