Dinh/Dinh 2014/NOTES/2014-1-23: Difference between revisions
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| align="center" style="background:#f0f0f0;"|'''T98G''' | | align="center" style="background:#f0f0f0;"|'''T98G''' | ||
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| 0|| | | 0||2||19||19||24||24||21 | ||
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| 0.1|| | | 0.1||8||91||103||121||109||123 | ||
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| 0.15|| | | 0.15||14||108||122||135||132||146 | ||
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| 0.2|| | | 0.2||38||128||143||152||145||160 | ||
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| Total Reads Analyzed|| | | Total Reads Analyzed||159||174||171||174||174||175 | ||
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* This analysis promisingly shows that we can positively identify many of the cancer sample reads in blood and reject most reads from blood samples in blood. | * This analysis promisingly shows that we can positively identify many of the cancer sample reads in blood and reject most reads from blood samples in blood. | ||
* Since 1 | * Since up to 1% of blood reads were identified as non-blood (MaxScoreFilter=0), this means that we can only recognize cancer when cancer sample is mixed at >> 1%. | ||
* How to reject more blood reads and improve cancer detection specificity? | * How to reject more blood reads and improve cancer detection specificity? |
Revision as of 01:38, 25 January 2014
GP1 Haplotype Analysis
SAM record parser
- In order to perform haplotype analysis, I needed to write a SAM record parser so that I can analyze each read and determine the CG positions and their methylation status. This works like samtools pileup but since the current version of samtools mpileup requires a bam file as input and a reference fasta file as input, it is probably more efficient to skip using samtools mpileup.
- The SAM parser needs to identify the position of each read base, and the mismatch positions. Since there are INDELS and H/S clippings, it needs to integrate both the CIGAR and MD:Z fields.
- Script for finding mismatch positions in SAM file: File:DdSAMparse.txt
- Script for counting the CpG locus alleles (also makes BED): File:CgLocusAlleles.txt
- To compare the parsing results, use sam parser to generate a BED format file for CpG positions & compare with results obtained from samtools pileup -> methylFreq -> BED
NP-GP1V6-Ind1-16-Dec26-Ind10_S10.BED.txt : cor = 0.9999383 NP-GP1V6-Ind1-16-Dec26-Ind11_S11.BED.txt : cor = 0.9999235 NP-GP1V6-Ind1-16-Dec26-Ind12_S12.BED.txt : cor = 0.9999451 NP-GP1V6-Ind1-16-Dec26-Ind13_S13.BED.txt : cor = 0.9999285 NP-GP1V6-Ind1-16-Dec26-Ind14_S14.BED.txt : cor = 0.9998338 NP-GP1V6-Ind1-16-Dec26-Ind15_S15.BED.txt : cor = 0.9999977 NP-GP1V6-Ind1-16-Dec26-Ind16_S16.BED.txt : cor = 0.9999913 NP-GP1V6-Ind1-16-Dec26-Ind9_S9.BED.txt : cor = 0.9999657
- There are some positions where in the SAM record parsing produced slightly different results from samtools pileup.
An example of non-concordance call: samtools pileup: chr9 100610863 100610864 '9/19' 19 + 100610863 100610864 90,150,0 sam parser: chr9 100610863 100610864 '9/49' 49 + 100610863 100610864 30,210,0
- Fixed an error with extractMethyl.pl, the Regex for indels can't be used to remove them.
Regex for indels: \+[0-9]+[ACGTNacgtn]+' \-[0-9]+[ACGTNacgtn]+' Example: ...TTTT.T.T..TTT-1GTT-1GT-1GTTTTT-1GTTTTT-1GT-1GTT-1GTTTTTTTTTTTTTT..T --------------------------------------------------- *** *** *** *** *** *** *** The positions marked with dashes at the bottom were removed using the regex for indels. However, only the positions marked with stars should be removed.
- Also, samtools pileup always convert 64 to 33 (even when the base quality is already in 33).
- Re-generate BED files from extractMethyl & calculated the correlation again. They are improved slightly and even have perfect correlation for some data files.
Count locus alleles
- Count the different alleles produced by four consecutive CpGs on the same read. There are 16 different possibilities.
- Examples of the locus count:
chr1:1475064:1475086:1475088:1475095 2UMMM,1MMUM,28MMMM, 31 chr1:1475086:1475088:1475095:1475100 1MUMM,30MMMM, 31 chr1:1475095:1475100:1475108:1475130 32MMMM, 32 chr1:1475108:1475130:1475141:1475143 1MMUM,1MMMU,30MMMM, 32 chr1:1475130:1475141:1475143:1475162 1MMUM,1MMMU,29MMMM, 31 chr1:1475141:1475143:1475162:1475164 1MUMM,1MMUM,29MMMM, 31
- Generated a locus count table for each sample. The locus count table contains the haplotype information for each sample. Consider only locus that are covered by at least 10 reads.
- For any read, calculate the probability of seeing a particular allele at a locus in blood sample using the haplotype information for blood:
- Example 1:
M01186:73:000000000-A20K5:1:1101:15873:5723:1:N:0:3_AGCCTT:R chr4:172734347:172734362:172734393:172734395 UUMM 0 <--- This means that no blood sample read have this haplotype chr4:172734362:172734393:172734395:172734426 UMMM 0 chr4:172734393:172734395:172734426:172734431 MMMU 0 chr4:172734395:172734426:172734431:172734435 MMUM 0 chr4:172734426:172734431:172734435:172734442 MUMU 0 chr4:172734431:172734435:172734442:172734445 UMUM 0 chr4:172734435:172734442:172734445:172734453 MUMM 0 chr4:172734442:172734445:172734453:172734455 UMMM 0 chr4:172734445:172734453:172734455:172734459 MMMM 0 Average probability score is 0, meaning that this read have no chance of being originated from a blood sample. (This is a non-blood read).
- Example 2:
M01186:73:000000000-A20K5:1:2102:6845:17522:1:N:0:3_CCTTAG:F chr2:26407679:26407682:26407684:26407689, UUUU 0.966 chr2:26407682:26407684:26407689:26407691, UUUU 0.967 chr2:26407684:26407689:26407691:26407697, UUUU 0.975 chr2:26407689:26407691:26407697:26407713, UUUU 0.975 chr2:26407691:26407697:26407713:26407721, UUUU 0.979 chr2:26407697:26407713:26407721:26407723, UUUU 0.982 chr2:26407713:26407721:26407723:26407725, UUUU 0.980 chr2:26407721:26407723:26407725:26407728, UUUU 0.979 chr2:26407723:26407725:26407728:26407732, UUUU 0.971 chr2:26407725:26407728:26407732:26407741, UUUU 0.966 chr2:26407728:26407732:26407741:26407743, UUUU 0.968 The average probability is high, meaning that his read might have also originated from a blood sample. (This cannot be distinguished from blood).
- It is almost certain that reads with 0 average scores are not from blood DNA, but it is harder to determine whether a 0.2 or even a 0.9 average score are not from blood. We may improve the confidence by increasing the haplotype size to include more than 4 consecutive CpG sites.
- To test specificity, I randomly sampled ~200 reads from each cancer BAM files (for read 1 of V4 capture ONLY), and calculate the probability score of finding each read in a blood sample (Indx6). As a control, I sampled ~200 of another blood sample to compare (Indx8).
- Round 1 of <=200 random reads analyzed
MaxScoreFilter | BLOOD | BE2C | U87MG | BXPC3 | PANC1 | T98G |
0 | 1 | 11 | 20 | 16 | 19 | 21 |
0.1 | 6 | 83 | 105 | 105 | 115 | 129 |
0.15 | 12 | 97 | 128 | 126 | 129 | 141 |
0.2 | 39 | 123 | 150 | 143 | 141 | 152 |
Total Reads Analyzed | 169 | 174 | 180 | 176 | 173 | 178 |
- Round 2 of <= 200 random reads analyzed
MaxScoreFilter | BLOOD | BE2C | U87MG | BXPC3 | PANC1 | T98G |
0 | 2 | 19 | 19 | 24 | 24 | 21 |
0.1 | 8 | 91 | 103 | 121 | 109 | 123 |
0.15 | 14 | 108 | 122 | 135 | 132 | 146 |
0.2 | 38 | 128 | 143 | 152 | 145 | 160 |
Total Reads Analyzed | 159 | 174 | 171 | 174 | 174 | 175 |
- Cancer sample reads were identified from less than 200 total cancer reads.
- This analysis promisingly shows that we can positively identify many of the cancer sample reads in blood and reject most reads from blood samples in blood.
- Since up to 1% of blood reads were identified as non-blood (MaxScoreFilter=0), this means that we can only recognize cancer when cancer sample is mixed at >> 1%.
- How to reject more blood reads and improve cancer detection specificity?
(1) Use cancer haplotype information to reject non-cancer reads. (2) Investigate the locus on blood reads that were identified as non-blood, are they overlapping SNPs? We need to remove locus that contains CpG-SNPs. (3) Try larger haplotype sizes with 5-7 CpGs.