recipe bioconductor-titancna

Subclonal copy number and LOH prediction from whole genome sequencing of tumours







biotools: titancna

Hidden Markov model to segment and predict regions of subclonal copy number alterations (CNA) and loss of heterozygosity (LOH), and estimate cellular prevalence of clonal clusters in tumour whole genome sequencing data.

package bioconductor-titancna

(downloads) docker_bioconductor-titancna



depends bioconductor-biocgenerics:


depends bioconductor-biocgenerics:


depends bioconductor-genomeinfodb:


depends bioconductor-genomeinfodb:


depends bioconductor-genomicranges:


depends bioconductor-genomicranges:


depends bioconductor-iranges:


depends bioconductor-iranges:


depends bioconductor-variantannotation:


depends bioconductor-variantannotation:


depends libblas:


depends libgcc-ng:


depends liblapack:


depends r-base:


depends r-data.table:


depends r-dplyr:


depends r-foreach:




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-titancna

and update with::

   mamba update bioconductor-titancna

To create a new environment, run:

mamba create --name myenvname bioconductor-titancna

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

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