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oai:www.repository.cam.ac.uk:1810/2751372019-01-30T13:09:41Zcom_1810_721com_1810_256064col_1810_218856

Proteogenomics for Personalised Molecular Profiling 10.17863/CAM.22318 Schlaffner, Christoph Norbert 0000000327173406 Bender, Andreas 0000000266837546 Choudhary, Jyoti 0000000308815477 Technological advancements in mass spectrometry allowing quantification of almost complete proteomes make proteomics a key platform for generating unique functional molecular data. Furthermore, the integrative analysis of genomic and proteomic data, termed proteogenomics, has emerged as a new field revealing insights into gene expression regulation, cell signalling, and disease processes. However, the lack of software tools for high-throughput integration and unbiased modification and variant detection hinder efforts for large-scale proteogenomics studies. The main objectives of this work are to address these issues by developing and applying new software tools and data analysis methods. Firstly, I address mapping of peptide sequences to reference genomes. I introduce a novel tool for high-throughput mapping and highlight its unique features facilitating quantitative and post-translational modification mapping alongside accounting for amino acid substitutions. The performance is benchmarked. Furthermore, I offer an additional tool that permits generation of web accessible hubs of genome wide mappings. To enable unbiased identification of post-translational modifications and amino acid substitutions for high resolution mass spectrometry data, I present algorithmic updates the mass tolerant blind spectrum comparison tool ’MS SMiV’. I demonstrate the applicability of the changes by benchmarking against a published mass tolerant database search of a high resolution tandem mass spectrometry dataset. I then present the application of ‘MS SMiV’ on a panel of 50 colorectal cancer cell lines. I show that the adaption of ‘MS SMiV’ outperforms traditional sequence database based identification of single amino acid variants. Furthermore, I highlight the utility of mass tolerant spectrum matching in combination with isobaric labelled quantitative proteomics in distinguishing between post-translational modifications and amino acid variants of similar mass. In the last part of this work I integrate both tools with a high-throughput proteogenomic identification pipeline and apply it to a pilot study of chondrocytes derived from 12 osteoarthritic individuals. I show the value of this approach in identifying variation between individuals and molecular levels and highlight them with individual examples. I show that multi-plexed proteogenomics can be used to infer genotypes of individuals. University of Cambridge 2018-07-21 Thesis Doctoral Doctor of Philosophy (PhD) en This work was supported by NIH grant ( U41HG007234 ) to the GENCODE project and Wellcome Trust grant ( WT098051 ) to the Sanger Institute. https://www.repository.cam.ac.uk/handle/1810/275137 https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/05d9c735-8007-47f4-b503-377b87906815/download bad34e89f8d847f86ed2fa63747d07de https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/b6509f75-0ada-4e85-af9b-fa37705e9c38/download 87eda9de84448d1f82354d60eee3eb5f https://creativecommons.org/licenses/by-nc-nd/4.0/ proteogenomics proteomics genomics bioinformatics open-source software annotation mass spectrometry personalised profiling personalised medicine large-scale post-translational modification amino acid variant single nucleotide variant isoform osteoarthritis workflow analysis computational biology spectrum library searching spectral similarity