MetaLab is an integrated, user-friendly software platform for fast and automated metaproteomic data analysis. It provides a complete pipeline for microbial protein identification, quantification, and taxonomic profiling directly from mass spectrometry raw data. MetaLab is designed to simplify and accelerate metaproteomics research for microbiome studies.

• Automated Workflow: From raw MS data to protein identification, quantification, and taxonomic profiling.
• Sample-Specific Database Generation: Efficiently handles large and complex protein databases for metaproteomics.
• Spectral Clustering: Dramatically improves the speed and sensitivity of peptide identification.
• Quantitative Analysis: Supports label-free and labeled quantification methods.
• Taxonomic Profiling: Estimates the relative abundance of taxa at all phylogenetic ranks.
• User-Friendly GUI: Designed for ease of use by researchers in microbiome and proteomics fields.
• Compatibility: Taxonomy result files are fully compatible with widely used metagenomics tools.

If you use MetaLab in your research, please cite the following publications:

1. MetaLab: an automated pipeline for metaproteomic data analysis
   Kai Cheng, Zhibin Ning, Xu Zhang, Leyuan Li, Bo Liao, Janice Mayne, Alain Stintzi, Daniel Figeys
   Microbiome 5, 157 (2017).
   https://doi.org/10.1186/s40168-017-0375-2

2. MetaLab-MAG: A Metaproteomic Data Analysis Platform for Genome-Level Characterization of Microbiomes from the Metagenome-Assembled Genomes Database Kai Cheng, Zhibin Ning, Leyuan Li, Xu Zhang, Joeselle M. Serrana, Janice Mayne, Daniel Figeys https://pubs.acs.org/doi/full/10.1021/acs.jproteome.2c00554

• Launch the GUI and follow the workflow to import raw data, set parameters, and run analysis.
• For detailed instructions, see the user manual or the original publications.

MetaLab is distributed under an open source license. See the LICENSE file or refer to the original publication for details.

For questions, bug reports, or contributions, please open an issue on GitHub or contact Kai Cheng ([email protected]) directly.