Kun:LabNotes/MONOD/2015-7-6
Jump to navigation
Jump to search
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