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DeepSig: deep learning improves signal peptide detection in proteins.

ABSTRACT: The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization.Here, we present DeepSig, an improved approach for signal peptide detection and cleavage-site prediction based on deep learning methods. Comparative benchmarks performed on an updated independent dataset of proteins show that DeepSig is the current best performing method, scoring better than other available state-of-the-art approaches on both signal peptide detection and precise cleavage-site identification.DeepSig is available as both standalone program and web server at https://deepsig.biocomp.unibo.it. All datasets used in this study can be obtained from the same website.pierluigi.martelli@unibo.it.Supplementary data are available at Bioinformatics online.

SUBMITTER: Savojardo C

PROVIDER: S-EPMC5946842 | biostudies-other | 2018 May

REPOSITORIES: biostudies-other

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DeepSig: deep learning improves signal peptide detection in proteins.

Savojardo Castrense C Martelli Pier Luigi PL Fariselli Piero P Casadio Rita R

Bioinformatics (Oxford, England) 20180501 10

<h4>Motivation</h4>The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization.<h4>Results</h4>Here, we present DeepSig, an improved approach for signal peptide detection and cleavage-site prediction based on deep learning methods. Comparative benchmarks performed on an updated independent dataset of proteins show that DeepSig is the current best performing method, scoring better than other available state-of-the-art a ...[more]

PMID: 29280997

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