recipe ngsep

NGSEP - Next Generation Sequencing Experience Platform

Homepage:

https://github.com/NGSEP/NGSEPcore

License:

GPL / GPL-3

Recipe:

/ngsep/meta.yaml

Links:

biotools: ngsep, doi: 10.1093/bioinformatics/btz275

NGSEP provides an object model to enable different kinds of analysis of DNA high throughput sequencing (HTS) data. The classic use of NGSEP is a reference guided construction and downstream analysis of large datasets of genomic variation. NGSEP performs accurate detection and genotyping of Single Nucleotide Variants (SNVs), small and large indels, short tandem repeats (STRs), inversions, and Copy Number Variants (CNVs). NGSEP also provides utilities for downstream analysis of variation in VCF files, including functional annotation of variants, filtering, format conversion, comparison, clustering, imputation, introgression analysis and different kinds of statistics.

package ngsep

(downloads) docker_ngsep

versions:

4.0.1-0

depends openjdk:

>=8

depends python:

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 ngsep

and update with::

   mamba update ngsep

To create a new environment, run:

mamba create --name myenvname ngsep

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/ngsep:<tag>

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

Notes

Version 4 includes new modules for read alignment and de-novo analysis of short and long reads including calculations of k-mers, error correction, de-novo analysis of Genotype-by-sequencing data and (coming soon) de-novo assembly of long read whole genome sequencing (WGS) data.

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