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.