Biocomputing Group, Department of Pharmacy and Biotechnology - Interdepartmental Centre 'L. Galvani' for Integrated Studies of Bioinformatics, Biophysics and Biocomplexity, University of Bologna, 40126 Bologna, Italy.
Department of Comparative Biomedicine and Food Science (BCA), University of Padova, Padova, Italy.
Bioinformatics. 2018 May 15;34(10):1690-1696. doi: 10.1093/bioinformatics/btx818.
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.
Supplementary data are available at Bioinformatics online.
在蛋白质序列中识别信号肽是蛋白质定位和功能特征分析的重要步骤。
本文提出了 DeepSig，这是一种基于深度学习方法的信号肽检测和切割位点预测的改进方法。在一个更新的独立蛋白质数据集上进行的比较基准测试表明，DeepSig 是目前表现最好的方法，在信号肽检测和精确切割位点识别方面均优于其他可用的最先进方法。
DeepSig 可作为独立程序和网络服务器使用，网址为 https://deepsig.biocomp.unibo.it。本研究中使用的所有数据集均可从同一网站获得。
补充数据可在“Bioinformatics”在线获取。
