MetaLab-MAG: A Metaproteomic Data Analysis Platform for Genome-Level Characterization of Microbiomes from the Metagenome-Assembled Genomes Database

Abstract

The studies of microbial communities have drawn increased attention in various research fields such as agriculture, environment, and human health. Recently, metaproteomics has become a powerful tool to interpret the roles of the community members by investigating the expressed proteins of the microbes. However, analyzing the metaproteomic data sets at genome resolution is still challenging because of the lack of efficient bioinformatics tools. Here we develop MetaLab-MAG, a specially designed tool for the characterization of microbiomes from metagenome-assembled genomes databases. MetaLab-MAG was evaluated by analyzing various human gut microbiota data sets and performed comparably or better than searching the gene catalog protein database directly. MetaLab-MAG can quantify the genome-level microbiota compositions and supports both label-free and isobaric labeling-based quantification strategies. MetaLab-MAG removes the obstacles of metaproteomic data analysis and provides the researchers with in-depth and comprehensive information from the microbiomes.

MetaX: A peptide centric metaproteomic data analysis platform using Operational Taxa-Functions (OTF)

“…This method relies on matching experimental data with computational predictions (45). Within metaproteomics, the use of metagenome-assembled genomes (MAGs) is common for taxa identification (16).…”

“…One prevalent solution is the application of Occam's razor to generate a minimal list of proteins sufficient for explaining all observed peptides (15). Various tools, like MetaLab (16), employ statistical models to analyze both functions and taxonomies in metaproteomics. Conversely, the anti-Occam's razor approach aims for a maximal explanatory protein set, in which each protein identified by at least one peptide is included, as utilized in MetaProteomeAnalyzer (17).…”

MetaDIA: A Novel Database Reduction Strategy for DIA Human Gut Metaproteomics

“…For the functional score, we constructed a fixed table from the MetaPep project [32]. While building the database Metapep, the peptide identification was performed by the software MetaLab MAG[7], which provides quantifications of protein abundance. Those proteins are well annotated.…”

“…Employing an iterative search strategy significantly reduces the search complexity in which the final search is against a database generated from previous searching results [4,5]. The iterative strategy has been successfully used but only for the data acquired by data-dependent acquisition (DDA) mode [6,7], Unfortunately, in DDA mode, only the most abundant precursor ions are selected for further inquiry, and lower abundant ones are overlooked [8].…”

“…Therefore, to leverage the benefits of DIA in metaproteomics, the searching space needs to be further reduced. In the previous DDA iterative strategy [7,28], the high-abundant proteins (HAP) were used for the first search to infer the species that exist in the sample then all the proteins belonging to those species were then used for the subsequent search. However, this database remains overly extensive when compared to the number of identified peptides.…”