Biological Function Assignment across Taxonomic Levels in Mass-Spectrometry-Based Metaproteomics via a Modified...

Area of Science:

• Microbiology
• Bioinformatics
• Proteomics

Background:

• Mass-spectrometry-based metaproteomics faces challenges in accurately identifying microbial functions due to the shared confidently identified peptide problem.
• Current tools often use the lowest common ancestor (LCA) algorithm, leading to incomplete taxonomic and functional assignments.

Purpose of the Study:

• To enhance the MiCId workflow for improved microorganism identification and biological function quantification.
• To address the limitations of existing metaproteomics tools in handling shared peptides and taxonomic lineage.

Main Methods:

• Implementation of an expectation-maximization (EM) algorithm within the MiCId workflow.
• Integration of a biological function database for enhanced analysis.
• Validation using synthetic datasets and reanalysis of human microbiome datasets.

Main Results:

• The enhanced MiCId workflow demonstrated superior control over false discoveries and improved accuracy in microorganism identification and biomass estimation compared to Unipept and MetaGOmics.
• The updated MiCId showed enhanced accuracy and false discovery control for biological function identification versus Unipept.
• Reliable computation of function abundances across the full taxonomic lineage was achieved.

Conclusions:

• The enhanced MiCId workflow provides a more accurate and reliable method for metaproteomic analysis, particularly for complex microbial communities.
• This approach overcomes limitations of LCA-based methods, enabling comprehensive functional insights across the entire taxonomic range.
• The findings are consistent with previous analyses, validating the utility of the enhanced MiCId workflow in real-world microbiome studies.