recipe bioconductor-fraser

Find RAre Splicing Events in RNA-Seq Data







https: :https:`//`

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

(downloads) docker_bioconductor-fraser



depends bioconductor-annotationdbi:


depends bioconductor-biobase:


depends bioconductor-biocgenerics:


depends bioconductor-biocparallel:


depends bioconductor-biomart:


depends bioconductor-bsgenome:


depends bioconductor-delayedarray:


depends bioconductor-delayedmatrixstats:


depends bioconductor-genomeinfodb:


depends bioconductor-genomicalignments:


depends bioconductor-genomicfeatures:


depends bioconductor-genomicranges:


depends bioconductor-hdf5array:


depends bioconductor-iranges:


depends bioconductor-outrider:


depends bioconductor-pcamethods:


depends bioconductor-rhdf5:


depends bioconductor-rsamtools:


depends bioconductor-rsubread:


depends bioconductor-s4vectors:


depends bioconductor-summarizedexperiment:


depends libblas:


depends libgcc-ng:


depends liblapack:


depends libstdcxx-ng:


depends r-base:


depends r-bbmisc:

depends r-cowplot:

depends r-data.table:

depends r-extradistr:

depends r-generics:

depends r-ggplot2:

depends r-ggrepel:

depends r-matrixstats:

depends r-pheatmap:

depends r-plotly:

depends r-prroc:

depends r-r.utils:

depends r-rcolorbrewer:

depends r-rcpp:

depends r-rcpparmadillo:

depends r-tibble:

depends r-vgam:



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-fraser

and update with::

   mamba update bioconductor-fraser

To create a new environment, run:

mamba create --name myenvname bioconductor-fraser

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<tag>

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

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