recipe svision-pro

Neural-network-based long-read SV caller.

Homepage:

https://github.com/songbowang125/SVision-pro

Documentation:

https://github.com/songbowang125/SVision-pro/blob/v2.3/README.md

License:

GPL3 / GPL-3.0-or-later

Recipe:

/svision-pro/meta.yaml

A neural-network-based instance segmentation framework that represents genome-to-genome-level sequencing differences visually and discovers SV comparatively between genomes without any prerequisite for inference models.

package svision-pro

versions:

2.3-0,  2.2-0,  2.1-0,  2.0-0

depends numpy:

1.21.6

depends pillow:

9.2.0

depends py-opencv:

>=4.5.3

depends pysam:

>=0.20.0

depends python:

>=3.7.9

depends pytorch:

1.10.1

depends scipy:

1.7.3

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 svision-pro

and update with::

   mamba update svision-pro

To create a new environment, run:

mamba create --name myenvname svision-pro

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/svision-pro:<tag>

(see `svision-pro/tags`_ for valid values for ``<tag>``)

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