Package Recipe 'bioconductor-sgseq'

recipe bioconductor-sgseq

Splice event prediction and quantification from RNA-seq data

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

Versions:

1.44.0-0, 1.40.0-0, 1.36.0-0, 1.34.0-0, 1.32.0-0, 1.28.0-0, 1.26.0-0, 1.24.0-1, 1.24.0-0,

1.44.0-0, 1.40.0-0, 1.36.0-0, 1.34.0-0, 1.32.0-0, 1.28.0-0, 1.26.0-0, 1.24.0-1, 1.24.0-0, 1.22.0-0, 1.20.0-0, 1.18.0-1, 1.16.2-0, 1.14.0-0, 1.12.0-0, 1.10.0-0

Depends:

on bioconductor-annotationdbi >=1.72.0,<1.73.0
on bioconductor-biocgenerics >=0.56.0,<0.57.0
on bioconductor-biostrings >=2.78.0,<2.79.0
on bioconductor-genomeinfodb >=1.46.0,<1.47.0
on bioconductor-genomicalignments >=1.46.0,<1.47.0
on bioconductor-genomicfeatures >=1.62.0,<1.63.0
on bioconductor-genomicranges >=1.62.0,<1.63.0
on bioconductor-iranges >=2.44.0,<2.45.0
on bioconductor-rsamtools >=2.26.0,<2.27.0
on bioconductor-rtracklayer >=1.70.0,<1.71.0
on bioconductor-s4vectors >=0.48.0,<0.49.0
on bioconductor-seqinfo >=1.0.0,<1.1.0
on bioconductor-summarizedexperiment >=1.40.0,<1.41.0
on r-base >=4.5,<4.6.0a0
on r-igraph
on r-runit

Additional platforms:

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-sgseq

to add into an existing workspace instead, run:

pixi add bioconductor-sgseq

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-sgseq

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-sgseq

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-sgseq:<tag>

(see bioconductor-sgseq/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-sgseq/README.html)