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

Erik Pitzer, Alexandre Masselot et Jacques Colinge
Proteomics (Weinheim), Vol.7(17), pp.3051-3054
01/09/2007
PMID: 17683051

Résumé

Algorithms Amino Acid Sequence Animals Computational Biology - methods Humans Peptide Fragments - chemistry Proteomics - methods Sequence Alignment - methods Sequence Analysis, Protein - methods Tandem Mass Spectrometry - methods
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.

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