recipe bioconductor-deepsnv

Detection of subclonal SNVs in deep sequencing data.

Homepage:

https://bioconductor.org/packages/3.17/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

(downloads) docker_bioconductor-deepsnv

versions:
1.46.0-01.44.0-11.44.0-01.40.0-21.40.0-11.40.0-01.38.0-01.36.0-11.36.0-0

1.46.0-01.44.0-11.44.0-01.40.0-21.40.0-11.40.0-01.38.0-01.36.0-11.36.0-01.34.0-01.32.0-01.30.0-11.28.0-01.26.1-01.24.0-01.22.0-01.20.0-0

depends bioconductor-biostrings:

>=2.68.0,<2.69.0

depends bioconductor-genomicranges:

>=1.52.0,<1.53.0

depends bioconductor-iranges:

>=2.34.0,<2.35.0

depends bioconductor-rhtslib:

>=2.2.0,<2.3.0

depends bioconductor-summarizedexperiment:

>=1.30.0,<1.31.0

depends bioconductor-variantannotation:

>=1.46.0,<1.47.0

depends libblas:

>=3.9.0,<4.0a0

depends libgcc-ng:

>=12

depends liblapack:

>=3.9.0,<4.0a0

depends libstdcxx-ng:

>=12

depends r-base:

>=4.3,<4.4.0a0

depends r-vgam:

requirements:

Installation

You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-deepsnv

and update with::

   mamba update bioconductor-deepsnv

To create a new environment, run:

mamba create --name myenvname bioconductor-deepsnv

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

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

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

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