recipe seqchromloader

Sequence and chromatin dataloader for deep learning

Homepage:

https://github.com/yztxwd/seqchromloader

Documentation:

https://github.com/seqcode/seqchromloader

Developer docs:

https://github.com/seqcode/seqchromloader

License:

MIT

Recipe:

/seqchromloader/meta.yaml

package seqchromloader

versions:

0.8.3-0,  0.7.8-0,  0.7.7-0,  0.7.6-0,  0.7.5-0,  0.7.4-0,  0.7.3-0,  0.7.1-0,  0.7.0-0, 

0.8.3-0,  0.7.8-0,  0.7.7-0,  0.7.6-0,  0.7.5-0,  0.7.4-0,  0.7.3-0,  0.7.1-0,  0.7.0-0,  0.6.8-0,  0.6.5-0,  0.6.3-0,  0.6.2-0,  0.5.3-0,  0.4.0-0,  0.2.4-0,  0.2.1-0

depends numpy:

>=1.17

depends pandas:

depends pybedtools:

>=0.9.0

depends pybigwig:

>=0.3.0

depends pyfaidx:

>=0.7.0

depends pysam:

>=0.19.0

depends python:

>=3.7,<3.10

depends pytorch:

>=1.10.0

depends pytorch-lightning:

>=1.7.0,<1.8.0

depends torchmetrics:

<=0.11.4

depends webdataset:

>=0.2.0

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 seqchromloader

and update with::

   mamba update seqchromloader

To create a new environment, run:

mamba create --name myenvname seqchromloader

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/seqchromloader:<tag>

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

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