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Developing algorithms to automate the identification of post translational modification in LC-MS/MS data

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Defense Thèse de doctorat : Univ. Genève, 2018 - Sc. 5194 - 2018/02/27
Abstract Understanding post-translational modification (PTM) of proteins and how PTMs influences cellular processes is an important part of understanding the biology of both healthy and diseased cells. The most widely used experimental technique for studying PTMs is Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS). When LC-MS/MS is applied to complex mixtures, such as cell lines or tissue samples, a large amount of raw data is produced which needs to be analysed to identify molecular structures. This thesis focuses on the development of software to automate the identification of PTMs in proteomic MS/MS data and identifying glycans in glycomic MS/MS data. The outcome of this thesis is three papers and their associated software packages: MzJava, MzMod and Glycoforest. MzJava provides the building blocks for developing MS/MS analysis software, MzMod is a validated and improved spectrum library based open modification search engine and Glycoforest pioneered a new approach for automating glycomics analysis.
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URN: urn:nbn:ch:unige-1045171
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HORLACHER, Oliver. Developing algorithms to automate the identification of post translational modification in LC-MS/MS data. Université de Genève. Thèse, 2018. https://archive-ouverte.unige.ch/unige:104517

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Deposited on : 2018-05-16

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