biostudies-literatureKarpievitch YNIDDK NIH HHSNIAID NIH HHSNCI NIH HHS2028-34https://www.ebi.ac.uk/biostudies/studies/S-EPMC2723007biostudies-literatureUnknown25(16)<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 metBioinformatics (Oxford, England)A statistical framework for protein quantitation in bottom-up MS-based proteomics.PMC2723007R33 DK070146Y1-AI-4894-01R25-CA-90301R21 DK070146R01 AI022933DK070146R25 CA090301Heffron FQian WJYoon HAnsong CTaverner THuang JStanley JSmith RDDabney ARKarpievitch YAdkins JNMetz TOfalseA statistical framework for protein quantitation in bottom-up MS-based proteomics.<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 met2009-01-01T00:00:00Z2009 Aug2025-04-05T15:10:44.545Z2019-03-27T00:24:06ZS-EPMC27230071953553810.1093/bioinformatics/btp362