Kun:LabNotes/MONOD/2014-8-12: Difference between revisions

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   CTT-frozen-100ng_1 50,274,814/48,129,684      55,387,277                90.8%
   CTT-frozen-100ng_1 50,274,814/48,129,684      55,387,277                90.8%
   CTT-frozen-5ng_1  14,303,236/13,695,699      15,310,475                93.4%
   CTT-frozen-5ng_1  14,303,236/13,695,699      15,310,475                93.4%
  CTT-FFPE-100ng_1  41,695,594/43,527,498      47,409,114                91.8%
   PC-P-1              6,363,821/5,972,702        7,727,128                82.3%
   PC-P-1              6,363,821/5,972,702        7,727,128                82.3%
   PC-P-10            7,607,573/7,274,238      9,433,521                80.6%
   PC-P-10            7,607,573/7,274,238      9,433,521                80.6%
   PC-T-1_1          44,275,077/42,697,725    53,030,360                83.4%
   PC-T-1_1          44,275,077/42,697,725    53,030,360                83.4%
*I believe the on-target rate is an indicator of the degree of DNA fragmentation. If a fraction of DNA are fragmented, they will be included in the sequencing libraries even without MspI digestion. The on-target rate would be lower in such cases. So plasma DNA always have lower on-target rates.
*I believe the on-target rate is an indicator of the degree of DNA fragmentation. If a fraction of DNA are fragmented, they will be included in the sequencing libraries even without MspI digestion. The on-target rate would be lower in such cases. So plasma DNA always have lower on-target rates.

Revision as of 19:34, 13 August 2014

RRBS data analysis (continued)

1. Compile a list of RRBS targets

  • Take all the RRBS data that we generated from primary tumor samples, concatenate all the methylFreq files, and generate a single BED file.
 cat 6-T-1_1.methylFreq 6-T-1_2.methylFreq 6-T-2_1.methylFreq 6-T-2_2.methylFreq 6-T-3_1.methylFreq 6-T-3_2.methylFreq 6-T-4_1.methylFreq 6-T-4_2.methylFreq 6-T-5_1.methylFreq 6-T-5_2.methylFreq 7-T-1_1.methylFreq 7-T-1_2.methylFreq 7-T-2_1.methylFreq 7-T-2_2.methylFreq 7-T-3_1.methylFreq 7-T-3_2.methylFreq 7-T-4_1.methylFreq 7-T-4_2.methylFreq 7-T-5_1.methylFreq 7-T-5_2.methylFreq CTT-FFPE-100ng_1.methylFreq CTT-FFPE-100ng_2.methylFreq CTT-FFPE-5ng_1.methylFreq CTT-FFPE-5ng_2.methylFreq CTT-frozen-100ng_1.methylFreq CTT-frozen-100ng_2.methylFreq CTT-frozen-5ng_1.methylFreq CTT-frozen-5ng_2.methylFreq PC-T-1_1.methylFreq PC-T-1_2.methylFreq PC-T-2_1.methylFreq PC-T-2_2.methylFreq PC-T-4_1.methylFreq PC-T-4_2.methylFreq PC-T-6_1.methylFreq PC-T-6_2.methylFreq PC-T-7_1.methylFreq PC-T-7_2.methylFreq |  /home/dinh/scripts/methylFreq2BED.pl  20 > /home/kunzhang/CpgMIP/Data/MONOD/MONOD_primary_tumor_RRBS_combined.BED.txt
  cat MONOD_primary_tumor_RRBS_combined.BED.txt | sort -k1,1 -k2,2n > MONOD_primary_tumor_RRBS_combined.sorted.BED.txt  
  • Group the sites covered into clusters:
  ./bed2Clusters.pl MONOD_primary_tumor_RRBS_combined.sorted.BED.txt > MONOD_primary_tumor_RRBS_targets.BED.txt
  • A total of 198,439 regions in the autosomes, with a total size of 48.8Mb.
  • I also compiled a more stringent list of target by requiring a minimal read depth of 100 for the CpG sites. This list has 119,966 autosomal regions, with a total size of 25.8Mb.
 awk ' $5>100 { print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9 }' MONOD_primary_tumor_RRBS_combined.sorted.BED.txt > MONOD_primary_tumor_RRBS_combined_RD100.sorted.BED.txt
 ./bed2Clusters.pl MONOD_primary_tumor_RRBS_combined_RD100.sorted.BED.txt > MONOD_primary_tumor_RRBS_targets_RD100.BED.txt

2. Check the total read depth for CpG sites within the RRBS targets

 cat 6-P-10.methylFreq | /home/dinh/scripts/methylFreq2BED.pl 1 |  /home/kunzhang/softwares/bedtools-2.17.0/bin/bedtools intersect -wa -a - -b /home/kunzhang/CpgMIP/Data/MONOD/MONOD_primary_tumor_RRBS_targets.BED.txt | /home/kunzhang/CpgMIP/Data/MONOD/bed_total_RD.pl
 cat 6-P-10.methylFreq | /home/dinh/scripts/methylFreq2BED.pl 1 | /home/kunzhang/CpgMIP/Data/MONOD/bed_total_RD.pl
 
                   On-target read depth     Total CpG read depth     % on-target
 6-P-10             15,965,319/14,617,979      20,663,932                77.3%
 6-P-1              10,145,982/9,347,247       13,357,081                76.0%
 6-T-1_1            42,122,819/40,519,748      51,404,477                81.9%
 6-T-2_1            39,727,871/38,177,035      47,770,689                83.2%
 CTT-frozen-100ng_1 50,274,814/48,129,684      55,387,277                90.8%
 CTT-frozen-5ng_1   14,303,236/13,695,699      15,310,475                93.4%
 CTT-FFPE-100ng_1   41,695,594/43,527,498      47,409,114                91.8%
 PC-P-1              6,363,821/5,972,702        7,727,128                82.3%
 PC-P-10             7,607,573/7,274,238       9,433,521                 80.6%
 PC-T-1_1           44,275,077/42,697,725     53,030,360                 83.4%
  • I believe the on-target rate is an indicator of the degree of DNA fragmentation. If a fraction of DNA are fragmented, they will be included in the sequencing libraries even without MspI digestion. The on-target rate would be lower in such cases. So plasma DNA always have lower on-target rates.