| Literature DB >> 30268435 |
Doruk Beyter1, Miin S Lin2, Yanbao Yu3, Rembert Pieper3, Vineet Bafna4.
Abstract
Shotgun metaproteomics has the potential to reveal the functional landscape of microbial communities but lacks appropriate methods for complex samples with unknown compositions. In the absence of prior taxonomic information, tandem mass spectra would be searched against large pan-microbial databases, which requires heavy computational workload and reduces sensitivity. We present ProteoStorm, an efficient database search framework for large-scale metaproteomics studies, which identifies high-confidence peptide-spectrum matches (PSMs) while achieving a two-to-three orders-of-magnitude speedup over popular tools. A reanalysis of a urinary tract infection (UTI) dataset of 110 individuals revealed a complex pattern of polymicrobial expression, including sub-types of UTIs, cases of bacterial vaginosis, and evidence of no underlying disease. Importantly, compared to the initial UTI study that restricted the search database to a manually curated list of 20 genera, ProteoStorm identified additional genera that were previously unreported, including a case of infection with the rare pathogen Propionimicrobium.Entities:
Keywords: LC-MS/MS; metaproteomics; microbial communities; microbiology; proteome informatics; proteomics
Mesh:
Year: 2018 PMID: 30268435 PMCID: PMC6231400 DOI: 10.1016/j.cels.2018.08.009
Source DB: PubMed Journal: Cell Syst ISSN: 2405-4712 Impact factor: 10.304