Abstract Microbiomes, especially within the gut, are complex and may comprise hundreds of species. The identification of peptides in metaproteomics presents a substantial challenge, as it involves matching peptides to mass spectra within an enormous search space for complex and unknown samples. This poses difficulties for both the accuracy and the speed of identification. Specifically, analysis of data-independent acquisition (DIA) datasets has relied on libraries constructed from prior data-dependent acquisition (DDA) results. However, this method is resource-intensive, consumes samples, and limits identification to peptides previously identified. These limitations restrict the application of DIA in metaproteomics research. We introduced a novel strategy to reduce the search space by utilizing species abundance and functional abundance information from the microbiome to score each peptide and prioritize those most likely to be detected. Using this strategy, we have developed and optimized a workflow called MetaDIA for the analysis of microbiome data generated by DIA, which operates independently of DDA assistance. Our approach successfully created a smaller, yet sufficient database for DIA data search in metaproteomics. The results demonstrated strong consistency with the traditional DDA-based library approach at both protein and functional levels. MetaDIA is readily accessible as an open-source project hosted on GitHub (https://github.com/northomics/MetaDIA).
Duan et al. (Sat,) studied this question.