SEMQuant: Extending Sipros-Ensemble with Match-Between-Runs for Comprehensive Quantitative Metaproteomics.

Area of Science:

• Microbiology
• Proteomics
• Bioinformatics

Background:

• Quantitative metaproteomics provides insights into microbial community dynamics by measuring protein abundance.
• Missing peptide quantification is a significant challenge in data-dependent acquisition (DDA) mode LC-MS.
• Match-between-runs (MBR) techniques help mitigate missing data by transferring peptide information across runs.

Purpose of the Study:

• To develop an integrated software solution for streamlined and accurate quantitative metaproteomics analysis.
• To address the persistent challenge of missing peptide quantification in metaproteomics datasets.

Main Methods:

• Integration of Sipros-Ensemble for peptide identification and IonQuant for MBR-based quantification.
• Development of SEMQuant, an end-to-end software pipeline for metaproteomics data analysis.
• Validation across diverse metaproteomics datasets to assess performance.

Main Results:

• SEMQuant consistently achieved high numbers of quantified proteins with excellent precision and accuracy.
• The software effectively addressed missing peptide quantification issues inherent in DDA mode.
• SEMQuant demonstrated robust performance across various microbial community datasets.

Conclusions:

• SEMQuant offers a powerful and effective solution for comprehensive quantitative metaproteomics.
• The software has the potential to significantly advance microbial community studies through improved data analysis.
• SEMQuant is freely available, promoting wider adoption and research in the field.