Kun:LabNotes/MONOD/2014-8-12: Difference between revisions
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./get_all_PC-P_LOD.pl > PC-P_LOD_out.txt & | ./get_all_PC-P_LOD.pl > PC-P_LOD_out.txt & | ||
[[Image:Colon_cancer_UMR_tumor_haplotype_load.png|350px]][[Image:Lung_cancer_UMR_tumor_haplotype_load.png|350px]][[Image:Pancreatic_cancer_UMR_tumor_haplotype_load.png|350px]] | [[Image:Colon_cancer_UMR_tumor_haplotype_load.png|350px]][[Image:Lung_cancer_UMR_tumor_haplotype_load.png|350px]][[Image:Pancreatic_cancer_UMR_tumor_haplotype_load.png|350px]] | ||
==5. Searching for primary tumor specific haplotypes== | |||
*In the analysis above, I used the pooled normal plasma and pooled primary tumor data as the negative and positive controls. For each haplotype from a patient's plasma, I determined the likelihood of it being present in normal plasma versus primary tumor, then calculated the tumor haplotype load. | |||
*The other approach, assuming the matched primary tumor is available, is to look for the haplotypes in the patient's plasma that exactly matched the ones seen in primary tumor and not present in normal plasma. This could be more specific as we make fewer assumption on what haplotype should look like tumor haplotypes. |
Revision as of 17:07, 29 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% |
7-T-1_1 | 55,494,618 | 68,629,065 | 80.9% |
7-T-1_2 | 42,296,152 | 51,527,101 | 82.1% |
7-T-2_1 | 42,767,775 | 52,191,210 | 81.9% |
7-T-2_2 | 45,814,173 | 57,146,402 | 80.2% |
7-T-3_1 | 43,288,619 | 53,666,959 | 80.7% |
7-T-3_2 | 36,712,682 | 44,754,376 | 82.0% |
7-T-4_1 | 33,329,767 | 45,116,118 | 73.9% |
7-T-4_2 | 33,219,393 | 41,142,529 | 80.7% |
7-T-5_1 | 57,347,061 | 69,371,414 | 82.7% |
7-T-5_2 | 44,318,977 | 54,673,022 | 81.1% |
CTT-FFPE-100ng_1 | 43,527,498 | 47,409,114 | 91.8% |
CTT-FFPE-100ng_2 | 42,796,717 | 46,523,502 | 92.0% |
CTT-FFPE-5ng_1 | 17,255,895 | 18,600,287 | 92.8% |
CTT-FFPE-5ng_2 | 33,184,997 | 36,355,026 | 91.3% |
CTT-frozen-100ng_1 | 50,274,814 | 55,387,277 | 90.8% |
CTT-frozen-100ng_2 | 55,941,976 | 60,903,449 | 91.9% |
CTT-frozen-5ng_1 | 14,303,236 | 15,310,475 | 93.4% |
CTT-frozen-5ng_2 | 31,206,451 | 34,203,612 | 91.2% |
NC-P-1 | 22,265,516 | 25,002,054 | 89.1% |
NC-P-2 | 16,284,869 | 19,552,139 | 83.3% |
NC-P-3 | 27,927,900 | 31,201,357 | 89.5% |
NC-P-5 | 9,532,973 | 12,424,591 | 76.7% |
NC-P-6 | 13,102,631 | 15,423,838 | 85.0% |
NC-P-7 | 12,679,131 | 15,679,757 | 80.9% |
NC-P-8 | 13,414,256 | 16,259,364 | 82.5% |
NC-P-9 | 13,445,816 | 15,379,652 | 87.4% |
PC-P-10 | 7,607,573 | 9,433,521 | 80.6% |
PC-P-1 | 6,363,821 | 7,727,128 | 82.4% |
PC-P-2 | 13,346,408 | 15,069,125 | 88.6% |
PC-P-3 | 3,151,205 | 4,093,467 | 77.0% |
PC-P-4 | 6,488,210 | 8,658,320 | 74.9% |
PC-P-5 | 5,347,960 | 6,722,768 | 79.5% |
PC-P-6 | 6,788,449 | 8,497,332 | 79.9% |
PC-P-7 | 6,294,024 | 8,269,872 | 76.1% |
PC-P-8 | 4,804,772 | 5,698,389 | 84.3% |
PC-P-9 | 3,845,849 | 4,390,032 | 87.6% |
PC-T-1_1 | 44,275,077 | 53,030,360 | 83.5% |
PC-T-1_2 | 51,315,205 | 59,257,668 | 86.6% |
PC-T-2_1 | 68,245,319 | 80,836,193 | 84.4% |
PC-T-2_2 | 59,886,740 | 67,965,891 | 88.1% |
PC-T-4_1 | 56,814,606 | 65,283,928 | 87.0% |
PC-T-4_2 | 53,207,679 | 61,130,443 | 87.0% |
PC-T-6_1 | 65,535,581 | 77,482,439 | 84.6% |
PC-T-6_2 | 53,192,172 | 63,933,657 | 83.2% |
PC-T-7_1 | 48,504,470 | 56,835,764 | 85.3% |
PC-T-7_2 | 46,509,004 | 55,296,800 | 84.1% |
3. Haplotype analysis in the UMR regions
- The previous analysis that I did was focusing on the LMS clusters, which is a small subset of UMRs. Perhaps the target selection was too strict and I might have missed other informative regions. Therefore, I decided to expand the net and search more broadly.
- I wrote a script to report all haplotypes in a list of target from a RRBS bam file. I then obtained all haplotypes for all samples within the 49Mb RRBS target regions.
/home/kunzhang/CpgMIP/Data/MONOD/mergedBam2hapInfo.pl /home/kunzhang/CpgMIP/Data/MONOD/MONOD_primary_tumor_RRBS_targets.BED.txt /media/Ext12T/DD_Ext12T/RRBS_MONOD/Bam_Merged/6-P-10.merged.bam >6-P-10.hapInfo.txt & Batch processing script: 1407-combined_expanded_step1_batch_command.sh
- Then I attempted to classify each haplotype as blood unmethylated haplotype or a cancer methylated haplotype, based on the methylation level of the CpG sites within UMR in the healthy plasma.
- First I merged all the NC plasma methylFreq files, and created a BED file that represent the average methylation for all the NC plasma samples. Then I use bedtools intersect to extract the sites within blood UMRs.
- Second I wrote a script to report the following three numbers for each target region: (i) the number of haplotypes; (ii) the number of methylated haplotypes; (iii) the sum negative log-p of all haplotypes. All these numbers for one sample are reported in one regionHap.txt file.
- Finally I wrote another script to combine the regionHap.txt files for multiple samples, and generate two matrix: HMH_load (the fraction of methylated haplotypes, a measurement of cancer load); HMH_NLP (the sum negative log-p, an indicator for significance)
/home/kunzhang/CpgMIP/Data/MONOD/regionMethHapAnalysis.pl /home/kunzhang/CpgMIP/Data/MONOD/MONOD_nc_plasma_RRBS_combined_UMRs.BED.txt 6-P-10.hapInfo.txt>6-P-10.regionHap.txt Batch processing script: 1407-combined_expanded_step2_batch_command.sh ../../report_regionHap_matrix.pl UCSD_MCC_PancreasCancer PC-P-10.regionHap.txt PC-P-1.regionHap.txt PC-P-2.regionHap.txt PC-P-3.regionHap.txt PC-P-4.regionHap.txt PC-P-5.regionHap.txt PC-P-6.regionHap.txt PC-P-7.regionHap.txt PC-P-8.regionHap.txt PC-P-9.regionHap.txt PC-T-1_1.regionHap.txt PC-T-1_2.regionHap.txt PC-T-2_1.regionHap.txt PC-T-2_2.regionHap.txt PC-T-4_1.regionHap.txt PC-T-4_2.regionHap.txt PC-T-6_1.regionHap.txt PC-T-6_2.regionHap.txt PC-T-7_1.regionHap.txt PC-T-7_2.regionHap.txt ../../report_regionHap_matrix.pl UCSD_MCC_LungCancer 7-P-10.regionHap.txt 7-P-1.regionHap.txt 7-P-2.regionHap.txt 7-P-3.regionHap.txt 7-P-4.regionHap.txt 7-P-5.regionHap.txt 7-P-6.regionHap.txt 7-P-7.regionHap.txt 7-P-8.regionHap.txt 7-P-9.regionHap.txt 7-T-1_1.regionHap.txt 7-T-1_2.regionHap.txt 7-T-2_1.regionHap.txt 7-T-2_2.regionHap.txt 7-T-3_1.regionHap.txt 7-T-3_2.regionHap.txt 7-T-4_1.regionHap.txt 7-T-4_2.regionHap.txt 7-T-5_1.regionHap.txt 7-T-5_2.regionHap.txt ../../report_regionHap_matrix.pl UCSD_MCC_ColonCancer 6-P-10.regionHap.txt 6-P-1.regionHap.txt 6-P-2.regionHap.txt 6-P-3.regionHap.txt 6-P-4.regionHap.txt 6-P-5.regionHap.txt 6-P-6.regionHap.txt 6-P-7.regionHap.txt 6-P-8.regionHap.txt 6-P-9.regionHap.txt 6-T-1_1.regionHap.txt 6-T-1_2.regionHap.txt 6-T-2_1.regionHap.txt 6-T-2_2.regionHap.txt 6-T-3_1.regionHap.txt 6-T-3_2.regionHap.txt 6-T-4_1.regionHap.txt 6-T-4_2.regionHap.txt 6-T-5_1.regionHap.txt 6-T-5_2.regionHap.txt ../../report_regionHap_matrix.pl Plasma 6-P-10.regionHap.txt 6-P-1.regionHap.txt 6-P-2.regionHap.txt 6-P-3.regionHap.txt 6-P-4.regionHap.txt 6-P-5.regionHap.txt 6-P-6.regionHap.txt 6-P-7.regionHap.txt 6-P-8.regionHap.txt 6-P-9.regionHap.txt 7-P-10.regionHap.txt 7-P-1.regionHap.txt 7-P-2.regionHap.txt 7-P-3.regionHap.txt 7-P-4.regionHap.txt 7-P-5.regionHap.txt 7-P-6.regionHap.txt 7-P-7.regionHap.txt 7-P-8.regionHap.txt 7-P-9.regionHap.txt NC-P-1.regionHap.txt NC-P-2.regionHap.txt NC-P-3.regionHap.txt NC-P-5.regionHap.txt NC-P-6.regionHap.txt NC-P-7.regionHap.txt NC-P-8.regionHap.txt NC-P-9.regionHap.txt PC-P-10.regionHap.txt PC-P-1.regionHap.txt PC-P-2.regionHap.txt PC-P-3.regionHap.txt PC-P-4.regionHap.txt PC-P-5.regionHap.txt PC-P-6.regionHap.txt PC-P-7.regionHap.txt PC-P-8.regionHap.txt PC-P-9.regionHap.txt ../../report_regionHap_matrix.pl Primary_tumor 6-T-1_1.regionHap.txt 6-T-1_2.regionHap.txt 6-T-2_1.regionHap.txt 6-T-2_2.regionHap.txt 6-T-3_1.regionHap.txt 6-T-3_2.regionHap.txt 6-T-4_1.regionHap.txt 6-T-4_2.regionHap.txt 6-T-5_1.regionHap.txt 6-T-5_2.regionHap.txt 7-T-1_1.regionHap.txt 7-T-1_2.regionHap.txt 7-T-2_1.regionHap.txt 7-T-2_2.regionHap.txt 7-T-3_1.regionHap.txt 7-T-3_2.regionHap.txt 7-T-4_1.regionHap.txt 7-T-4_2.regionHap.txt 7-T-5_1.regionHap.txt 7-T-5_2.regionHap.txt PC-T-1_1.regionHap.txt PC-T-1_2.regionHap.txt PC-T-2_1.regionHap.txt PC-T-2_2.regionHap.txt PC-T-4_1.regionHap.txt PC-T-4_2.regionHap.txt PC-T-6_1.regionHap.txt PC-T-6_2.regionHap.txt PC-T-7_1.regionHap.txt PC-T-7_2.regionHap.txt CTT-FFPE-100ng_1.regionHap.txt CTT-FFPE-100ng_2.regionHap.txt CTT-FFPE-5ng_1.regionHap.txt CTT-FFPE-5ng_2.regionHap.txt CTT-frozen-100ng_1.regionHap.txt CTT-frozen-100ng_2.regionHap.txt CTT-frozen-5ng_1.regionHap.txt CTT-frozen-5ng_2.regionHap.txt
- To take a quick look at the HMH load in the primary tumors, I took the Primary_tumor.HMH_load.matrix file, manually filtered out the regions that have average load below 0.1 (just to reduce the number of regions from 20k+ to ~5k. Then made a heatmap with R (note that I removed the Biochain CTT samples to avoid variability of sample source). Based on the heatmap, there seems to be a decent level of heterogeneity even for the samples of the same tumor types.
library(gplots) x=read.table("MCC_Primary_tumor_mean_0.1_up.HMH_load.matrix.txt",header=TRUE,row.names=1); heatmap.2(as.matrix(x), col=bluered(128), scale="none", cexCol=0.8, cexRow=0.1 ,key=T, symkey=F,density.info="histogram",trace="none",dendrogram="both") File:MCC primary tumor mean 0.1 up heatmap.png
4. Mix hap analysis
- It looks like simple classification of a haplotype into blood or tumor based on the number of methylated CpG is a little bit crude, and it's hard to come up with a robust strategy for detecting tumor haplotypes in patient's plasma.
- I decided to go back to the mix hap analysis and make a number of adjustments.
- My previous version of mix hap analysis assumes BSPP data, in which most or all reads overlap perfectly. With RRBS data, many reads in a target region don't overlap. So I needed to be more flexible in comparing haplotypes.
- The average read depth is lower in RRBS data. For CpG sites that have low or no coverage in normal plasma, I need to use the methylation level of whole blood WGBS data to estimate the haplotype frequency.
- I decided to switch to LOD (log of odds ratio) between normal plasma and tumor. Essentially, I created two reference hapInfo file, one for all normal plasma (NC-P) samples combined, another for all primary tumor samples of one cancer type (PC, CC or LC) combined. For each sequence reads in the patient plasma, I asked what's the probability that the haplotype came from blood and the probability it came from tumor. Then I took the log of the odds ratio between these two probabilities, and sum them up for each target region and for the entire genome.
- I started with all RRBS targets within the UMRs, but then need to trim the target list, based on which target is informative. Since the sample size is limited, doing cross validation is not realistic. So I simply pooled all patient plasma data (treating PC, CC, LC separately), and ran mixMethHapAnalysis_19Aug14.pl against pooled normal plasma and pooled tumor to identify the regions that have the highest LOD scores.
../../mixMethHapAnalysis_19Aug14.pl NC-P-ALL.hapInfo.txt 6-T-ALL.hapInfo.txt 6-P-ALL.hapInfo.txt /home/kunzhang/CpgMIP/MONOD/Public_data/WB_middle_age_UMR.BED.txt | sort -k 2,2nr > RRBS_targets_LOD_CC.txt & ../../mixMethHapAnalysis_19Aug14.pl NC-P-ALL.hapInfo.txt 6-T-ALL.hapInfo.txt 6-P-ALL.hapInfo.txt /home/kunzhang/CpgMIP/MONOD/Public_data/WB_middle_age_UMR.BED.txt | sort -k 2,2nr > RRBS_targets_LOD_CC.txt & ../../mixMethHapAnalysis_19Aug14.pl NC-P-ALL.hapInfo.txt 6-T-ALL.hapInfo.txt 6-P-ALL.hapInfo.txt /home/kunzhang/CpgMIP/MONOD/Public_data/WB_middle_age_UMR.BED.txt | sort -k 2,2nr > RRBS_targets_LOD_CC.txt &
- To evaluate the level of false positives due to variability in normal plasma, I took one normal plasma as a test sample to compare with the pool of the other seven normal plasma. I did this for each of the 8 plasma samples, and reported a list of regions that show high LOD (false positives). These regions were excluded from the list of informative markers. It appeared that whether there are false positive calls in a region also depends on which tumor reference file is used. For now, I just identified these regions for each of the three tumor types separately. In the future, we might need to process a lot of normal plasma and primary tumor samples to fully cover the biological variability.
./find_NC-P_high-LOD_targets.pl > NC-P_high-LOD_targets.txt
- I manually split the NC-P_high-LOD_targets.txt file into three based on the tumor types, then extracted the informative marker lists based on the differences of LOD scores between patient plasma and normal plasma.
./filter_targets.pl RRBS_targets_LOD_PC.txt NC-P_high-PC-LOD_targets.txt | sort -k 2,2nr > RRBS_targets_LOD_PC_top_347.txt ./filter_targets.pl RRBS_targets_LOD_LC.txt NC-P_high-LC-LOD_targets.txt | sort -k 2,2nr > RRBS_targets_LOD_LC_top_346.txt ./filter_targets.pl RRBS_targets_LOD_LC.txt NC-P_high-CC-LOD_targets.txt | sort -k 2,2nr > RRBS_targets_LOD_CC_top_302.txt
- Then I searched for the tumor haplotypes in each plasma samples within the marker regions.
./get_all_CC-P_LOD.pl > CC-P_LOD_out.txt & ./get_all_LC-P_LOD.pl > LC-P_LOD_out.txt & ./get_all_PC-P_LOD.pl > PC-P_LOD_out.txt & File:Colon cancer UMR tumor haplotype load.pngFile:Lung cancer UMR tumor haplotype load.pngFile:Pancreatic cancer UMR tumor haplotype load.png
5. Searching for primary tumor specific haplotypes
- In the analysis above, I used the pooled normal plasma and pooled primary tumor data as the negative and positive controls. For each haplotype from a patient's plasma, I determined the likelihood of it being present in normal plasma versus primary tumor, then calculated the tumor haplotype load.
- The other approach, assuming the matched primary tumor is available, is to look for the haplotypes in the patient's plasma that exactly matched the ones seen in primary tumor and not present in normal plasma. This could be more specific as we make fewer assumption on what haplotype should look like tumor haplotypes.