Data and text mining Advance Access publication September 27, 2013

Motivation: Tandem mass spectrometry provides the means to match mass spectrometry signal observations with the chemical entities that generated them. The technology produces signal spectra that contain information about the chemical dissociation pattern of a peptide that was forced to fragment using methods like collision-induced dissoci-ation. The ability to predict these MS2 signals and to understand this fragmentation process is important for sensitive high-throughput proteomics research. Results: We present a new tool called MS2PIP for predicting the in-tensity of the most important fragment ion signal peaks from a peptide sequence. MS2PIP pre-processes a large dataset with confident pep-tide-to-spectrum matches to facilitate data-driven model induction using a random forest regression learning algorithm. The intensity pre-dictions of MS2PIP were evaluated on several independent evaluation sets and found to correlate significantly better with the observed frag-ment-ion intensities as compared with the current state-of-the-art PeptideART tool. Availability: MS2PIP code is available for both training and predictin