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Description:

MGLEX 0.2.1

This Python Package provides a probabilistic model to classify nucleotide
sequences in metagenome samples. It was developed as a framework to help
researchers to reconstruct individual genomes from such datasets using custom
workflows and to give developers the possibility to integrate the model into
their programs.

Free software: GPLv3 license
Source code: https://github.com/fungs/mglex
Documentation: https://mglex.readthedocs.io

Features

Integrates nucleotide composition, multi-sample coverage
and taxonomic annotation
Learns a model in linear time with respect to the number of input sequences
Classifies novel sequences in linear time
Calculates likelihood and p-values
Calculates probabilistic distances between genome bins

Dependencies
MGLEX is a Python 3 package, it does not run with Python 2 versions. It depends on

NumPy
SciPy (for few functions)
docopt

Installation

Install dependencies with Debian/Ubuntu & Python-Virtualenv
We show how to install MLGEX under Debian and Ubuntu, but other platforms are similar.
You can simply install the requirements as system packages.
sudo apt install python3 python3-numpy python3-scipy
We recommend to create a Python virtual installation enviroment for MGLEX. In order to do so, install the venv package for your Python version (e.g. the Debian package python3.4-venv), if not included (or use virtualenv). The following command will make use of the installed system packages.
python3 -m venv --system-site-packages mglex-env
source mglex-env/bin/activate

Install dependencies with Conda
Similarly, you can use Anaconda or Conda to prepare an environment with the dependencies and activate it.
conda create -n mglex-env -c conda-forge numpy scipy docopt python=3
source activate mglex-env

Install MGLEX Python package
MGLEX is deposited on the Python Package Index and we recommend to install it via pip.
python -m pip install mglex

Credits
This package was created using NumPy by Johannes Dröge at the Computational
Biology of Infection Research Group at the Helmholtz Centre for Infection
Research, Braunschweig, Germany.
Please cite:
Dröge J, Schönhuth A, McHardy AC. (2017)
A probabilistic model to recover individual genomes from metagenomes.
PeerJ Computer Science 3:e117 https://doi.org/10.7717/peerj-cs.117