recipe bioconductor-deepsnv

Detection of subclonal SNVs in deep sequencing data.

Homepage:

https://bioconductor.org/packages/3.16/bioc/html/deepSNV.html

License:

GPL-3

Recipe:

/bioconductor-deepsnv/meta.yaml

Links:

biotools: deepsnv

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.

package bioconductor-deepsnv

Versions:

1.44.0-0,  1.40.0-2,  1.40.0-1,  1.40.0-0,  1.38.0-0,  1.36.0-1,  1.36.0-0,  1.34.0-0,  1.32.0-0, 

1.44.0-0,  1.40.0-2,  1.40.0-1,  1.40.0-0,  1.38.0-0,  1.36.0-1,  1.36.0-0,  1.34.0-0,  1.32.0-0,  1.30.0-1,  1.28.0-0,  1.26.1-0,  1.24.0-0,  1.22.0-0,  1.20.0-0

Depends:

• bioconductor-biostrings >=2.66.0,<2.67.0

• bioconductor-genomicranges >=1.50.0,<1.51.0

• bioconductor-iranges >=2.32.0,<2.33.0

• bioconductor-rhtslib >=2.0.0,<2.1.0

• bioconductor-summarizedexperiment >=1.28.0,<1.29.0

• bioconductor-variantannotation >=1.44.0,<1.45.0

• libblas >=3.9.0,<4.0a0

• libgcc-ng >=12

• liblapack >=3.9.0,<4.0a0

• libstdcxx-ng >=12

• r-base >=4.2,<4.3.0a0

• r-vgam

Required By:

• bioconductor-mitoclone2

Installation

With an activated Bioconda channel (see set-up-channels), install with:

conda install bioconductor-deepsnv

and update with:

conda update bioconductor-deepsnv

or use the docker container:

docker pull quay.io/biocontainers/bioconductor-deepsnv:<tag>

(see bioconductor-deepsnv/tags for valid values for <tag>)

Download stats

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