{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Simopoulos CMA"],"funding":["Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada","Ontario Genomics","Ontario Ministry of Economic Development and Innovation","Genome Canada"],"pagination":["e0038122"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9426440"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["7(4)"],"pubmed_abstract":["Metaproteomics is used to explore the functional dynamics of microbial communities. However, acquiring metaproteomic data by tandem mass spectrometry (MS/MS) is time-consuming and resource-intensive, and there is a demand for computational methods that can be used to reduce these resource requirements. We present MetaProClust-MS1, a computational framework for microbiome feature screening developed to prioritize samples for follow-up MS/MS. In this proof-of-concept study, we tested and compared MetaProClust-MS1 results on gut microbiome data, from fecal samples, acquired using short 15-min MS1-only chromatographic gradients and MS1 spectra from longer 60-min gradients to MS/MS-acquired data. We found that MetaProClust-MS1 identified robust gut microbiome responses caused by xenobiotics wit"],"journal":["mSystems"],"pubmed_title":["MetaProClust-MS1: an MS1 Profiling Approach for Large-Scale Microbiome Screening."],"pmcid":["PMC9426440"],"funding_grant_id":["OGI-149","ORF-DIG 14405","OGI-156","CREATE in Technologies for Microbiome Science and Engineering (TECHNOMISE)","210034","Discovery Grant"],"pubmed_authors":["Simopoulos CMA","Khamis MM","Figeys D","Zhang X","Lavallee-Adam M","Ning Z","Li L"],"additional_accession":[]},"is_claimable":false,"name":"MetaProClust-MS1: an MS1 Profiling Approach for Large-Scale Microbiome Screening.","description":"Metaproteomics is used to explore the functional dynamics of microbial communities. However, acquiring metaproteomic data by tandem mass spectrometry (MS/MS) is time-consuming and resource-intensive, and there is a demand for computational methods that can be used to reduce these resource requirements. We present MetaProClust-MS1, a computational framework for microbiome feature screening developed to prioritize samples for follow-up MS/MS. In this proof-of-concept study, we tested and compared MetaProClust-MS1 results on gut microbiome data, from fecal samples, acquired using short 15-min MS1-only chromatographic gradients and MS1 spectra from longer 60-min gradients to MS/MS-acquired data. We found that MetaProClust-MS1 identified robust gut microbiome responses caused by xenobiotics wit","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Aug","modification":"2026-06-13T06:03:45.225Z","creation":"2025-02-19T03:28:56.604Z"},"accession":"S-EPMC9426440","cross_references":{"pubmed":["35950762"],"doi":["10.1128/msystems.00381-22"]}}