Sequence optimization as an alternative to de novo analysis of tandem mass spectrometry data

Motivation: Peptide identification following tandem mass spectrometry (MS/MS) is usually achieved by searching for the best match between the mass spectrum of an unidentified peptide and model spectra generated from peptides in a sequence database. This methodology will be successful only if the pep...

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Bibliographic Details
Published in:Bioinformatics 2004-09, Vol.20 (14), p.2296-2304
Main Authors: Heredia-Langner, Alejandro, Cannon, William R., Jarman, Kenneth D., Jarman, Kristin H.
Format: Article
Language:English
Subjects:
Algorithms
Amino Acid Sequence
Biological and medical sciences
Complex Mixtures - analysis
Complex Mixtures - chemistry
Fundamental and applied biological sciences. Psychology
General aspects
Mass Spectrometry - methods
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Molecular Sequence Data
Peptides - analysis
Peptides - chemistry
Sequence Alignment - methods
Sequence Analysis, Protein - methods
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Summary:Motivation: Peptide identification following tandem mass spectrometry (MS/MS) is usually achieved by searching for the best match between the mass spectrum of an unidentified peptide and model spectra generated from peptides in a sequence database. This methodology will be successful only if the peptide under investigation belongs to an available database. Our objective is to develop and test the performance of a heuristic optimization algorithm capable of dealing with some features commonly found in actual MS/MS spectra that tend to stop simpler deterministic solution approaches. Results: We present the implementation of a Genetic Algorithm (GA) in the reconstruction of amino acid sequences using only spectral features, discuss some of the problems associated with this approach and compare its performance to a de novo sequencing method. The GA can potentially overcome some of the most problematic aspects associated with de novo analysis of real MS/MS data such as missing or unclearly defined peaks and may prove to be a valuable tool in the proteomics field. We assess the performance of our algorithm under conditions of perfect spectral information, in situations where key spectral features are missing, and using real MS/MS spectral data.
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/bth242