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Title page for ETD etd-07012015-184131


Type of Document Master's Thesis
Author Kochen, Michael Allen
Author's Email Address kochen.michael@gmail.com
URN etd-07012015-184131
Title Greazy: Open-Source Software for Automated Phospholipid MS/MS Identification
Degree Master of Science
Department Biomedical Informatics
Advisory Committee
Advisor Name Title
David L. Tabb Committee Chair
Bing Zhang Committee Member
John A. McLean Committee Member
Keywords
  • FDR
  • fragmentation
  • validation
  • lipidome
  • false
  • discovery
Date of Defense 2015-06-11
Availability unrestricted
Abstract
Lipid identification from data sets produced with high-throughput technologies is essential to discovery in lipidomics experiments. A number of software tools for making lipid identifications from tandem spectra have been developed in recent years, but they lack the robustness and sophistication of their proteomics counterparts. We have developed Greazy, a tool for the automated identification of phospholipids from tandem mass spectra, which utilizes methods developed for proteomics. Greazy builds user-defined a search space of phospholipids and associated theoretical tandem spectra. Experimental spectra are scored against search space lipids with similar precursor masses using two probability-based scores: a peak score that employs the hypergeometric distribution and an intensity score that utilizes the percentage of total ion intensity residing in matching peaks. These results are filtered with LipidLama using a mixture modelling approach and utilizing a density estimation algorithm. We assess the performance of Greazy against the NIST 2014 metabolomics library, observing high accuracy in a search of multiple lipid classes. We compare Greazy/LipidLama against the commercial lipid identification software LipidSearch and show that the two platforms differ considerably in the sets of identified spectra while showing good agreement on those spectra identified by both. Lastly, we demonstrate the utility of Greazy/LipidLama by searching data sets from four biological replicates. These findings substantiate the application of methods developed for proteomics to the identification of lipids.
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