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
- 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
MGLEX is a Python 3 package, it does not run with Python 2 versions. It depends on
- NumPy
- SciPy (for few functions)
- docopt
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-scipyWe 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/activateSimilarly, 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-envMGLEX is deposited on the Python Package Index and we recommend to install it via pip.
python -m pip install mglexThis 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