High-performance peptide identification by tandem mass spectrometry allows reliable automatic data processing in proteomics

• Published in: Proteomics (Weinheim) Vol. 4; no. 7; pp. 1977 - 1984
• Main Authors: Colinge, Jacques, Masselot, Alexandre, Cusin, Isabelle, Mahé, Eve, Niknejad, Anne, Argoud-Puy, Ghislaine, Reffas, Samia, Bederr, Nassima, Gleizes, Anne, Rey, Pierre-Antoine, Bougueleret, Lydie
• Format: Journal Article
• Language: English
• Published: Weinheim WILEY-VCH Verlag 01.07.2004; WILEY‐VCH Verlag; Wiley-VCH
• Subjects: Algorithm; Algorithms; Analytical, structural and metabolic biochemistry; Automation; Biological and medical sciences; False Positive Reactions; Fundamental and applied biological sciences. Psychology; Humans; Identification; Mass Spectrometry - methods; Miscellaneous; Peptides - chemistry; Proteins; Proteomics - methods; ROC Curve; Spectrometry; Spectrometry, Mass, Electrospray Ionization - methods; Tandem mass spectrometry; Time Factors; Identification; Peptides; Mass spectrometry; Proteomics
• ISSN: 1615-9853; 1615-9861
• DOI: 10.1002/pmic.200300708

In a previous paper we introduced a novel model‐based approach (OLAV) to the problem of identifying peptides via tandem mass spectrometry, for which early implementations showed promising performance. We recently further improved this performance to a remarkable level (1–2% false positive rate at 95% true positive rate) and characterized key properties of OLAV like robustness and training set size. We present these results in a synthetic and coherent way along with detailed performance comparisons, a new scoring component making use of peptide amino acidic composition, and new developments like automatic parameter learning. Finally, we discuss the impact of OLAV on the automation of proteomics projects.