Frank Karl, Sippl Manfred J
Center of Applied Molecular Engineering, University of Salzburg, Jakob-Haringerstrasse 5, 5020 Salzburg, Austria.
Bioinformatics. 2008 Oct 1;24(19):2172-6. doi: 10.1093/bioinformatics/btn422. Epub 2008 Aug 12.
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.
Signal-BLAST is available online at http://sigpep.services.came.sbg.ac.at/signalblast.html.
当前信号肽预测器的准确性非常出色。最成功的预测器基于神经网络和隐马尔可夫模型，灵敏度达到99%，准确率达到95%。在此，我们证明流行的BLASTP比对工具可进行调整以用于信号肽预测，且能达到同样高的预测成功率。基于比对的技术还有其他优势。尽管信号肽预测器成功率很高，但仍会产生错误预测。例如，像多聚缬氨酸这样的简单序列会被预测为信号肽。学习系统的总体架构使得难以追踪此类问题的原因。通过使用序列比较技术，可以识别或完全避免这种错误预测。基于这些结果，我们实现了一个名为Signal-BLAST的公共网络服务。Signal-BLAST返回的预测结果透明且易于分析。
Signal-BLAST可在http://sigpep.services.came.sbg.ac.at/signalblast.html在线获取。
