Assessing peptide de novo sequencing algorithms performance on large and diverse data sets

Erik Pitzer; Alexandre Masselor; Jacques Colinge

doi:10.1002/pmic.200700224

Assessing peptide de novo sequencing algorithms performance on large and diverse data sets

Erik Pitzer, Alexandre Masselor, Jacques Colinge

Research Center Hagenberg

Research output: Contribution to journal › Article › peer-review

31 Citations (Scopus)

Abstract

De novo peptide sequencing algorithms are often tested on relatively small data sets made of excellent spectra. Since there are always more and more tandem mass spectra available, we have assembled six large, reliable, and diverse (three mass spectrometer types) data sets intended for such tests and we make them accessible via a web server. To exemplify their use we investigate the performance of Lutefisk, PepNovo, and PepNovoTag, three well-established peptide de novo sequencing programs.

Original language	English
Pages (from-to)	3051-3054
Number of pages	4
Journal	PROTEOMICS
Volume	7
Issue number	17
DOIs	https://doi.org/10.1002/pmic.200700224
Publication status	Published - Sept 2007

Keywords

Algorithm
Bioinformatics
De novo sequencing
Amino Acid Sequence
Sequence Analysis, Protein/methods
Humans
Tandem Mass Spectrometry/methods
Peptide Fragments/chemistry
Computational Biology/methods
Algorithms
Animals
Sequence Alignment/methods
Proteomics/methods

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10.1002/pmic.200700224

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KW - Algorithm

KW - Bioinformatics

KW - De novo sequencing

KW - Amino Acid Sequence

KW - Sequence Analysis, Protein/methods

KW - Humans

KW - Tandem Mass Spectrometry/methods

KW - Peptide Fragments/chemistry

KW - Computational Biology/methods

KW - Algorithms

KW - Animals

KW - Sequence Alignment/methods

KW - Proteomics/methods

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ER -

Assessing peptide de novo sequencing algorithms performance on large and diverse data sets

Abstract

Keywords

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