MGLEX
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-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/activateInstall 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-envInstall MGLEX Python package
MGLEX is deposited on the Python Package Index and we recommend to install it via pip.
python -m pip install mglexCredits
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
1 maintainer
Metadata
1 maintainer
Release files for MGLEX 0.2.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| MGLEX-0.2.1.tar.gz | 55.1 kB | Without provenance | Details for MGLEX-0.2.1.tar.gz |
Release files / MGLEX-0.2.1.tar.gz
| Download URL | MGLEX-0.2.1.tar.gz |
|---|---|
| Size | 55.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
2a81a50c2f3ceb85a4f936ee7822c1249eeb74d46d4eca498201207d08788541
|
|
BLAKE2b-256 checksum How to use checksums |
c614530f4233219561623d77743eef3fceb79ab3c1caabcc2e49de2b634170c7
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
Security for MGLEX 0.2.1
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
