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metaTag: functional and taxonomical annotation of metagenomes through phylogenetic tree placement

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python Code style: black Project Status: Active – The project has reached a stable, usable state and is being actively developed.

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💡 What is MetaTag?

This repository contains tools to assign taxonomy and function annotations to short reads through pylogenetic tree evolutionary placement.

🔧 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

Installation test

To check that everything is working properly, you can run a test what will perform the entire workflow on a minimal dataset. To run it, call bash script:

conda activate metatag
(metatag) bash tests/run_test.sh

or through Python's unittest module:

conda activate metatag
(metatag) python -m unittest tests/test_pipeline.py

It should produce a final tree with query sequences placed on it, as well as a bunch of intermediary files without any errors.

🚀 Usage

There are two main ways to use MetaTag: through the command line interface (CLI) or through the Python API. You can find an example of the API usage in the following Notebook:

🔄 Dependencies

MetaTag would not work without these awesome projects:

Thanks!

:octocat: 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.