deepSNV
Detection of subclonal SNVs in deep sequencing data.
Bioconductor version: 3.23 · Package version: 1.58.0
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.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("deepSNV")Details
| Maintainer | Moritz Gerstung <moritz.gerstung@ebi.ac.uk> |
| Author | Niko Beerenwinkel [ths], Raul Alcantara [ctb], David Jones [ctb], John Marshall [ctb], Inigo Martincorena [ctb], Moritz Gerstung [aut, cre] |
| License | GPL-3 |
| System Requirements | GNU make |
| Downloads rank | 587 |
| Source branch | RELEASE_3_23 |
| biocViews | DataImport, GeneticVariability, Genetics, SNP, Sequencing, Software |
Documentation
- An R package for detecting low frequency variants in deep sequencing experiments
- Shearwater ML
- Subclonal variant calling with multiple samples and prior knowledge using shearwater
Download
Dependencies
Depends: R (>= 2.13.0), methods, graphics, parallel, IRanges, GenomicRanges, SummarizedExperiment, Biostrings, VGAM, VariantAnnotation (>= 1.27.6)
Imports: Rhtslib
LinkingTo: Rhtslib (>= 1.13.1)
Suggests: RColorBrewer, knitr, rmarkdown