Kun:LabNotes/MONOD/2015-7-6

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Data analysis: re-defining bins based on methylation haplotype blocks

  • I have previously defined a set of methylation LD blocks based on the N37 and whole-blood WGBS data, plus tumor RRBS data:Kun:LabNotes/MONOD/2015-3-11.
  • Since several WGBS data sets have been published lately, I decided to repeat this using all WGBS data, in order to be as unbiased as possible.
  • The list of WGBS data sets included are (Dinh did all the mapping and gave me the mapped bam files):
    • Whole blood WGBS data (REF);
    • Our internal N37 WGBS data (ten tissues): /media/LTS_33T/WGBS_LTS33/Hg19/Noi_N37_WGBS/BAMfiles
    • WGBS data from H1 ESCs and differentiated cells (REF): /media/LTS_33T/WGBS_LTS33/Hg19/Xie2013/Re-map/BAMfiles
    • WGBS data from human adult tissues (Salk, REF): /media/TmpStore2/Dinh_Store2/Tissues_WGBS_Ecker/BAMfiles
    • WGBS data from a cancer cell line and a colon cancer tumor tissue (REF): /media/TmpStore2/Dinh_Store2/Tumor_WGBS/BAMfiles
  • I still use the same "mappable bins" to generate the initial hapInfo files for mld_block partitioning. This was done on TSCC since there are so many files to process.
    • Transfer bam files to TSCC.
    • Create a "sampleInfo" file to each data set.
    • Create job files and submit them to the queue.
 /home/k4zhang/my_oasis_tscc/MONOD/batch_bam2hapInfo2.pl  Media:H1ESC_WGBS_sampleInfo_mappable_bins.txt
 /home/k4zhang/my_oasis_tscc/MONOD/batch_bam2hapInfo2.pl  Media:Ecker-Tissue_WGBS_sampleInfo_mappable_bins.txt
 /home/k4zhang/my_oasis_tscc/MONOD/batch_bam2hapInfo2.pl  Media:N37_10_tissue_pool_WGBS_sampleInfo_mappable_bins.txt
 /home/k4zhang/my_oasis_tscc/MONOD/batch_bam2hapInfo2.pl  Media:whole_blood_WGBS_sampleInfo_mappable_bins.txt
 /home/k4zhang/my_oasis_tscc/MONOD/batch_bam2hapInfo2.pl  Media:tumor_WGBS_sampleInfo_mappable_bins.txt
    • Transfer all hapInfo files to genome-miner, merge and call mld_blocks.
 ./merge_all_WGBS_hapInfo_by_chr.pl
Chromosome Total_block_size(bp) Average_block_size(bp) Number_of_blocks
chr1 1,313,397 93 14,039
chr2 1,036,535 95 10,865
chr3 837,635 101 8,221
chr4 617,761 96 6,424
chr5 702,048 96 7,269
chr6 818,849 98 8,281
chr7 721,352 91 7,915
chr8 619,684 96 6,405
chr9 582,178 91 6,353
chr10 679,991 91 7,413
chr11 755,885 96 7,870
chr12 708,022 98 7,176
chr13 329,397 95 3,459
chr14 476,826 97 4,881
chr15 477,637 95 5,008
chr16 635,834 99 6,386
chr17 798,337 92 8,605
chr18 263,857 89 2,958
chr19 800,571 92 8,620
chr20 429,997 95 4,493
chr21 158,893 87 1,818
chr22 306,308 89 3,429
Total 14,070,994 147,888
    • Transfer the bed files to TSCC, and generate all hapInfo files for the mld_blocks.
    • Transfer all hapInfo files for the mld_blocks back to genome-miner, create the methHap_load_matrix.
      • All WGBS data: /home/kunzhang/CpgMIP/MONOD/Data/WGBS_data/mld_block_hapInfo_July2015/All_chromosomes_combined/WGBS_methHap_load_matrix_July2015.txt
      • All RRBS data:/home/kunzhang/CpgMIP/MONOD/Data/1407-combined_RRBS/mld_blocks_hapInfo_July2015/RRBS_methHap_load_matrix_July2015.txt
      • All WGBS-SeqCap data: /home/kunzhang/CpgMIP/MONOD/Data/150209_SN216/SeqCap/mld_blocks_July2015/WGBS_SeqCap_methHap_load_matrix_July2015.txt