{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/85032"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/85032","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Bioinformatics of High Throughput Proteomics Using Tandem Mass Spectrometry of Intact Proteins","abstract":"Top down mass spectrometry is a unique approach to the problem of identifying and characterizing proteins with a DNA-predicted sequence. It would be desirable to move top down mass spectrometry in to the 'omic' sciences by developing a high throughput form that could be used to simultaneously identify and characterize thousands of proteins. Before this is possible, the bioinformatic analysis of top down mass spectrometric data needed to be automated. This work demonstrates a functional automated top down search environment called ProSightHT. Further, it derives and demonstrates the utility of an improved scoring system that simultaneously reduces the rate of false identifications and missed characterizations. A superior Automated Protein Characterization database schema is introduced that allows the storage of all prior protein form information, and a statistically valid approach to protein form quantification is demonstrated.","abstract_html":"Top down mass spectrometry is a unique approach to the problem of identifying and characterizing proteins with a DNA-predicted sequence. It would be desirable to move top down mass spectrometry in to the 'omic' sciences by developing a high throughput form that could be used to simultaneously identify and characterize thousands of proteins. Before this is possible, the bioinformatic analysis of top down mass spectrometric data needed to be automated. This work demonstrates a functional automated top down search environment called ProSightHT. Further, it derives and demonstrates the utility of an improved scoring system that simultaneously reduces the rate of false identifications and missed characterizations. 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