{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Habra H"],"funding":["NIEHS NIH HHS","Foundation for the National Institutes of Health","NCI NIH HHS"],"pagination":["5028-5036"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9906987"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["93(12)"],"pubmed_abstract":["LC-HRMS experiments detect thousands of compounds, with only a small fraction of them identified in most studies. Traditional data processing pipelines contain an alignment step to assemble the measurements of overlapping features across samples into a unified table. However, data sets acquired under nonidentical conditions are not amenable to this process, mostly due to significant alterations in chromatographic retention times. Alignment of features between disparately acquired LC-MS metabolomics data could aid collaborative compound identification efforts and enable meta-analyses of expanded data sets. Here, we describe <i>metabCombiner</i>, a new computational pipeline for matching known and unknown features in a pair of untargeted LC-MS data sets and concatenating their abundances int"],"journal":["Analytical chemistry"],"pubmed_title":["<i>metabCombiner</i>: Paired Untargeted LC-HRMS Metabolomics Feature Matching and Concatenation of Disparately Acquired Data Sets."],"pmcid":["PMC9906987"],"funding_grant_id":["P30 ES017885","U2C ES026553","U2CES030164","T32 CA140044","U2CES026553","U2C ES030164"],"pubmed_authors":["Habra H","Kachman M","Clish C","Evans CR","Bullock K","Karnovsky A"],"additional_accession":[]},"is_claimable":false,"name":"<i>metabCombiner</i>: Paired Untargeted LC-HRMS Metabolomics Feature Matching and Concatenation of Disparately Acquired Data Sets.","description":"LC-HRMS experiments detect thousands of compounds, with only a small fraction of them identified in most studies. Traditional data processing pipelines contain an alignment step to assemble the measurements of overlapping features across samples into a unified table. However, data sets acquired under nonidentical conditions are not amenable to this process, mostly due to significant alterations in chromatographic retention times. Alignment of features between disparately acquired LC-MS metabolomics data could aid collaborative compound identification efforts and enable meta-analyses of expanded data sets. Here, we describe <i>metabCombiner</i>, a new computational pipeline for matching known and unknown features in a pair of untargeted LC-MS data sets and concatenating their abundances int","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Mar","modification":"2025-04-04T00:36:10.335Z","creation":"2025-04-04T00:36:10.335Z"},"accession":"S-EPMC9906987","cross_references":{"pubmed":["33724799"],"doi":["10.1021/acs.analchem.0c03693"]}}