MacCoss, Michael J.Faivre, Danielle A.2023-09-272023-09-272023Faivre_washington_0250E_26225.pdfhttp://hdl.handle.net/1773/50836Thesis (Ph.D.)--University of Washington, 2023Proteomics primarily focuses on identifying and quantifying peptides using database searches. However, there are instances where certain features of interest, such as unexpected byproducts, abnormal cleavages, unknown modifications, sequence variants, and glycans, remain unidentified. These unidentified peptide-like features can be collectively referred to as the “Hidden Proteome.” Unfortunately, these hidden features are often overlooked in quality control techniques, discovery experiments, and targeted analyses. This research aims to address this gap and complement traditional proteomics approaches. To investigate the “Hidden Proteome” and enhance data quality assessment, the Python package msions is introduced. This package allows users to assess the extent to which their signal falls into the “Hidden Proteome” and identify issues in the mass spectrometry (MS) data. Functionality of msions was also used to study the mechanism by which high-field asymmetric waveform ion mobility spectrometry (FAIMS) improves MS results. Ion mobility approaches can provide valuable insights into the “Hidden Proteome” since the partially orthogonal separation aids in sample characterization. Further characterization can be performed using a workflow developed to detect persistent MS1 features and enable users to examine unidentified features alongside features with assigned peptide identities. These “Hidden Proteome” features play a crucial role in Multi-Attribute Method (MAM) for quality control of biopharmaceuticals and other new feature detection approaches, such as the analysis of proteoforms, protein-protein interactions, metabolic responses to stimuli, and impurities. Overall, these projects offer valuable packages, workflows, and tools that can greatly benefit researchers in the field of proteomics. With the integration of msions into Limelight and the incorporation of the MAM project into Skyline, these endeavors are expected to continue to grow and reach a wider audience in the future.application/pdfen-USCC BYgas-phase fractionationmass spectrometryproteomicsBioinformaticsBiochemistryGeneticsGeneticsThesis