Kun:LabNotes/MONOD/2013-11-22: Difference between revisions

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*Check the capture efficiencies for all probes using [[Media:bam2probeEfficiency.txt|bam2probeEfficiency.pl]]: Batch processing script: [[Media:get_probeEfficiency_all_files.txt|get_probeEfficiency_all_files.sh]]; Merge all files into a matrix:  
*Check the capture efficiencies for all probes using [[Media:bam2probeEfficiency.txt|bam2probeEfficiency.pl]]: Batch processing script: [[Media:get_probeEfficiency_all_files.txt|get_probeEfficiency_all_files.sh]]; Merge all files into a matrix:  
   ./[[Media:get_probeEfficiency_matrix.txt|get_probeEfficiency_matrix.pl]] > [[Media:131227_MiSeq_V4_probeEfficiency_matrix_UMI_20Mar14.txt|131227_MiSeq_V4_probeEfficiency_matrix_UMI_20Mar14.txt]]
   ./[[Media:get_probeEfficiency_matrix.txt|get_probeEfficiency_matrix.pl]] > [[Media:131227_MiSeq_V4_probeEfficiency_matrix_UMI_20Mar14.txt|131227_MiSeq_V4_probeEfficiency_matrix_UMI_20Mar14.txt]]
Some probes seemed to have very different capture efficiencies between cancer and whole blood. This could be due to the fact that two probes were synthesized for H1/H2 that having CpG sites and that the "C" probes have higher annealing efficiencies.
*TO BE CONTINUED.
*TO BE CONTINUED.

Revision as of 05:21, 21 March 2014

MONOD round 1

Target identification

  • Data used:
    • Cancer data:
      • GBM: U87 (ENCODE RRBS Hudson Alpha; ENCODE 450k)
      • Pancreatic cancer: PANC1 (ENCODE RRBS UW & Hudson Alpha; ENCODE 450k)
    • Whole blood data: GSE30253 RRBS data; GSE31263 WGBS data
  • DMSs and DMS clusters identification:
    • I wrote a simple script that takes the average of all existing data for each CpG site, and reported the sites at which the methylation difference was greater than 0.8 between cancer and whole blood.
  ./find_DMS_MONOD_v1.pl > MONOD_v1_DMS.txt
    • These sites were then grouped into DMS clusters.
  ./extract_clusters.pl MONOD_v1_DMS.txt > MONOD_v1_DMS_clusters.txt

Probe design

BSPP capture

Data analysis

  • All 16 sequencing libraries were sequenced in a PE150bp MiSeq run: /home/kunzhang/seqStore/131227_MiSeq_GP1
  • Pre-processing of sequencing reads: the 6bp UMI in each of the Read 1 is extracted and placed in the read name, then 27bp of the 5'-ends (corresponding to H1&H2) were trimmed off from Read 1 and Read 2.
  ./extract_UMI_PE.pl
  • The reads were then mapped by bisReadMapperPE19.pl, and the bam files were used for haplotype analysis. The 400bp probe set had very poor capture, so the following analysis focus on the data generated with the 150bp probe set (GP1_V4).
library # reads # mapped reads % mapping
T98G_V4 1,803,550 1,129,385 62.6%
T98G_V6 3,151,667 489,282 15.5%
U87MG_V4 1,586,339 1,019,409 64.3%
U87MG_V6 2,858,619 422,211 14.8%
UCLA-SZ_B1_V4 1,427,484 768,088 53.8%
UCLA-SZ_B1_V6 2,174,198 381,183 17.5%
UCLA-SZ_D1_V4 1,410,018 512,719 36.4%
UCLA-SZ_D1_V6 1,126,259 143,605 12.8%
UCLA-SZ_H11_V4 1,575,000 639,234 40.6%
UCLA-SZ_H11_V6 2,166,372 269,974 12.5%
  • I wrote the bam2hapInfo.pl script to extract all haplotypes from a pair of bam files within all targeted regions.
  ../bam2hapInfo.pl /home/kunzhang/CpgMIP/MONOD/GBM_PC_CRC_V4_capture_regions_hg19.txt BE2C_V4_R1_UMI_001.fastq.sorted.fwd.bam BE2C_V4_R1_UMI_001.fastq.sorted.rev.bam >BE2C_V4_hapInfo.txt
  ../bam2hapInfo.pl /home/kunzhang/CpgMIP/MONOD/GBM_PC_CRC_V4_capture_regions_hg19.txt PANC1_V4_R1_UMI_001.fastq.sorted.fwd.bam PANC1_V4_R1_UMI_001.fastq.sorted.rev.bam >PANC1_V4_hapInfo.txt
  ../bam2hapInfo.pl /home/kunzhang/CpgMIP/MONOD/GBM_PC_CRC_V4_capture_regions_hg19.txt T98G_V4_R1_UMI_001.fastq.sorted.fwd.bam T98G_V4_R1_UMI_001.fastq.sorted.rev.bam >T98G_V4_hapInfo.txt
  ../bam2hapInfo.pl /home/kunzhang/CpgMIP/MONOD/GBM_PC_CRC_V4_capture_regions_hg19.txt U87MG_V4_R1_UMI_001.fastq.sorted.fwd.bam U87MG_V4_R1_UMI_001.fastq.sorted.rev.bam >U87MG_V4_hapInfo.txt
  ../bam2hapInfo.pl /home/kunzhang/CpgMIP/MONOD/GBM_PC_CRC_V4_capture_regions_hg19.txt UCLA-SZ_B1_V4_R1_UMI_001.fastq.sorted.fwd.bam UCLA-SZ_B1_V4_R1_UMI_001.fastq.sorted.rev.bam >UCLA-SZ_B1_V4_hapInfo.txt
  ../bam2hapInfo.pl /home/kunzhang/CpgMIP/MONOD/GBM_PC_CRC_V4_capture_regions_hg19.txt UCLA-SZ_D1_V4_R1_UMI_001.fastq.sorted.fwd.bam UCLA-SZ_D1_V4_R1_UMI_001.fastq.sorted.rev.bam >UCLA-SZ_D1_V4_hapInfo.txt
  ../bam2hapInfo.pl /home/kunzhang/CpgMIP/MONOD/GBM_PC_CRC_V4_capture_regions_hg19.txt UCLA-SZ_H11_V4_R1_UMI_001.fastq.sorted.fwd.bam UCLA-SZ_H11_V4_R1_UMI_001.fastq.sorted.rev.bam >UCLA-SZ_H11_V4_hapInfo.txt
Sample # on-target reads # unique reads Clonal rate
BE2C_V4 862,623 442,154 48.7%
BXPC3_V4 1,232,955 709,284 42.5%
PANC1_V4 763,920 268,728 64.8%
T98G_V4 1,107,621 305,041 72.5%
U87MG_V4 1,011,039 333,677 67.0%
UCLA-SZ_B1_V4 713,916 396,377 44.5%
UCLA-SZ_D1_V4 472,779 86,148 81.8%
UCLA-SZ_H11_V4 583,033 186,801 68.0%
 ./get_probeEfficiency_matrix.pl > 131227_MiSeq_V4_probeEfficiency_matrix_UMI_20Mar14.txt

Some probes seemed to have very different capture efficiencies between cancer and whole blood. This could be due to the fact that two probes were synthesized for H1/H2 that having CpG sites and that the "C" probes have higher annealing efficiencies.

  • TO BE CONTINUED.