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Development of computational methods for analysing proteomic data for genome annotation

Development of computational methods for analysing proteomic data for genome annotation


Darwin College, University of Cambridge and The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UNITED KINGDOM.


Current functional genomics relies on known and characterised genes, but despite significant efforts in the field of genome annotation, accurate identification and elucidation of protein coding gene structures remains challenging. Methods are limited to computational predictions and transcript-level experimental evidence, hence translation cannot be verified. Proteomic mass spectrometry is a method that enables sequencing of gene product fragments, enabling the validation and refinement of existing gene annotation as well as the detection of novel protein coding regions. However, the application of proteomics data to genome annotation is hindered by the lack of suitable tools and methods to achieve automatic data processing and genome mapping at high accuracy and throughput. The main objectives of this work are to address these issues and to demonstrate the applicability in a pilot study that validates and refines annotation of Mus musculus.

In the first part of this project I evaluate the scoring schemes of \Mascot", which is a peptide identification software that is routinely used, for low and high mass accuracy data and show these to be not sufficiently accurate. I develop an alternative scoring method that provides more sensitive peptide identification specifically for high accuracy data, while allowing the user to fix the false discovery rate.

Building upon this, I utilise the machine learning algorithm “Percolator” to further extend my Mascot scoring scheme with a large set of orthogonal scoring features that assess the quality of a peptide-spectrum match. I demonstrate very good sensitivity with this approach and highlight the importance of reliable and robust peptide-spectrum match significance measures.

To close the gap between high throughput peptide identification and large scale genome annotation analysis I introduce a proteogenomics pipeline. A comprehensive database is the central element of this pipeline, enabling the efficient mapping of known and predicted peptides to their genomic loci, each of which is associated with supplemental annotation information such as gene and transcript identifiers. Software scripts allow the creation of automated genome annotation analysis reports.

In the last part of my project the pipeline is applied to a large mouse MS dataset. I show the value and the level of coverage that can be achieved for validating genes and gene structures, while also highlighting the limitations of this technique. Moreover, I show where peptide identifications facilitated the correction of existing annotation, such as re-defining the translated regions or splice boundaries. Moreover, I propose a set of novel genes that are identified by the MS analysis pipeline with high confidence, but largely lack transcriptional or conservational evidence.