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

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   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.
*There isn't a big difference between the two sets of target regions identified at different level of stringency.
*There isn't a big difference between the two sets of target regions identified at different level of stringency. Therefore, I checked the on-target rates for all samples using the larger target set.
{| {{table}}
| align="center" style="background:#f0f0f0;"|'''Samples'''
| align="center" style="background:#f0f0f0;"|'''All sites RD'''
| align="center" style="background:#f0f0f0;"|'''On-target RD'''
| align="center" style="background:#f0f0f0;"|'''On-target Rate'''
|-
| 6-P-10||15,965,319||20,663,932||77.3%
|-
| 6-P-1||10,145,982||13,357,081||76.0%
|-
| 6-P-2||6,896,186||9,062,234||76.1%
|-
| 6-P-3||19,268,289||21,802,969||88.4%
|-
| 6-P-4||5,974,773||7,495,311||79.7%
|-
| 6-P-5||12,590,365||16,494,290||76.3%
|-
| 6-P-6||7,512,063||12,272,372||61.2%
|-
| 6-P-7||11,161,941||15,365,391||72.6%
|-
| 6-P-8||10,857,495||14,764,214||73.5%
|-
| 6-P-9||9,301,655||11,828,425||78.6%
|-
| 6-T-1_1||42,122,819||51,404,477||81.9%
|-
| 6-T-1_2||55,332,112||68,680,670||80.6%
|-
| 6-T-2_1||39,727,871||47,770,689||83.2%
|-
| 6-T-2_2||43,299,367||53,183,818||81.4%
|-
| 6-T-3_1||36,204,067||45,527,237||79.5%
|-
| 6-T-3_2||37,657,579||46,334,499||81.3%
|-
| 6-T-4_1||48,592,836||59,200,663||82.1%
|-
| 6-T-4_2||44,582,324||52,297,088||85.2%
|-
| 6-T-5_1||42,943,331||51,084,769||84.1%
|-
| 6-T-5_2||51,597,080||62,764,249||82.2%
|-
| 7-P-10||14,120,252||19,830,640||71.2%
|-
| 7-P-1||18,418,449||22,370,674||82.3%
|-
| 7-P-2||3,066,869||4,201,304||73.0%
|-
| 7-P-3||8,092,483||11,658,106||69.4%
|-
| 7-P-4||16,586,445||20,741,132||80.0%
|-
| 7-P-5||8,001,688||10,231,049||78.2%
|-
| 7-P-6||7,444,769||9,792,239||76.0%
|-
| 7-P-7||10,815,500||13,957,997||77.5%
|-
| 7-P-8||10,431,532||14,322,889||72.8%
|-
| 7-P-9||9,590,120||13,362,917||71.8%
|-
|
|}

Revision as of 00:48, 14 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.
  • There isn't a big difference between the two sets of target regions identified at different level of stringency. Therefore, I checked the on-target rates for all samples using the larger target set.
Samples All sites RD On-target RD On-target Rate
6-P-10 15,965,319 20,663,932 77.3%
6-P-1 10,145,982 13,357,081 76.0%
6-P-2 6,896,186 9,062,234 76.1%
6-P-3 19,268,289 21,802,969 88.4%
6-P-4 5,974,773 7,495,311 79.7%
6-P-5 12,590,365 16,494,290 76.3%
6-P-6 7,512,063 12,272,372 61.2%
6-P-7 11,161,941 15,365,391 72.6%
6-P-8 10,857,495 14,764,214 73.5%
6-P-9 9,301,655 11,828,425 78.6%
6-T-1_1 42,122,819 51,404,477 81.9%
6-T-1_2 55,332,112 68,680,670 80.6%
6-T-2_1 39,727,871 47,770,689 83.2%
6-T-2_2 43,299,367 53,183,818 81.4%
6-T-3_1 36,204,067 45,527,237 79.5%
6-T-3_2 37,657,579 46,334,499 81.3%
6-T-4_1 48,592,836 59,200,663 82.1%
6-T-4_2 44,582,324 52,297,088 85.2%
6-T-5_1 42,943,331 51,084,769 84.1%
6-T-5_2 51,597,080 62,764,249 82.2%
7-P-10 14,120,252 19,830,640 71.2%
7-P-1 18,418,449 22,370,674 82.3%
7-P-2 3,066,869 4,201,304 73.0%
7-P-3 8,092,483 11,658,106 69.4%
7-P-4 16,586,445 20,741,132 80.0%
7-P-5 8,001,688 10,231,049 78.2%
7-P-6 7,444,769 9,792,239 76.0%
7-P-7 10,815,500 13,957,997 77.5%
7-P-8 10,431,532 14,322,889 72.8%
7-P-9 9,590,120 13,362,917 71.8%