recipe bioconductor-rmmquant

RNA-Seq multi-mapping Reads Quantification Tool






RNA-Seq is currently used routinely, and it provides accurate information on gene transcription. However, the method cannot accurately estimate duplicated genes expression. Several strategies have been previously used, but all of them provide biased results. With Rmmquant, if a read maps at different positions, the tool detects that the corresponding genes are duplicated; it merges the genes and creates a merged gene. The counts of ambiguous reads is then based on the input genes and the merged genes. Rmmquant is a drop-in replacement of the widely used tools findOverlaps and featureCounts that handles multi-mapping reads in an unabiased way.

package bioconductor-rmmquant

(downloads) docker_bioconductor-rmmquant



depends bioconductor-apeglm:


depends bioconductor-biocstyle:


depends bioconductor-deseq2:


depends bioconductor-genomicranges:




depends bioconductor-s4vectors:


depends bioconductor-summarizedexperiment:


depends bioconductor-tbx20bamsubset:


depends bioconductor-txdb.mmusculus.ucsc.mm9.knowngene:


depends libblas:


depends libgcc-ng:


depends liblapack:


depends libstdcxx-ng:


depends r-base:


depends r-devtools:

depends r-rcpp:




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-rmmquant

and update with::

   mamba update bioconductor-rmmquant

To create a new environment, run:

mamba create --name myenvname bioconductor-rmmquant

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-rmmquant/tags`_ for valid values for ``<tag>``)

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