MSNovo: A Dynamic Programming Algorithm for de Novo Peptide Sequencing via Tandem Mass Spectrometry

peer-reviewed · Analytical Chemistry · 2007

peer-reviewed · Analytical Chemistry · 2007. Lijuan Mo et al. Tandem mass spectrometry (MS/MS) has become the experimental method of choice for high-throughput…
Date 2007-06-15
Type peer-reviewed
Venue Analytical Chemistry
Publisher American Chemical Society
Contribution algorithm
DOI 10.1021/ac070039n
Citations (OpenAlex) 94
Venue 2-year citedness 6.29

Abstract

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 http://msms.usc.edu/supplementary/msnovo.

Authors

  1. Lijuan Mo · University of Southern California
  2. Debojyoti Dutta · University of Southern California
  3. Yunhu Wan · University of Southern California
  4. Ting Chen · Harvard University, University of Southern California

Methods and tools

  • MSNovo: Mass-array dynamic programming

Cites (9)

Cited by (20)

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