A New Parameterized Algorithm for Rapid Peptide Sequencing

De novo sequencing is an important computational approach to determining the amino acid sequence of a peptide with tandem mass spectrometry (MS/MS). Most of the existing approaches use a graph model to describe a spectrum and the sequencing is performed by computing the longest antisymmetric path in...

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Veröffentlicht in:PloS one Jg. 9; H. 2; S. e87476
1. Verfasser: Song, Yinglei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 14.02.2014
Public Library of Science (PLoS)
Schlagworte:
Accuracy
Algorithms
Amino Acid Sequence
Amino acids
Analysis
Bioinformatics
Biology
Computer applications
Computer Science
Computer Simulation
Computers
Decomposition
Efficiency
Graphs
Identification
Mass spectrometry
Mass spectroscopy
Mathematics
Missing mass (astrophysics)
Molecular Sequence Data
Mutation
Nucleotide sequence
Parameterization
Peptides
Peptides - chemistry
Proteins
Proteomics
Scientific imaging
Sequence Analysis, Protein - methods
Tandem Mass Spectrometry
Time Factors
ISSN:1932-6203, 1932-6203
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Zusammenfassung:De novo sequencing is an important computational approach to determining the amino acid sequence of a peptide with tandem mass spectrometry (MS/MS). Most of the existing approaches use a graph model to describe a spectrum and the sequencing is performed by computing the longest antisymmetric path in the graph. The task is often computationally intensive since a given MS/MS spectrum often contains noisy data, missing mass peaks, or post translational modifications/mutations. This paper develops a new parameterized algorithm that can efficiently compute the longest antisymmetric partial path in an extended spectrum graph that is of bounded path width. Our testing results show that this algorithm can efficiently process experimental spectra and provide sequencing results of high accuracy.
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Competing Interests: The author has declared that no competing interests exist.
Conceived and designed the experiments: YS. Analyzed the data: YS. Contributed reagents/materials/analysis tools: YS. Wrote the paper: YS.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0087476