<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Simopoulos CMA</submitter><funding>Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada</funding><funding>Ontario Genomics</funding><funding>Ontario Ministry of Economic Development and Innovation</funding><funding>Genome Canada</funding><pagination>e0038122</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9426440</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>7(4)</volume><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</pubmed_abstract><journal>mSystems</journal><pubmed_title>MetaProClust-MS1: an MS1 Profiling Approach for Large-Scale Microbiome Screening.</pubmed_title><pmcid>PMC9426440</pmcid><funding_grant_id>OGI-149</funding_grant_id><funding_grant_id>ORF-DIG 14405</funding_grant_id><funding_grant_id>OGI-156</funding_grant_id><funding_grant_id>CREATE in Technologies for Microbiome Science and Engineering (TECHNOMISE)</funding_grant_id><funding_grant_id>210034</funding_grant_id><funding_grant_id>Discovery Grant</funding_grant_id><pubmed_authors>Simopoulos CMA</pubmed_authors><pubmed_authors>Khamis MM</pubmed_authors><pubmed_authors>Figeys D</pubmed_authors><pubmed_authors>Zhang X</pubmed_authors><pubmed_authors>Lavallee-Adam M</pubmed_authors><pubmed_authors>Ning Z</pubmed_authors><pubmed_authors>Li L</pubmed_authors></additional><is_claimable>false</is_claimable><name>MetaProClust-MS1: an MS1 Profiling Approach for Large-Scale Microbiome Screening.</name><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</description><dates><release>2022-01-01T00:00:00Z</release><publication>2022 Aug</publication><modification>2026-06-13T06:03:45.225Z</modification><creation>2025-02-19T03:28:56.604Z</creation></dates><accession>S-EPMC9426440</accession><cross_references><pubmed>35950762</pubmed><doi>10.1128/msystems.00381-22</doi></cross_references></HashMap>