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Bioinformatics Advance Access published online on August 12, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn422
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

High Performance Signal Peptide Prediction Based on Sequence Alignment Techniques

Karl Frank a and Manfred J. Sippl a,*

aCenter of Applied Molecular Engineering, University of Salzburg, Jakob-Haringerstraße 5, 5020 Salzburg, Austria

*To whom correspondence should be addressed. Manfred J. Sippl, E-mail: sippl{at}came.sbg.ac.at


  Abstract

Summary: 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

Contact: karl{at}frank.name, sippl{at}came.sbg.ac.at

Associate Editor: Dr. Limsoon Wong

Received on June 6, 2008; revised on August 5, 2008; accepted on August 7, 2008

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