Proteogenomics for Personalised Molecular Profiling

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.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2018

Author dc:creator

• Schlaffner, Christoph Norbert

Advisors dc:contributor.advisor

• Bender, Andreas
• Choudhary, Jyoti

• 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

Licence

• https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/b6509f75-0ada-4e85-af9b-fa37705e9c38/download
• https://creativecommons.org/licenses/by-nc-nd/4.0/

Language dc:language

en

Author Identifier

0000-0003-2717-3406
0000-0002-6683-7546
0000-0003-0881-5477

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/275137