Package Recipe 'dysgu'

recipe dysgu

A collection of tools for calling structural variants using short or long reads.

Homepage:: https://github.com/kcleal/dysgu
Documentation:: https://github.com/kcleal/dysgu/blob/v1.8.7/README.rst
License:: MIT / MIT
Recipe:: /dysgu/meta.yaml
Links:: doi: 10.1093/nar/gkac039

package dysgu¶

Versions:

1.8.7-0, 1.8.6-0, 1.8.5-0, 1.8.4-0, 1.8.3-0, 1.8.2-1, 1.8.2-0, 1.8.1-1, 1.8.1-0,

1.8.7-0, 1.8.6-0, 1.8.5-0, 1.8.4-0, 1.8.3-0, 1.8.2-1, 1.8.2-0, 1.8.1-1, 1.8.1-0, 1.8.0-1, 1.8.0-0, 1.7.0-1, 1.7.0-0, 1.6.7-0, 1.6.6-0, 1.6.5-1, 1.6.5-0, 1.6.4-1, 1.6.4-0, 1.6.2-0, 1.6.1-0, 1.6.0-1, 1.6.0-0, 1.5.0-1, 1.5.0-0, 1.4.2-0, 1.4.0-0, 1.3.16-0

Depends:

on bzip2 >=1.0.8,<2.0a0
on click >=8.0
on cython >=0.29
on htslib >=1.12
on htslib >=1.22.1,<1.23.0a0
on libcurl >=8.14.1,<9.0a0
on libgcc >=13
on liblzma >=5.8.1,<6.0a0
on libstdcxx >=13
on libzlib >=1.3.1,<2.0a0
on lightgbm >=3.1
on networkx >=2.4
on numpy >=1.18
on numpy >=1.21,<3
on openssl >=1.1
on openssl >=3.5.3,<4.0a0
on pandas >=1.2
on pysam >=0.23
on pysam >=0.23.3,<0.24.0a0
on python >=3.10,<3.11.0a0
on python_abi 3.10.* *_cp310
on scikit-learn >=0.22
on scipy >=1.7
on sortedcontainers
on superintervals >=0.2.10
on superintervals >=0.3.5,<0.4.0a0

Additional platforms:

linux-aarch64, osx-arm64

Installation¶

You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).

Pixi¶

With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:

pixi global install dysgu

to add into an existing workspace instead, run:

pixi add dysgu

In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:

pixi workspace channel add conda-forge
pixi workspace channel add bioconda

Conda¶

With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:

conda install dysgu

Alternatively, to install into a new environment, run:

conda create -n envname dysgu

with envname being the name of the desired environment.

Container¶

Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:

docker pull quay.io/biocontainers/dysgu:<tag>

(see dysgu/tags for valid values for <tag>).

Integrated deployment¶

Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.

Download stats¶

Link to this page¶

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/dysgu/README.html)