DeepSig: deep learning improves signal peptide detection in proteins
Bioinformatics, 2018•academic.oup.com
Motivation The identification of signal peptides in protein sequences is an important step
toward protein localization and function characterization. Results 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 …
toward protein localization and function characterization. Results 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 …
Motivation
The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization.
Results
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.
Availability and implementation
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.
Supplementary information
Supplementary data are available at Bioinformatics online.
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