ArticleBioinformatics (Oxford, England)2015
RVD2: an ultra-sensitive variant detection model for low-depth heterogeneous next-generation sequencing data.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- TNER: a novel background error suppression method for mutation detection in circulating tumor DNA.BMC bioinformatics · 2018Article
- Variational inference for rare variant detection in deep, heterogeneous next-generation sequencing data.BMC bioinformatics · 2017Article
- Reverse Chemical Genetics: Comprehensive Fitness Profiling Reveals the Spectrum of Drug Target Interactions.PLoS genetics · 2016Article
- Comparison of genetic variants in matched samples using thesaurus annotation.Bioinformatics (Oxford, England) · 2016Article
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Authors and funding
3 authors.
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Abstract
motivationNext-generation sequencing technology is increasingly being used for clinical diagnostic tests. Clinical samples are often genomically heterogeneous due to low sample purity or the presence of genetic subpopulations. Therefore, a variant calling algorithm for calling low-frequency polymorphisms in heterogeneous samples is needed.
resultsWe present a novel variant calling algorithm that uses a hierarchical Bayesian model to estimate allele frequency and call variants in heterogeneous samples. We show that our algorithm improves upon current classifiers and has higher sensitivity and specificity over a wide range of median read depth and minor allele fraction. We apply our model and identify 15 mutated loci in the PAXP1 gene in a matched clinical breast ductal carcinoma tumor sample; two of which are likely loss-of-heterozygosity events. AVAILABILITY AND IMPLEMENTATION: http://genomics.wpi.edu/rvd2/. CONTACT: pjflaherty@wpi.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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