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Mass spectrometric data mining for protein sequences

Part 4. Bioinformatics

4.5. Computational Methods for High-throughput Genetic Analysis: Expression Profiling

Specialist Review

  1. Christian Cole,
  2. Patrick J. Lester,
  3. Simon J. Hubbard

Published Online: 15 APR 2005

DOI: 10.1002/047001153X.g405207

Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics

Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics

How to Cite

Cole, C., Lester, P. J. and Hubbard, S. J. 2005. Mass spectrometric data mining for protein sequences. Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics. 4:4.5:56.

Author Information

  1. University of Manchester, Manchester, UK

Publication History

  1. Published Online: 15 APR 2005

Abstract

The identification of proteins from characteristic mass spectra underpins much of proteome science, using various experimental and bioinformatic strategies to match the analytical data to protein sequences in the databases. Typically, this relies on bioinformatic approaches that are able to reconcile mass spectrometric data with possible peptide sequences, either in a database or de novo, in order to identify the protein(s) under study. A variety of bioinformatic search tools are available for this, using a range of approaches to search, score, and assess the significance of potential matches. These approaches are reviewed in this article and placed into the context of modern proteomics, including potential future developments and directions in which the field is moving.

Keywords:

  • proteomics;
  • bioinformatics;
  • peptide mass fingerprint;
  • database searching;
  • tandem mass spectrometry