MOTIVATION: Tandem mass spectrometry (MS/MS) is a dominant approach for 
large-scale high-throughput post-translational modification (PTM) profiling. 
Although current state-of-the-art blind PTM spectral analysis algorithms can 
predict thousands of modified peptides (PTM predictions) in an MS/MS experiment, 
a significant percentage of these predictions have inaccurate modification mass 
estimates and false modification site assignments. This problem can be addressed 
by post-processing the PTM predictions with a PTM refinement algorithm. We 
developed a novel PTM refinement algorithm, iPTMClust, which extends a recently 
introduced PTM refinement algorithm PTMClust and uses a non-parametric Bayesian 
model to better account for uncertainties in the quantity and identity of PTMs 
in the input data. The use of this new modeling approach enables iPTMClust to 
provide a confidence score per modification site that allows fine-tuning and 
interpreting resulting PTM predictions.
RESULTS: The primary goal behind iPTMClust is to improve the quality of the PTM 
predictions. First, to demonstrate that iPTMClust produces sensible and accurate 
cluster assignments, we compare it with k-means clustering, mixtures of 
Gaussians (MOG) and PTMClust on a synthetically generated PTM dataset. Second, 
in two separate benchmark experiments using PTM data taken from a phosphopeptide 
and a yeast proteome study, we show that iPTMClust outperforms state-of-the-art 
PTM prediction and refinement algorithms, including PTMClust. Finally, we 
illustrate the general applicability of our new approach on a set of human 
chromatin protein complex data, where we are able to identify putative novel 
modified peptides and modification sites that may be involved in the formation 
and regulation of protein complexes. Our method facilitates accurate PTM 
profiling, which is an important step in understanding the mechanisms behind 
many biological processes and should be an integral part of any proteomic study.
AVAILABILITY: Our algorithm is implemented in Java and is freely available for 
academic use from http://genes.toronto.edu.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics 
online.