- recipe bioconductor-deepsnv
Detection of subclonal SNVs in deep sequencing data.
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¶
- depends bioconductor-biostrings:
- depends bioconductor-genomicranges:
- depends bioconductor-iranges:
- depends bioconductor-rhtslib:
- depends bioconductor-summarizedexperiment:
- depends bioconductor-variantannotation:
- depends libblas:
- depends libgcc-ng:
- depends liblapack:
- depends libstdcxx-ng:
- depends r-base:
- depends r-vgam:
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
myenvnamebeing 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>``)