MSNovo: a dynamic programming algorithm for de novo peptide sequencing via tandem mass spectrometry

Anal Chem. 2007 Jul 1;79(13):4870-8. doi: 10.1021/ac070039n. Epub 2007 Jun 6.


Tandem mass spectrometry (MS/MS) has become the experimental method of choice for high-throughput proteomics-based biological discovery. The two primary ways of analyzing MS/MS data are database search and de novo sequencing. In this paper, we present a new approach to peptide de novo sequencing, called MSNovo, which has the following advanced features. (1) It works on data generated from both LCQ and LTQ mass spectrometers and interprets singly, doubly, and triply charged ions. (2) It integrates a new probabilistic scoring function with a mass array-based dynamic programming algorithm. The simplicity of the scoring function, with only 6-10 parameters to be trained, avoids the problem of overfitting and allows MSNovo to be adopted for other machines and data sets easily. The mass array data structure explicitly encodes all possible peptides and allows the dynamic programming algorithm to find the best peptide. (3) Compared to existing programs, MSNovo predicts peptides as well as sequence tags with a higher accuracy, which is important for those applications that search protein databases using the de novo sequencing results. More specifically, we show that MSNovo outperforms other programs on various ESI ion trap data. We also show that for high-resolution data the performance of MSNovo improves significantly. Supporting Information, executable files and data sets can be found at

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Computational Biology*
  • Databases, Factual
  • Molecular Weight
  • Peptides / chemistry*
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Sequence Analysis / methods*
  • Sequence Analysis / statistics & numerical data
  • Tandem Mass Spectrometry / methods*
  • Tandem Mass Spectrometry / statistics & numerical data


  • Peptides