- recipe bioconductor-sgseq
Splice event prediction and quantification from RNA-seq data
- Homepage:
- License:
Artistic-2.0
- Recipe:
- 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:
1.34.0-0,1.32.0-0,1.28.0-0,1.26.0-0,1.24.0-1,1.24.0-0,1.22.0-0,1.20.0-0,1.18.0-1,1.34.0-0,1.32.0-0,1.28.0-0,1.26.0-0,1.24.0-1,1.24.0-0,1.22.0-0,1.20.0-0,1.18.0-1,1.16.2-0,1.14.0-0,1.12.0-0,1.10.0-0- depends bioconductor-annotationdbi:
>=1.62.0,<1.63.0- depends bioconductor-biocgenerics:
>=0.46.0,<0.47.0- depends bioconductor-biostrings:
>=2.68.0,<2.69.0- depends bioconductor-genomeinfodb:
>=1.36.0,<1.37.0- depends bioconductor-genomicalignments:
>=1.36.0,<1.37.0- depends bioconductor-genomicfeatures:
>=1.52.0,<1.53.0- depends bioconductor-genomicranges:
>=1.52.0,<1.53.0- depends bioconductor-iranges:
>=2.34.0,<2.35.0- depends bioconductor-rsamtools:
>=2.16.0,<2.17.0- depends bioconductor-rtracklayer:
>=1.60.0,<1.61.0- depends bioconductor-s4vectors:
>=0.38.0,<0.39.0- depends bioconductor-summarizedexperiment:
>=1.30.0,<1.31.0- 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
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-sgseq:<tag> (see `bioconductor-sgseq/tags`_ for valid values for ``<tag>``)
Download stats¶
Link to this page¶
Render an 
badge with the following MarkDown:
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