recipe gemoma

Gene Model Mapper (GeMoMa) is a homology-based gene prediction program. GeMoMa uses the annotation of protein-coding genes in a reference genome to infer the annotation of protein-coding genes in a target genome. Thereby, GeMoMa utilizes amino acid sequence and intron position conservation. In addition, GeMoMa allows to incorporate RNA-seq evidence for splice site prediction.

Homepage:

http://www.jstacs.de/index.php/GeMoMa

License:

GPL3

Recipe:

/gemoma/meta.yaml

Links:

doi: 10.1093/nar/gkw092, doi: 10.1186/s12859-018-2203-5

package gemoma

(downloads) docker_gemoma

versions:

1.9-0,  1.7.1-0,  1.6.4-1,  1.6.4-0

depends blast:

>=2.12.0

depends mmseqs2:

>=14.7e284

depends openjdk:

>=8,<12

depends python:

>=3.7

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 gemoma

and update with::

   mamba update gemoma

To create a new environment, run:

mamba create --name myenvname gemoma

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/gemoma:<tag>

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

Notes

GeMoMa is Java program that comes with a custom wrapper python script. By default "-Xms3g -Xmx6g" is set in the wrapper. If you want to overwrite it you can specify these values directly after your binaries. If you have _JAVA_OPTIONS set globally this will take precedence.

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