scholarly journals An empirical Bayesian framework for somatic mutation detection from cancer genome sequencing data

2013 ◽  
Vol 41 (7) ◽  
pp. e89-e89 ◽  
Author(s):  
Yuichi Shiraishi ◽  
Yusuke Sato ◽  
Kenichi Chiba ◽  
Yusuke Okuno ◽  
Yasunobu Nagata ◽  
...  
Keyword(s):  
Genome Sequencing ◽  
Somatic Mutation ◽  
Mutation Detection ◽  
Bayesian Framework ◽  
Cancer Genome ◽  
Sequencing Data ◽  
Empirical Bayesian ◽  
Cancer Genome Sequencing ◽  
Somatic Mutation Detection

scholarly journals FaSD-somatic: a fast and accurate somatic SNV detection algorithm for cancer genome sequencing data

2014 ◽  
Vol 30 (17) ◽  
pp. 2498-2500 ◽  
Author(s):  
Weixin Wang ◽  
Panwen Wang ◽  
Feng Xu ◽  
Ruibang Luo ◽  
Maria Pik Wong ◽  
...  
Keyword(s):  
Genome Sequencing ◽  
Detection Algorithm ◽  
Cancer Genome ◽  
Sequencing Data ◽  
Cancer Genome Sequencing

scholarly journals Genomon ITDetector: a tool for somatic internal tandem duplication detection from cancer genome sequencing data

2014 ◽  
Vol 31 (1) ◽  
pp. 116-118 ◽  
Author(s):  
Kenichi Chiba ◽  
Yuichi Shiraishi ◽  
Yasunobu Nagata ◽  
Kenichi Yoshida ◽  
Seiya Imoto ◽  
...  
Keyword(s):  
Genome Sequencing ◽  
Tandem Duplication ◽  
Cancer Genome ◽  
Sequencing Data ◽  
Internal Tandem Duplication ◽  
Cancer Genome Sequencing ◽  
Duplication Detection

scholarly journals Detecting somatic point mutations in cancer genome sequencing data: a comparison of mutation callers

10.1186/gm495 ◽  
2013 ◽  
Vol 5 (10) ◽  
pp. 91 ◽  
Author(s):  
Qingguo Wang ◽  
Peilin Jia ◽  
Fei Li ◽  
Haiquan Chen ◽  
Hongbin Ji ◽  
...  
Keyword(s):  
Genome Sequencing ◽  
Point Mutations ◽  
Cancer Genome ◽  
Sequencing Data ◽  
Cancer Genome Sequencing

scholarly journals Unique K-mer sequences for validating cancer-related substitution, insertion and deletion mutations

2020 ◽  
Author(s):  
HoJoon Lee ◽  
Ahmed Shuaibi ◽  
John M. Bell ◽  
Dmitri S. Pavlichin ◽  
Hanlee P. Ji
Keyword(s):  
Genome Sequencing ◽  
Somatic Mutations ◽  
Variant Calling ◽  
Cancer Genome ◽  
Sequencing Data ◽  
Deletion Mutations ◽  
Insertion And Deletion ◽  
Cancer Genome Sequencing ◽  
Significant Difference ◽  
Model Tuning

ABSTRACTThe cancer genome sequencing has led to important discoveries such as identifying cancer gene. However, challenges remain in the analysis of cancer genome sequencing. One significant issue is that mutations identified by multiple variant callers are frequently discordant even when using the same genome sequencing data. For insertion and deletion mutations, oftentimes there is no agreement among different callers. Identifying somatic mutations involves read mapping and variant calling, a complicated process that uses many parameters and model tuning. To validate the identification of true mutations, we developed a method using k-mer sequences. First, we characterized the landscape of unique versus non-unique k-mers in the human genome. Second, we developed a software package, KmerVC, to validate the given somatic mutations from sequencing data. Our program validates the occurrence of a mutation based on statistically significant difference in frequency of k-mers with and without a mutation from matched normal and tumor sequences. Third, we tested our method on both simulated and cancer genome sequencing data. Counting k-mer involving mutations effectively validated true positive mutations including insertions and deletions across different individual samples in a reproducible manner. Thus, we demonstrated a straightforward approach for rapidly validating mutations from cancer genome sequencing data.

VHVT: An ultra-sensitive somatic mutation detection and performance assessment program.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 1588-1588 ◽  
Author(s):  
Jilong Liu ◽  
Zu Liu ◽  
Shaomin Cheng ◽  
Fengming Guo ◽  
Meihua Tan ◽  
...  
Keyword(s):  
Somatic Mutation ◽  
Mutation Detection ◽  
Low Frequency ◽  
Allelic Frequency ◽  
Driver Mutations ◽  
Clinical Samples ◽  
Sequencing Error ◽  
Sequencing Data ◽  
Standard Data ◽  
Somatic Mutation Detection

1588 Background: NGS as a high throughput technique is particular valuable for cancer given its ability to detect multiple driver mutations. While reads contain SNVs and short InDels can be mapped to the right position using gatk-like programs, a program designed for germline mutation detection, reads contain long InDels such as EGFR EX19 deletions often wrongly mapped especially when deletions near the ends of the reads. Thus, gatk would not recognize these reads, consequently underestimate the mutation allelic frequency, and even missed out InDels when supporting reads were rare. Methods: Here we present a variation hotspot validation toolkit (VHVT), a validation based method to precisely detect the ultra-low frequency somatic mutations. As far as we know, it is the first specialized somatic mutation detection software. First, reference sequences aimed at the hotspot mutations were assembled, then reads were be mapped to the new assembled reference to precisely distinguish the supporting reads. Moreover, log odds (LOD) and Poisson mathematical model were integrated to control sequencing error, as a result, VHVT can achieve a limitation of detection at 0.01% with sensitivity and specificity above 95% and 99% respectively. In addition, we developed a method to quantitatively assess the performance of variation detection program using standard reference data. By mapping to the reconstructed reference, all supporting reads will be detected in sequencing data, and comparing theses with the number of supporting reads delivered by a program we can define recognition ratio of supporting reads. Results: Our reference standard data showed that VHVT can recognize average 30% more support reads than gatk for EGFR EX19 deletions. In a total 498 NSCLC clinical samples test, VHVT detected actionable mutations in 289 samples. 243 positive mutations were verified (168 by SANGER sequencing, 75 by ddPCR) with concordance rate at 100%. Conclusions: Taken all together, our results demonstrated the robust performance of VHVT for somatic mutation detection and program assessment and thus facilitate the development of personalized cancer therapy.

scholarly journals A comprehensive assessment of somatic mutation detection in cancer using whole-genome sequencing

2015 ◽  
Vol 6 (1) ◽  
Author(s):  
Tyler S. Alioto ◽  
Ivo Buchhalter ◽  
Sophia Derdak ◽  
Barbara Hutter ◽  
Matthew D. Eldridge ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Somatic Mutation ◽  
Mutation Detection ◽  
Comprehensive Assessment ◽  
Whole Genome ◽  
Somatic Mutation Detection

scholarly journals Statistically identifying tumor suppressors and oncogenes from pan-cancer genome-sequencing data

2015 ◽  
pp. btv430 ◽  
Author(s):  
Runjun D. Kumar ◽  
Adam C. Searleman ◽  
S. Joshua Swamidass ◽  
Obi L. Griffith ◽  
Ron Bose
Keyword(s):  
Genome Sequencing ◽  
Tumor Suppressors ◽  
Cancer Genome ◽  
Sequencing Data ◽  
Cancer Genome Sequencing ◽  
Pan Cancer

scholarly journals Feature-based classifiers for somatic mutation detection in tumour–normal paired sequencing data

2011 ◽  
Vol 28 (2) ◽  
pp. 167-175 ◽  
Author(s):  
Jiarui Ding ◽  
Ali Bashashati ◽  
Andrew Roth ◽  
Arusha Oloumi ◽  
Kane Tse ◽  
...  
Keyword(s):  
Somatic Mutation ◽  
Mutation Detection ◽  
Sequencing Data ◽  
Feature Based ◽  
Somatic Mutation Detection
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