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MetaX

MetaX is a novel tool for linking peptide sequences with taxonomic and functional information in Metaproteomics. We introduce the Operational Taxon-Function (OTF) concept to explore microbial roles and interactions ("who is doing what and how") within ecosystems.

MetaX also features statistical modules and plotting tools for analyzing peptides, taxa, functions, proteins, and taxon-function contributions across groups.

abstract

Operational Taxon-Function (OTF)

Operational Taxon-Function (OTF) Unit: An operational unit which represents the association between specific taxa and biological functions.

OTF_Structure

OTF Analyzer

The Tools in OTF Analyzer

compostion

OTF Examples

Linking Taxa and Functions in different levels of the hierarchy, and various functional categories. e.g., Species-KO, Genus-CAZy, Phylum-EC, etc. Lots of visualization tools can be selected.

Network

Dots represent taxa and squares represent functions in the network.

  • OTF

Heatmap

Show all Taxa of a function.

  • tf_link_heatmap

Bar

Show all functions of a taxon

  • tf_link_bar

OTF Heatmap

Show OTFS intensity in groups(samples), e.g., Species-KO OTF Heatmap

otf_heatmap

Download & Installation

  • Desktop Version (Recommended)

    The desktop version comes fully set up and ready to use, including all required dependencies and a graphical user interface.

    Download the MetaX Desktop Version


  • Command-line version

    Use PyPi to install, then type metax in the terminal to launch the GUI.

    python -m pip install MetaXTools

Guidebook

  • Desktop Version(desktop):
    • Refer to the MetaX Cookbook for detailed instructions on how to use MetaX with the graphical user interface.

  • Command-line version:
    • Read the example documentation in the Notebook for detailed instructions and examples.

Citing

If you use MetaX in your research, please cite the following publication:

MetaX: A peptide centric metaproteomic data analysis platform using Operational Taxa-Functions (OTF). Wu, Q., Ning, Z., Zhang, A., Zhang, X., Sun, Z., & Figeys, D. (2024). bioRxiv. DOI: 10.1101/2024.04.19.590315.