recipe sfs_code

This article introduces a new forward population genetic simulation program that can efficiently generate samples from populations with complex demographic histories under various models of natural selection. The program (SFS_CODE) is highly flexible, allowing the user to simulate realistic genomic regions with several loci evolving according to a variety of mutation models (from simple to context-dependent), and allows for insertions and deletions. Each locus can be annotated as either coding or non-coding, sex-linked or autosomal, selected or neutral, and have an arbitrary linkage structure (from completely linked to independent). © The Author 2008. Published by Oxford University Press. All rights reserved.

Homepage:

http://sfscode.sourceforge.net/SFS_CODE/index/index.html

License:

file

Recipe:

/sfs_code/meta.yaml

Links:

biotools: sfs_code, doi: 10.1093/bioinformatics/btn522

package sfs_code

(downloads) docker_sfs_code

versions:

20150910-620150910-520150910-420150910-320150910-220150910-120150910-0

depends libgcc-ng:

>=12

requirements:

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 sfs_code

and update with::

   mamba update sfs_code

To create a new environment, run:

mamba create --name myenvname sfs_code

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/sfs_code:<tag>

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

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