DOI: 10.18129/B9.bioc.deepSNV    

Detection of subclonal SNVs in deep sequencing data.

Bioconductor version: Release (3.6)

This package provides provides quantitative variant callers for detecting subclonal mutations in ultra-deep (>=100x coverage) sequencing experiments. The deepSNV algorithm is used for a comparative setup with a control experiment of the same loci and uses a beta-binomial model and a likelihood ratio test to discriminate sequencing errors and subclonal SNVs. The shearwater algorithm computes a Bayes classifier based on a beta-binomial model for variant calling with multiple samples for precisely estimating model parameters - such as local error rates and dispersion - and prior knowledge, e.g. from variation data bases such as COSMIC.

Author: Niko Beerenwinkel [ths], David Jones [ctb], Inigo Martincorena [ctb], Moritz Gerstung [aut, cre]

Maintainer: Moritz Gerstung <moritz.gerstung at>

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PDF R Script An R package for detecting low frequency variants in deep sequencing experiments
PDF R Script Subclonal variant calling with multiple samples and prior knowledge using shearwater
HTML R Script Shearwater ML
PDF   Reference Manual
Text   NEWS


biocViews DataImport, GeneticVariability, Genetics, SNP, Sequencing, Software
Version 1.24.0
In Bioconductor since BioC 2.10 (R-2.15) (6 years)
License GPL-3
Depends R (>= 2.13.0), methods, graphics, parallel, Rhtslib, IRanges, GenomicRanges, SummarizedExperiment, Biostrings, VGAM, VariantAnnotation(>= 1.13.44)
Imports Rhtslib
LinkingTo Rhtslib
Suggests RColorBrewer, knitr, rmarkdown
Depends On Me
Imports Me
Suggests Me GenomicFiles
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