UNIGE document Doctoral Thesis
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Title

Development of computational tools to improve data-independent workflows for the characterization of proteins and metabolites by mass spectrometry
Author
Bilbao Pena, Aivett
Directors
Hopfgartner, Gerard
Lisacek, Frédérique
Defense Thèse de doctorat : Univ. Genève, 2015 - Sc. 4872 - 2015/12/09
Abstract This thesis focuses on the design, implementation and benchmarking of various software tools aiming to improve the identification and quantification of complex protein digests and metabolites analyzed by LC-MS/MS. The solutions involve different steps in the workflow, from enhanced data acquisition to novel post-acquisition data processing strategies. A significant increase of peptide/protein identification rates was achieved by combining exclusion and inclusion lists in data-dependent acquisition. Data-independent acquisition schemes are examined, in particular, related algorithms and computational methods are discussed. A program was implemented to design and optimize different SWATH acquisition methods and the benefits of variable isolation windows are demonstrated for the profiling of proteomic and metabolic samples. A ranking algorithm was developed to assign low priority to fragment ions affected by interference during SWATH acquisition and the improvements for label-free quantification are illustrated. Finally, several demultiplexing approaches towards peptide identification by sequence database search of SWATH spectra were investigated.
Keywords Data-independent acquisition — Interference removal — Metabolomics — Proteomics — SWATH — Variable-precursor-isolation-window widths
Identifiers
URN: urn:nbn:ch:unige-800462
Full text
Thesis (9.8 MB) - document accessible for UNIGE members only Limited access to UNIGE
Structures
Faculté des sciences
Research groups Spectrométrie de masse du vivant
Proteome Informatics Group
Citation
(ISO format) 		BILBAO PENA, Aivett. Development of computational tools to improve data-independent workflows for the characterization of proteins and metabolites by mass spectrometry. Université de Genève. Thèse, 2015. https://archive-ouverte.unige.ch/unige:80046

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Deposited on : 2016-01-25

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