Award details

Improved identification of proteins from fragment ion spectra using machine learning in proteomics

Reference	EGM17685
Principal Investigator / Supervisor	Professor Simon Hubbard
Co-Investigators / Co-Supervisors	Professor Simon James Gaskell, Dr Josip Lovric, Dr Benjamin Stapley, Professor Hujun Yin
Institution	The University of Manchester
Department	Life Sciences
Funding type	Research
Value (£)	348,156
Status	Completed
Type	Research Grant
Start date	01/04/2003
End date	31/12/2006
Duration	45 months

Abstract

Proteome protein identification using mass spectrometry underpins modern proteome science in the study of the functional entities in the cell. This project will use a collected dataset of tandem mass spectrometric peptide data, augmented by newly acquired spectra on peptides synthesised for the project. From this, we aim to develop improved computational methods to infer the maximum peptide sequence information from fragment ion spectra. These algorithms will use the results of machine learning and other techniques in a dynamics programming context for database searching and de novo sequencing. This will result in more confident and complete protein sequence identification, which will consequently improve protein identification throughput and aid the identification of post-translational modifications.

Summary

unavailable

Committee	Closed Committee - Engineering & Biological Systems (EBS)
Research Topics	X – not assigned to a current Research Topic
Research Priority	X – Research Priority information not available
Research Initiative	Exploiting Genomics: Manufacturing & New Post Tech (EGM) [2001]
Funding Scheme	X – not Funded via a specific Funding Scheme

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