SPEPlip: the detection of signal peptide and lipoprotein cleavage sites

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Bibliographische Detailangaben
Zeitschriftentitel:	Bioinformatics
Personen und Körperschaften:	Fariselli, Piero, Finocchiaro, Giacomo, Casadio, Rita
In:	Bioinformatics, 19, 2003, 18, S. 2498-2499
Format:	E-Article
Sprache:	Englisch
veröffentlicht:	Oxford University Press (OUP)
Schlagwörter:	Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability

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Zusammenfassung:	<jats:title>Abstract</jats:title> <jats:p>Summary: SPEPlip is a neural network-based method, trained and tested on a set of experimentally derived signal peptides from eukaryotes and prokaryotes. SPEPlip identifies the presence of sorting signals and predicts their cleavage sites. The accuracy in cross-validation is similar to that of other available programs: the rate of false positives is 4 and 6%, for prokaryotes and eukaryotes respectively and that of false negatives is 3% in both cases. When a set of 409 prokaryotic lipoproteins is predicted, SPEPlip predicts 97% of the chains in the signal peptide class. However, by integrating SPEPlip with a regular expression search utility based on the PROSITE pattern, we can successfully discriminate signal peptide-containing chains from lipoproteins. We propose the method for detecting and discriminating signal peptides containing chains and lipoproteins.</jats:p> <jats:p>Availability: It can be accessed through the web page at http://gpcr.biocomp.unibo.it/predictors/</jats:p>
Umfang:	2498-2499
ISSN:	1367-4811 1367-4803
DOI:	10.1093/bioinformatics/btg360