recipe latentstrainanalysis

Partitioning and analysis methods for large, complex sequence datasets

Homepage:

https://github.com/brian-cleary/LatentStrainAnalysis

License:

MIT

Recipe:

/latentstrainanalysis/meta.yaml

LSA was developed as a pre-assembly tool for partitioning metagenomic reads. It uses a hyperplane hashing function and streaming SVD in order to find covariance relations between k-mers. The code, and the process outline in LSFScripts in particular, have been optimized to scale to massive data sets in fixed memory with a highly distributed computing environment.

package latentstrainanalysis

(downloads) docker_latentstrainanalysis

versions:

0.0.1-3,  0.0.1-2,  0.0.1-1,  0.0.1-0

depends gensim:

depends numpy:

depends parallel:

depends pyro4:

depends python:

<3

depends scipy:

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 latentstrainanalysis

and update with::

   mamba update latentstrainanalysis

To create a new environment, run:

mamba create --name myenvname latentstrainanalysis

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/latentstrainanalysis:<tag>

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

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