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

Erik Pitzer, Alexandre Masselor, Jacques Colinge

Research output: Contribution to journalArticlepeer-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 languageEnglish
Pages (from-to)3051-3054
Number of pages4
JournalPROTEOMICS
Volume7
Issue number17
DOIs
Publication statusPublished - Sep 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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