SUMMARY: The performance of sequence database search methods is usually judged by receiver operating characteristic (ROC) analysis. The proper interpretation of the results obtained and a fair comparison across different methods critically depends on the properties of the data set used for such an analysis; in particular, each query must have the same number of true positives and true negatives. Here, we present a novel web service based on a dataset specifically designed for ROC analysis and the investigation of alignment quality. The data set is derived from a quantitative classification of protein structures (COPS), while analysis and results are presented through an intuitive web interface. The analysis provides details such as false positives per query, and visualization of the structural similarity between query and targets. Most importantly, results obtained for a specific alignment method are immediately related to those obtained for several popular standard sequence alignment methods.

UNLABELLED: The accuracy of current signal peptide predictors is outstanding. The most successful predictors are based on neural networks and hidden Markov models, reaching a sensitivity of 99% and an accuracy of 95%. Here, we demonstrate that the popular BLASTP alignment tool can be tuned for signal peptide prediction reaching the same high level of prediction success. Alignment-based techniques provide additional benefits. In spite of high success rates signal peptide predictors yield false predictions. Simple sequences like polyvaline, for example, are predicted as signal peptides. The general architecture of learning systems makes it difficult to trace the cause of such problems. This kind of false predictions can be recognized or avoided altogether by using sequence comparison techniques. Based on these results we have implemented a public web service, called Signal-BLAST. Predictions returned by Signal-BLAST are transparent and easy to analyze. AVAILABILITY: Signal-BLAST is available online at http://sigpep.services.came.sbg.ac.at/signalblast.html.