Abstract
Abstract Summary: Although the HMMER package is widely used to produce profile hidden Markov models (profile HMMs) for protein domains, it has been difficult to create a profile HMM for signal peptides. Here we describe an approach for building a complex model of eukaryotic signal peptides by the standard HMMER package. Signal peptide prediction with this model gives a 95.6% sensitivity and 95.7% specificity. Availability: The profile HMM for signal peptides, data sets, and the scripts for analyzing data are available for non-commercial use at http://share.gene.com/. Contact: zemin@gene.com * To whom correspondence should be addressed.
Keywords
Hidden Markov modelScripting languageSIGNAL (programming language)Computer scienceMarkov chainSensitivity (control systems)Signal peptideMarkov modelR packageArtificial intelligencePattern recognition (psychology)Machine learningBiologyProgramming languageGenePeptide sequenceGeneticsElectronic engineeringEngineering
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Publication Info
- Year
- 2003
- Type
- article
- Volume
- 19
- Issue
- 2
- Pages
- 307-308
- Citations
- 113
- Access
- Closed
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Cite This
Zemin Zhang,
William I. Wood
(2003).
A profile hidden Markov model for signal peptides generated by HMMER.
Bioinformatics
, 19
(2)
, 307-308.
https://doi.org/10.1093/bioinformatics/19.2.307
Identifiers
- DOI
- 10.1093/bioinformatics/19.2.307