{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Karpievitch Y"],"funding":["NIDDK NIH HHS","NIAID NIH HHS","NCI NIH HHS"],"pagination":["2028-34"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC2723007"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["25(16)"],"pubmed_abstract":["<h4>Motivation</h4>Quantitative mass spectrometry-based proteomics requires protein-level estimates and associated confidence measures. Challenges include the presence of low quality or incorrectly identified peptides and informative missingness. Furthermore, models are required for rolling peptide-level information up to the protein level.<h4>Results</h4>We present a statistical model that carefully accounts for informative missingness in peak intensities and allows unbiased, model-based, protein-level estimation and inference. The model is applicable to both label-based and label-free quantitation experiments. We also provide automated, model-based, algorithms for filtering of proteins and peptides as well as imputation of missing values. Two LC/MS datasets are used to illustrate the met"],"journal":["Bioinformatics (Oxford, England)"],"pubmed_title":["A statistical framework for protein quantitation in bottom-up MS-based proteomics."],"pmcid":["PMC2723007"],"funding_grant_id":["R33 DK070146","Y1-AI-4894-01","R25-CA-90301","R21 DK070146","R01 AI022933","DK070146","R25 CA090301"],"pubmed_authors":["Heffron F","Qian WJ","Yoon H","Ansong C","Taverner T","Huang J","Stanley J","Smith RD","Dabney AR","Karpievitch Y","Adkins JN","Metz TO"],"additional_accession":[]},"is_claimable":false,"name":"A statistical framework for protein quantitation in bottom-up MS-based proteomics.","description":"<h4>Motivation</h4>Quantitative mass spectrometry-based proteomics requires protein-level estimates and associated confidence measures. Challenges include the presence of low quality or incorrectly identified peptides and informative missingness. Furthermore, models are required for rolling peptide-level information up to the protein level.<h4>Results</h4>We present a statistical model that carefully accounts for informative missingness in peak intensities and allows unbiased, model-based, protein-level estimation and inference. The model is applicable to both label-based and label-free quantitation experiments. We also provide automated, model-based, algorithms for filtering of proteins and peptides as well as imputation of missing values. Two LC/MS datasets are used to illustrate the met","dates":{"release":"2009-01-01T00:00:00Z","publication":"2009 Aug","modification":"2025-04-05T15:10:44.545Z","creation":"2019-03-27T00:24:06Z"},"accession":"S-EPMC2723007","cross_references":{"pubmed":["19535538"],"doi":["10.1093/bioinformatics/btp362"]}}