MetaTag is a Python-based pipeline to assign functional and taxonomical annotations to unannotated short metagenomic sequence data sets.

These are the available subcommands, run as metatag <subcommand> <options>:

• preprocess
• database
• tree
• place
• assign
• count
• plot
• relabel

Setup

The easiest way to use MetaTag is through the provided docker container. To use it, pull the image:

docker pull ghcr.io/robaina/metatag:latest

Then run the container interactively:

docker run -i ghcr.io/robaina/metatag:latest

Otherwise, you can install MetaTag like this:

1. Fork git repo into local machine (click on fork) and clone, or simply clone main branch with

git clone https://github.com/Robaina/MetaTag.git

1. CD to project MetaTag and set conda environment if not already set:

conda env create -n metatag -f envs/metatag-dev.yml

1. Build and install MetaTag:

conda activate metatag
(metatag) poetry build && pip install dist/metatag*.whl

Installing MetaTag on Windows

MetaTag is designed to run on Linux machines. However, it can be installed within the Windows Subsystem for Linux via conda or docker.

General usage

MetaTag can be used either as command line tool or as a Python package.

Getting started and Examples

Dependencies

MetaTag would not work without these awesome projects:

• hmmer
• prodigal
• pyfastx
• biopython
• papara
• epa-ng
• gappa
• empress
• muscle
• mafft
• numpy
• pandas
• psutil

Thanks!

Contributing

Contributions are always welcome! If you don't know where to start, you may find an interesting issue to work in here. Please, read our contribution guidelines first.

Citation

If you use this software, please cite it as below:

Semidán Robaina Estévez. (2022). MetaTag: (Version 0.0.2). Metagenome functional and taxonomical annotation through phylogenetic tree placement. Zenodo. https://doi.org/10.5281/zenodo.7048685