Many methods have been described to predict the subcellular location of proteins from sequence information. However, most of these methods either rely on global sequence properties or use a set of known protein targeting motifs to predict protein localization. Here, we develop and test a novel method that identifies potential targeting motifs using a discriminative approach based on hidden Markov models (discriminative HMMs). These models search for motifs that are present in a compartment but absent in other, nearby, compartments by utilizing an hierarchical structure that mimics the protein sorting mechanism. We show that both discriminative motif finding and the hierarchical structure improve localization prediction on a benchmark data set of yeast proteins. The motifs identified can be mapped to known targeting motifs and they are more conserved than the average protein sequence. Using our motif-based predictions, we can identify potential annotation errors in public databases for the location of some of the proteins. A software implementation and the data set described in this paper are available from http://murphylab.web.cmu.edu/software/2009_TCBB_motif/.

MOTIVATION: Classification of tissues using static gene-expression data has received considerable attention. Recently, a growing number of expression datasets are measured as a time series. Methods that are specifically designed for this temporal data can both utilize its unique features (temporal evolution of profiles) and address its unique challenges (different response rates of patients in the same class). RESULTS: We present a method that utilizes hidden Markov models (HMMs) for the classification task. We use HMMs with less states than time points leading to an alignment of the different patient response rates. To focus on the differences between the two classes we develop a discriminative HMM classifier. Unlike the traditional generative HMM, discriminative HMM can use examples from both classes when learning the model for a specific class. We have tested our method on both simulated and real time series expression data. As we show, our method improves upon prior methods and can suggest markers for specific disease and response stages that are not found when using traditional classifiers. AVAILABILITY: Matlab implementation is available from http://www.cs.cmu.edu/~thlin/tram/.