High-performance peptide identification by tandem mass spectrometry allows reliable automatic data processing in proteomics
| Autor: | Samia Reffas, Alexandre Masselot, Nassima Bederr, Anne Niknejad, Lydie Bougueleret, Ghislaine Argoud-Puy, Anne Gleizes, Pierre-Antoine Rey, Isabelle Cusin, Eve Mahe, Jacques Colinge |
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| Rok vydání: | 2004 |
| Předmět: |
Proteomics
Spectrometry Mass Electrospray Ionization Time Factors Tandem mass spectrometry computer.software_genre Mass spectrometry Biochemistry Mass Spectrometry Automation Robustness (computer science) Component (UML) Humans False Positive Reactions Molecular Biology Chromatography business.industry Chemistry Identification (information) ROC Curve False positive rate Data mining business Peptides computer Algorithms |
| Zdroj: | Proteomics. 4(7) |
| ISSN: | 1615-9853 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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