title: Performance evaluation of existing de novo sequencing algorithms.
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.affiliation: Department of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Moscow 119992.
authors.forename: Sergey
authors.initials: S
authors.lastname: Pevtsov
authors.forename: Irina
authors.initials: I
authors.lastname: Fedulova
authors.forename: Hamid
authors.initials: H
authors.lastname: Mirzaei
authors.forename: Charles
authors.initials: C
authors.lastname: Buck
authors.forename: Xiang
authors.initials: X
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000818:Animals; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods; D009113:Muramidase / Q000737:chemistry / Q000302:isolation & purification; D010446:Peptide Fragments / Q000737:chemistry; D040901:Proteomics / Q000379:methods*; D017386:Sequence Homology, Amino Acid; D012709:Serum Albumin / Q000737:chemistry / Q000302:isolation & purification
chemical_list: D010446:Peptide Fragments; D012709:Serum Albumin; D009113:Muramidase