Performance Evaluation of Existing De Novo Sequencing Algorithms

peer-reviewed · Journal of Proteome Research · 2006

peer-reviewed · Journal of Proteome Research · 2006. Sergey Pevtsov et al. Two methods have been developed for protein identification from tandem mass spectra: database searching and…
Date 2006-11-01
Type peer-reviewed
Venue Journal of Proteome Research
Publisher American Chemical Society (ACS)
Contribution benchmark
DOI 10.1021/pr060222h
Citations (OpenAlex) 104
Venue 2-year citedness 3.83

Abstract

Two methods have been developed for protein identification from tandem mass spectra: database searching and de novo sequencing. De novo sequencing identifies peptide directly from tandem mass spectra. Among many proposed algorithms, we evaluated the performance of the five de novo sequencing algorithms, AUDENS, Lutefisk, NovoHMM, PepNovo, and PEAKS. Our evaluation methods are based on calculation of relative sequence distance (RSD), algorithm sensitivity, and spectrum quality. We found that de novo sequencing algorithms have different performance in analyzing QSTAR and LCQ mass spectrometer data, but in general, perform better in analyzing QSTAR data than LCQ data. For the QSTAR data, the performance order of the five algorithms is PEAKS > Lutefisk, PepNovo > AUDENS, NovoHMM. The performance of PEAKS, Lutefisk, and PepNovo strongly depends on the spectrum quality and increases with an increase of spectrum quality. However, AUDENS and NovoHMM are not sensitive to the spectrum quality. Compared with other four algorithms, PEAKS has the best sensitivity and also has the best performance in the entire range of spectrum quality. For the LCQ data, the performance order is NovoHMM > PepNovo, PEAKS > Lutefisk > AUDENS. NovoHMM has the best sensitivity, and its performance is the best in the entire range of spectrum quality. But the overall performance of NovoHMM is not significantly different from the performance of PEAKS and PepNovo. AUDENS does not give a good performance in analyzing either QSTAR and LCQ data.

Authors

  1. Sergey Pevtsov · Lomonosov Moscow State University, Purdue University
  2. Irina Fedulova · Lomonosov Moscow State University, Purdue University
  3. Hamid Mirzaei · Lomonosov Moscow State University, Purdue University
  4. Charles R. Buck · Lomonosov Moscow State University, Purdue University, Purdue University West Lafayette
  5. Xiang Zhang (Purdue) · Lomonosov Moscow State University, Purdue University, Purdue University West Lafayette

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