title: High-performance signal peptide prediction based on sequence alignment techniques.
abstract: 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.
  
  
  BACKGROUND
  Signal-BLAST is available online at http://sigpep.services.came.sbg.ac.at/signalblast.html.
authors.affiliation: Center of Applied Molecular Engineering, University of Salzburg, Jakob-Haringerstrasse 5, 5020 Salzburg, Austria.
authors.forename: Karl
authors.initials: K
authors.lastname: Frank
authors.forename: Manfred J
authors.initials: MJ
authors.lastname: Sippl
mesh_terms: D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D021382:Protein Sorting Signals; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein
chemical_list: D021382:Protein Sorting Signals