recipe bioconductor-uniquorn

Identification of cancer cell lines based on their weighted mutational/ variational fingerprint







biotools: uniquorn, doi: 10.18632/oncotarget.16110

'Uniquorn' enables users to identify cancer cell lines. Cancer cell line misidentification and cross-contamination reprents a significant challenge for cancer researchers. The identification is vital and in the frame of this package based on the locations/ loci of somatic and germline mutations/ variations. The input format is vcf/ vcf.gz and the files have to contain a single cancer cell line sample (i.e. a single member/genotype/gt column in the vcf file).

package bioconductor-uniquorn

(downloads) docker_bioconductor-uniquorn



depends bioconductor-genomicranges:


depends bioconductor-iranges:


depends bioconductor-variantannotation:


depends r-base:


depends r-data.table:

depends r-doparallel:

depends r-foreach:

depends r-r.utils:

depends r-stringr:

depends r-writexls:



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-uniquorn

and update with::

   mamba update bioconductor-uniquorn

To create a new environment, run:

mamba create --name myenvname bioconductor-uniquorn

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

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