recipe cuna

CUNA: Cytosine Uracil Neural Algorithm for ancient DNA damage detection using nanopore signals.

Homepage:

https://github.com/iris1901/CUNA

License:

MIT / MIT

Recipe:

/cuna/meta.yaml

CUNA is a deep learning pipeline for detecting cytosine deamination (C→U) events in ancient DNA, using raw nanopore signals. It includes feature extraction, model training, and modification detection.

package cuna

versions:

0.1.0-0

depends h5py:

depends matplotlib-base:

depends numba:

depends numpy:

depends pandas:

depends pod5:

depends pysam:

depends python:

>=3.8

depends pytorch:

depends samtools:

depends scikit-learn:

depends scipy:

depends tqdm:

requirements:

additional platforms:

Installation

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 cuna

and update with::

   mamba update cuna

To create a new environment, run:

mamba create --name myenvname cuna

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 quay.io/biocontainers/cuna:<tag>

(see `cuna/tags`_ for valid values for ``<tag>``)

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