recipe bioconductor-sgseq

Splice event prediction and quantification from RNA-seq data

Homepage:

https://bioconductor.org/packages/3.18/bioc/html/SGSeq.html

License:

Artistic-2.0

Recipe:

/bioconductor-sgseq/meta.yaml

Links:

biotools: sgseq

SGSeq is a software package for analyzing splice events from RNA-seq data. Input data are RNA-seq reads mapped to a reference genome in BAM format. Genes are represented as a splice graph, which can be obtained from existing annotation or predicted from the mapped sequence reads. Splice events are identified from the graph and are quantified locally using structurally compatible reads at the start or end of each splice variant. The software includes functions for splice event prediction, quantification, visualization and interpretation.

package bioconductor-sgseq

(downloads) docker_bioconductor-sgseq

versions:
1.36.0-01.34.0-01.32.0-01.28.0-01.26.0-01.24.0-11.24.0-01.22.0-01.20.0-0

1.36.0-01.34.0-01.32.0-01.28.0-01.26.0-01.24.0-11.24.0-01.22.0-01.20.0-01.18.0-11.16.2-01.14.0-01.12.0-01.10.0-0

depends bioconductor-annotationdbi:

>=1.64.0,<1.65.0

depends bioconductor-biocgenerics:

>=0.48.0,<0.49.0

depends bioconductor-biostrings:

>=2.70.0,<2.71.0

depends bioconductor-genomeinfodb:

>=1.38.0,<1.39.0

depends bioconductor-genomicalignments:

>=1.38.0,<1.39.0

depends bioconductor-genomicfeatures:

>=1.54.0,<1.55.0

depends bioconductor-genomicranges:

>=1.54.0,<1.55.0

depends bioconductor-iranges:

>=2.36.0,<2.37.0

depends bioconductor-rsamtools:

>=2.18.0,<2.19.0

depends bioconductor-rtracklayer:

>=1.62.0,<1.63.0

depends bioconductor-s4vectors:

>=0.40.0,<0.41.0

depends bioconductor-summarizedexperiment:

>=1.32.0,<1.33.0

depends r-base:

>=4.3,<4.4.0a0

depends r-igraph:

depends r-runit:

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-sgseq

and update with::

   mamba update bioconductor-sgseq

To create a new environment, run:

mamba create --name myenvname bioconductor-sgseq

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-sgseq:<tag>

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

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