Kun:LabNotes/MONOD/2015-3-11: Difference between revisions

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   /home/kunzhang/softwares/bedtools-2.17.0/bin/bedtools intersect -wa -a N37_WGBS_tumor_seqCap_RRBS_tumor_NC_all_chrs_RD10_80up.mld_blocks.bed -b ~/CpgMIP/MONOD/Data/141112_HiSeqRapidRun/OID42096_hg19_UMR_v1_capture_targets.bed > N37_WGBS_tumor_seqCap_RRBS_tumor_NC_all_chrs_RD10_80up.mld_blocks_seqCap_subset.bed
   /home/kunzhang/softwares/bedtools-2.17.0/bin/bedtools intersect -wa -a N37_WGBS_tumor_seqCap_RRBS_tumor_NC_all_chrs_RD10_80up.mld_blocks.bed -b ~/CpgMIP/MONOD/Data/141112_HiSeqRapidRun/OID42096_hg19_UMR_v1_capture_targets.bed > N37_WGBS_tumor_seqCap_RRBS_tumor_NC_all_chrs_RD10_80up.mld_blocks_seqCap_subset.bed
   /home/kunzhang/softwares/bedtools-2.17.0/bin/bedtools intersect -wa -a N37_WGBS_tumor_seqCap_RRBS_tumor_NC_all_chrs_RD10_80up.mld_blocks.bed -b /home/kunzhang/CpgMIP/Data/MONOD/1407-combined_RRBS/latest_organized_data/target_files/Primary_tumor_ALL.genomecov.RD50_80UP.merged.bed > N37_WGBS_tumor_seqCap_RRBS_tumor_NC_all_chrs_RD10_80up.mld_blocks_RRBS_subset.bed
   /home/kunzhang/softwares/bedtools-2.17.0/bin/bedtools intersect -wa -a N37_WGBS_tumor_seqCap_RRBS_tumor_NC_all_chrs_RD10_80up.mld_blocks.bed -b /home/kunzhang/CpgMIP/Data/MONOD/1407-combined_RRBS/latest_organized_data/target_files/Primary_tumor_ALL.genomecov.RD50_80UP.merged.bed > N37_WGBS_tumor_seqCap_RRBS_tumor_NC_all_chrs_RD10_80up.mld_blocks_RRBS_subset.bed
===Extract haplotypes into hapInfo files based on the informative bins.
===Extract haplotypes into hapInfo files based on the informative bins.===
*For WGBS data, just operate on the 10-tissue pooled data for now.  
*For WGBS data, just operate on the 10-tissue pooled data for now.  
*For RRBS data, use the subsets of mld_blocks that overlap with the RRBS target.
*For RRBS data, use the subsets of mld_blocks that overlap with the RRBS target.
*For SeqCap data, use the subsets of mld_blocks that overlap with the SeqCap target.
*For SeqCap data, use the subsets of mld_blocks that overlap with the SeqCap target.

Revision as of 00:16, 20 March 2015

Data analysis: defining bins based on methylation haplotype blocks

  • I wanted to be more systematic in analyzing the combined data from RRBS, SeqCap and BSPP experiments.

Define informative bins for haplotype analysis

  • A key is to define a set of bins (or windows) across the genome for haplotype extraction.
    • My previous approaches were somewhat ad hoc, mostly based on read coverage in each group of data sets. I did try a number of different ways to split bigger windows into smaller ones for RRBS and SeqCap data, but none was systematic.
    • The thought here is that we want to define a bin where there is some internal methylation haplotype structure. In other words, every CpG site should be linked to other sites in the same bin. So if we use the conception of methylation LD, the pair-wise LD for all sites in a bin should be above a certain threshold. Formally, we can partition all CpG sites in the entire genome into methylation LD blocks. Each block would be a bin for MONOD. I wrote a script hapInfo2mld_blocks.pl for this purpose.
    • The next question is what data shall we use to define the bins. To be completely unbiased, we want to use WGBS data in the ideal situation. We have the N37 WGBS data from ten human tissues, plus the Heyn2013 whole blood WGBS data. All these data have been mapped. The Epigenomics Roadmap project just release a large number of WGBS data sets, but Dinh hasn't completed the mapping. These are all from non-cancerous tissues. I think we need to include some cancer data as cancer epigenome is highly screwed up and not represented by any normal tissue. So I would include our own primary tumor data generated by RRBS and SeqCap.
    • To obtain methylation haplotype blocks with hapInfo2mld_blocks.pl, we need haplotypes in the hapInfo format, which would require some sort of target definition in the first place. A reasonable set of targets to start with could be uniquely mappable and sequencible regions in the human genome. In other words, the genome is already partitioned into segments by those repetitive reads. For this purpose, I simply took the N37 WGBS data, reported the read coverage, and identified regions of at least 80bp in size with RD>=10 ( Batch processing script).
    • Partititioning methylation haplotypes and generate summary statistics ( Batch processing script).
Chromosome Total_block_size(bp) Average_block_size(bp) Number_of_blocks
chr1 7,104,372 95 74,446
chr2 6,151,111 94 65,456
chr3 4,181,003 98 42,678
chr4 3,500,168 91 38,258
chr5 4,015,791 95 42,447
chr6 4,286,579 98 43,779
chr7 4,417,448 89 49,439
chr8 3,779,783 93 40,809
chr9 3,564,824 92 38,769
chr10 4,299,677 92 46,511
chr11 4,115,304 95 43,243
chr12 3,860,323 95 40,823
chr13 2,129,337 91 23,421
chr14 2,638,407 95 27,662
chr15 2,659,525 97 27,537
chr16 3,893,757 88 44,005
chr17 4,351,313 92 47,328
chr18 1,925,501 91 21,247
chr19 4,001,607 89 44,728
chr20 2,631,521 93 28,358
chr21 1,279,904 87 14,638
chr22 2,187,548 87 25,080
Total 80,974,803 2,038 870,662
    • Identifying subsets of blocks overlapping with SeqCap (100,829) and RRBS (128,492) targets.
  /home/kunzhang/softwares/bedtools-2.17.0/bin/bedtools intersect -wa -a N37_WGBS_tumor_seqCap_RRBS_tumor_NC_all_chrs_RD10_80up.mld_blocks.bed -b ~/CpgMIP/MONOD/Data/141112_HiSeqRapidRun/OID42096_hg19_UMR_v1_capture_targets.bed > N37_WGBS_tumor_seqCap_RRBS_tumor_NC_all_chrs_RD10_80up.mld_blocks_seqCap_subset.bed
  /home/kunzhang/softwares/bedtools-2.17.0/bin/bedtools intersect -wa -a N37_WGBS_tumor_seqCap_RRBS_tumor_NC_all_chrs_RD10_80up.mld_blocks.bed -b /home/kunzhang/CpgMIP/Data/MONOD/1407-combined_RRBS/latest_organized_data/target_files/Primary_tumor_ALL.genomecov.RD50_80UP.merged.bed > N37_WGBS_tumor_seqCap_RRBS_tumor_NC_all_chrs_RD10_80up.mld_blocks_RRBS_subset.bed

Extract haplotypes into hapInfo files based on the informative bins.

  • For WGBS data, just operate on the 10-tissue pooled data for now.
  • For RRBS data, use the subsets of mld_blocks that overlap with the RRBS target.
  • For SeqCap data, use the subsets of mld_blocks that overlap with the SeqCap target.