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DOI: 10.18129/B9.bioc.deepSNV    

Detection of subclonal SNVs in deep sequencing data.

Bioconductor version: Release (3.5)

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 1.22.0
In Bioconductor since BioC 2.10 (R-2.15) (5.5 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
Depends On Me
Imports Me
Suggests Me GenomicFiles
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