The target-decoy search strategy has been successfully applied in shotgun proteomics for validating peptide and protein identifications. If, on one hand, this method has proven to be very efficient for error estimation, on the other hand, little attention has been paid to the resulting sensitivity. Only two scores are normally used and thresholds are explored in a very simplistic way. In this work, a multivariate decoy analysis is described, where many quality parameters are considered. This analysis is treated in our approach as an optimization problem for sensitivity maximization. Furthermore, an efficient heuristic is proposed to solve this problem. Experiments comparing our method, termed MUDE (multivariate decoy database analysis), with traditional bivariate decoy analysis and with Peptide/ProteinProphet showed that our procedure significantly enhances the retrieved number of identifications when comparing the same false discovery rates. Particularly for phosphopeptide/protein identifications, we could demonstrate more than a two-fold increase in sensitivity compared with the Trans-Proteomic Pipeline tools.

The significant accuracy demonstrated by our predictive model shows that NICeSim might be used for hypothesis testing to minimize in vivo experiments. We observed that the model delivers predictions that are in very good agreement with the literature, demonstrating that NICeSim might be an important tool for supporting decision making in medical practice. Other very important characteristics of NICeSim are its flexibility and dynamism. NICeSim is flexible because it allows the inclusion and deletion of variables according to the requirements of a particular study. It is also dynamic because it trains a just-in-time model. Therefore, the system is improved as data from new patients become available. Finally, NICeSim can be extended in a cooperative manner because it is an open-source system.