Package Recipe 'bioconductor-deepsnv'

recipe bioconductor-deepsnv

Detection of subclonal SNVs in deep sequencing data.

Homepage:: https://bioconductor.org/packages/3.20/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:

1.56.0-0, 1.52.0-1, 1.52.0-0, 1.48.0-0, 1.46.0-0, 1.44.0-1, 1.44.0-0, 1.40.0-2, 1.40.0-1,

1.56.0-0, 1.52.0-1, 1.52.0-0, 1.48.0-0, 1.46.0-0, 1.44.0-1, 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:

on bioconductor-biostrings >=2.78.0,<2.79.0
on bioconductor-biostrings >=2.78.0,<2.79.0a0
on bioconductor-genomicranges >=1.62.0,<1.63.0
on bioconductor-genomicranges >=1.62.1,<1.63.0a0
on bioconductor-iranges >=2.44.0,<2.45.0
on bioconductor-iranges >=2.44.0,<2.45.0a0
on bioconductor-rhtslib >=3.6.0,<3.7.0
on bioconductor-rhtslib >=3.6.0,<3.7.0a0
on bioconductor-summarizedexperiment >=1.40.0,<1.41.0
on bioconductor-summarizedexperiment >=1.40.0,<1.41.0a0
on bioconductor-variantannotation >=1.56.0,<1.57.0
on bioconductor-variantannotation >=1.56.0,<1.57.0a0
on libblas >=3.9.0,<4.0a0
on libgcc >=14
on liblapack >=3.9.0,<4.0a0
on liblzma >=5.8.2,<6.0a0
on libstdcxx >=14
on libzlib >=1.3.1,<2.0a0
on r-base >=4.5,<4.6.0a0
on r-vgam

Additional platforms:

linux-aarch64

Installation¶

You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).

Pixi¶

With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:

pixi global install bioconductor-deepsnv

to add into an existing workspace instead, run:

pixi add bioconductor-deepsnv

In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:

pixi workspace channel add conda-forge
pixi workspace channel add bioconda

Conda¶

With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:

conda install bioconductor-deepsnv

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-deepsnv

with envname being the name of the desired environment.

Container¶

Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:

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

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

Integrated deployment¶

Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.

Download stats¶

Link to this page¶

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/bioconductor-deepsnv/README.html)