Author(s): Colinge J, Masselot A, Cusin I, Mah E, Niknejad A,
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Abstract 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.
This article was published in Proteomics
and referenced in Journal of Proteomics & Bioinformatics