Editing
Dinh/Dinh 2014/NOTES/2014-1-23
(section)
Jump to navigation
Jump to search
Warning:
You are not logged in. Your IP address will be publicly visible if you make any edits. If you
log in
or
create an account
, your edits will be attributed to your username, along with other benefits.
Anti-spam check. Do
not
fill this in!
=== Identify cancer reads by comparing against blood haplotype profile === * For any read, calculate the probability of seeing a particular allele at a locus in blood sample using the haplotype information for blood: * Total probability is obtained by multiplying all of the found probabilities. * 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 Total 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 total probability is high, meaning that this 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 (Blood_B1, Indx6). As a control, I sampled ~200 of another blood sample to compare (Blood_H11, Indx8). * Round 1 of <=200 random reads analyzed {| class="wikitable" cellpadding=2 | align="center" style="background:#f0f0f0;"|'''MaxScoreFilter''' | align="center" style="background:#f0f0f0;"|'''BLOOD''' | align="center" style="background:#f0f0f0;"|'''BE2C''' | align="center" style="background:#f0f0f0;"|'''U87MG''' | align="center" style="background:#f0f0f0;"|'''BXPC3''' | align="center" style="background:#f0f0f0;"|'''PANC1''' | align="center" style="background:#f0f0f0;"|'''T98G''' |- | 0||8||57||72||74||74||71 |- | 0.1||18||87||98||100||100||98 |- | 0.15||19||93||99||102||102||99 |- | 0.2||24||97||105||109||109||106 |- | Total Reads Analyzed||125||139||134||130||130||134 |} * Round 2 of <= 200 random reads analyzed {| class="wikitable" cellpadding=2 | align="center" style="background:#f0f0f0;"|'''MaxScoreFilter''' | align="center" style="background:#f0f0f0;"|'''BLOOD''' | align="center" style="background:#f0f0f0;"|'''BE2C''' | align="center" style="background:#f0f0f0;"|'''U87MG''' | align="center" style="background:#f0f0f0;"|'''BXPC3''' | align="center" style="background:#f0f0f0;"|'''PANC1''' | align="center" style="background:#f0f0f0;"|'''T98G''' |- | 0||7||53||73||65||68||64 |- | 0.1||13||75||96||93||98||89 |- | 0.15||15||78||102||94||100||94 |- | 0.2||20||83||106||96||104||98 |- | Total Reads Analyzed||123||127||136||130||129||127 |} * 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 4% of blood reads (8/200) were identified as non-blood (MaxScoreFilter=0), this means that we can only recognize cancer when cancer sample is mixed at >> 1%. * When cancer reads are mixed at 10%, cancer reads are barely detectable above noise. Blood BE2C U87MG BXPC3 PANC1 T98G %detectable 4 2.9 3.6 3.7 3.7 3.55 (Round 1) %detectable 3.5 2.7 3.7 3.3 3.4 3.2 (Round 2) blood = #reads detected /200 cancer = 0.1*(#reads detected/200) * 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.
Summary:
Please note that all contributions to ZhangLabWiki may be edited, altered, or removed by other contributors. If you do not want your writing to be edited mercilessly, then do not submit it here.
You are also promising us that you wrote this yourself, or copied it from a public domain or similar free resource (see
ZhangLabWiki:Copyrights
for details).
Do not submit copyrighted work without permission!
Cancel
Editing help
(opens in new window)
Navigation menu
Personal tools
Not logged in
Talk
Contributions
Create account
Log in
Namespaces
Page
Discussion
English
Views
Read
Edit
View history
More
Search
Navigation
Main Page
Current events
Recent changes
Random page
Investigators
Matt Cai
Song Chen
Eric Chu
Dinh Diep
Elizabeth Duong
Shicheng Guo
Alan Fung
Daniel Jacobsen
Blue Lake
Huy Lam
Alice Li
Andrew Richards
Brandon Sos
Chris Wei
Yan Wu
Kun Zhang
Tools
What links here
Related changes
Special pages
Page information