recipe hatchet

Holistic Allele-specific Tumor Copy-number Heterogeneity






HATCHet is an algorithm to infer allele and clone-specific copy-number aberrations (CNAs), clone proportions, and whole-genome duplications (WGD) for several tumor clones jointly from multiple bulk-tumor samples of the same patient or from a single bulk-tumor sample.

package hatchet

(downloads) docker_hatchet



depends bcftools:


depends biopython:

depends hmmlearn:

depends libcxx:


depends matplotlib-base:

depends mosdepth:

depends pandas:

depends picard:

depends psutil:

depends pyomo:

depends pysam:

depends python:


depends python_abi:

3.10.* *_cp310

depends requests:

depends samtools:


depends scikit-learn:

depends scipy:

depends seaborn:

depends tabix:



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 hatchet

and update with::

   mamba update hatchet

To create a new environment, run:

mamba create --name myenvname hatchet

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 `hatchet/tags`_ for valid values for ``<tag>``)

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