{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Hughes DA"],"funding":["UK Economic and Social Science Research Council","British Heart Foundation","Cancer Research UK","MRC Integrative Epidemiology Unit","University of Bristol","Biomedical Centre at the University Hospitals Bristol NHS Foundation Trust","British Heart Foundation Doctoral Training Program","CRUK Integrative Cancer Epidemiology Programme","Avon Longitudinal Study of Parents and Children","Medical Research Council Integrative Epidemiology Unit","National Institute of Health Research Senior Investigator","University of Bristol NIHR Biomedical Research Centre","European Research Council","British Heart Foundation Chair","Medical Research Council","National Institute of Health","National Institute for Health Research (NIHR)","UK British Heart Foundation","Wellcome Trust"],"pagination":["1980-1987"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8963298"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["38(7)"],"pubmed_abstract":["<h4>Motivation</h4>Metabolomics is an increasingly common part of health research and there is need for preanalytical data processing. Researchers typically need to characterize the data and to exclude errors within the context of the intended analysis. Whilst some preprocessing steps are common, there is currently a lack of standardization and reporting transparency for these procedures.<h4>Results</h4>Here, we introduce metaboprep, a standardized data processing workflow to extract and characterize high quality metabolomics datasets. The package extracts data from preformed worksheets, provides summary statistics and enables the user to select samples and metabolites for their analysis based on a set of quality metrics. A report summarizing quality metrics and the influence of available "],"journal":["Bioinformatics (Oxford, England)"],"pubmed_title":["metaboprep: an R package for preanalysis data description and processing."],"pmcid":["PMC8963298"],"funding_grant_id":["FS/17/60/33474","NF-0616-10102","WT101597MA","MC_UU_00011/1","WT 217065/Z/19/Z","AA/18/7/34219","669545","CH/F/20/90003","MC_PC_21038","29019","FS/17/60/33474B","217065/Z/19/Z","R01 DK10324","MR/R502340/1","C18281/A29019","202802/Z/16/Z","CS/16/4/32482","MC_UU_00011/6","MC_PC_15018","MR/N024397/1"],"pubmed_authors":["Taylor K","Timpson NJ","Lawlor DA","McBride N","Lee MA","Hughes DA","Corbin LJ","Mason D"],"additional_accession":[]},"is_claimable":false,"name":"metaboprep: an R package for preanalysis data description and processing.","description":"<h4>Motivation</h4>Metabolomics is an increasingly common part of health research and there is need for preanalytical data processing. Researchers typically need to characterize the data and to exclude errors within the context of the intended analysis. Whilst some preprocessing steps are common, there is currently a lack of standardization and reporting transparency for these procedures.<h4>Results</h4>Here, we introduce metaboprep, a standardized data processing workflow to extract and characterize high quality metabolomics datasets. The package extracts data from preformed worksheets, provides summary statistics and enables the user to select samples and metabolites for their analysis based on a set of quality metrics. A report summarizing quality metrics and the influence of available ","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Mar","modification":"2025-08-23T03:05:34.713Z","creation":"2025-04-03T22:44:46.871Z"},"accession":"S-EPMC8963298","cross_references":{"pubmed":["35134881"],"doi":["10.1093/bioinformatics/btac059"]}}