Package Recipe 'bioconductor-fraser'

recipe bioconductor-fraser

Find RAre Splicing Events in RNA-Seq Data

Homepage:: https://bioconductor.org/packages/3.20/bioc/html/FRASER.html
License:: MIT + file LICENSE
Recipe:: /bioconductor-fraser/meta.yaml
Links:: https: :https:`//doi.org/10.1038/s41467-020-20573-7`

Detection of rare aberrant splicing events in transcriptome profiles. Read count ratio expectations are modeled by an autoencoder to control for confounding factors in the data. Given these expectations, the ratios are assumed to follow a beta-binomial distribution with a junction specific dispersion. Outlier events are then identified as read-count ratios that deviate significantly from this distribution. FRASER is able to detect alternative splicing, but also intron retention. The package aims to support diagnostics in the field of rare diseases where RNA-seq is performed to identify aberrant splicing defects.

package bioconductor-fraser¶

Versions:

2.6.0-0, 2.4.6-0, 2.2.0-0, 1.99.4-0, 1.99.3-0, 1.14.0-0, 1.12.1-0, 1.10.0-1, 1.10.0-0,

2.6.0-0, 2.4.6-0, 2.2.0-0, 1.99.4-0, 1.99.3-0, 1.14.0-0, 1.12.1-0, 1.10.0-1, 1.10.0-0, 1.6.1-2, 1.6.1-1, 1.6.1-0, 1.6.0-1, 1.6.0-0, 1.4.0-0, 1.2.1-1, 1.2.1-0, 1.2.0-0, 1.0.1-0, 1.0.0-0

Depends:

on bioconductor-annotationdbi >=1.72.0,<1.73.0
on bioconductor-annotationdbi >=1.72.0,<1.73.0a0
on bioconductor-biobase >=2.70.0,<2.71.0
on bioconductor-biobase >=2.70.0,<2.71.0a0
on bioconductor-biocgenerics >=0.56.0,<0.57.0
on bioconductor-biocgenerics >=0.56.0,<0.57.0a0
on bioconductor-biocparallel >=1.44.0,<1.45.0
on bioconductor-biocparallel >=1.44.0,<1.45.0a0
on bioconductor-biomart >=2.66.0,<2.67.0
on bioconductor-biomart >=2.66.0,<2.67.0a0
on bioconductor-bsgenome >=1.78.0,<1.79.0
on bioconductor-bsgenome >=1.78.0,<1.79.0a0
on bioconductor-delayedarray >=0.36.0,<0.37.0
on bioconductor-delayedarray >=0.36.0,<0.37.0a0
on bioconductor-delayedmatrixstats >=1.32.0,<1.33.0
on bioconductor-delayedmatrixstats >=1.32.0,<1.33.0a0
on bioconductor-genomeinfodb >=1.46.0,<1.47.0
on bioconductor-genomeinfodb >=1.46.2,<1.47.0a0
on bioconductor-genomicalignments >=1.46.0,<1.47.0
on bioconductor-genomicalignments >=1.46.0,<1.47.0a0
on bioconductor-genomicfeatures >=1.62.0,<1.63.0
on bioconductor-genomicfeatures >=1.62.0,<1.63.0a0
on bioconductor-genomicranges >=1.62.0,<1.63.0
on bioconductor-genomicranges >=1.62.1,<1.63.0a0
on bioconductor-hdf5array >=1.38.0,<1.39.0
on bioconductor-hdf5array >=1.38.0,<1.39.0a0
on bioconductor-iranges >=2.44.0,<2.45.0
on bioconductor-iranges >=2.44.0,<2.45.0a0
on bioconductor-outrider >=1.28.0,<1.29.0
on bioconductor-outrider >=1.28.0,<1.29.0a0
on bioconductor-pcamethods >=2.2.0,<2.3.0
on bioconductor-pcamethods >=2.2.0,<2.3.0a0
on bioconductor-rhdf5 >=2.54.0,<2.55.0
on bioconductor-rhdf5 >=2.54.1,<2.55.0a0
on bioconductor-rsamtools >=2.26.0,<2.27.0
on bioconductor-rsamtools >=2.26.0,<2.27.0a0
on bioconductor-rsubread >=2.24.0,<2.25.0
on bioconductor-rsubread >=2.24.0,<2.25.0a0
on bioconductor-s4vectors >=0.48.0,<0.49.0
on bioconductor-s4vectors >=0.48.0,<0.49.0a0
on bioconductor-summarizedexperiment >=1.40.0,<1.41.0
on bioconductor-summarizedexperiment >=1.40.0,<1.41.0a0
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-bbmisc
on r-cowplot
on r-data.table
on r-extradistr
on r-generics
on r-ggplot2
on r-ggrepel
on r-matrixstats
on r-pheatmap
on r-plotly
on r-pracma
on r-prroc
on r-r.utils
on r-rcolorbrewer
on r-rcpp
on r-rcpparmadillo
on r-rmtstat
on r-tibble
on r-vgam

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-fraser

to add into an existing workspace instead, run:

pixi add bioconductor-fraser

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-fraser

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-fraser

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-fraser:<tag>

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