Geptop 2.0: An Updated, More Precise, and Faster Geptop Server for Identification of Prokaryotic Essential Genes.

Qing-Feng Wen,Yi-Zhou Gao,Feng-Biao Guo,Chuan Dong,Shuo Liu,Hai-Xia Guo

doi:10.3389/fmicb.2019.01236

Abstract

Geptop has performed effectively in the identification of prokaryotic essential genes since its first release in 2013. It estimates gene essentiality for prokaryotes based on orthology and phylogeny. Genome-scale essentiality data of more prokaryotic species are available, and the information has been collected into public essential gene repositories such as DEG and OGEE. A faster and more accurate toolkit is needed to meet the increasing prokaryotic genome data. We updated Geptop by supplementing more validated essentiality data into reference set (from 19 to 37 species), and introducing multi-process technology to accelerate the computing speed. Compared with Geptop 1.0 and other gene essentiality prediction models, Geptop 2.0 can generate more stable predictions and finish the computation in a shorter time. The software is available both as an online server and a downloadable standalone application. We hope that the improved Geptop 2.0 will facilitate researches in gene essentiality and the development of novel antibacterial drugs. The gene essentiality prediction tool is available at http://cefg.uestc.cn/geptop.

Highlights

Essential genes are critical for the survival and development of organisms (Mushegian and Koonin, 1996)
Information about gene essentiality was obtained from DEG or OGEE, and the complete protein coding sequences of all 40 bacteria were acquired from GenBank
After obtaining the 40 area under the curve (AUC) scores calculated based on the real essentiality annotation and the predicted essentiality score, we only selected those genomes whose AUC scores were higher than 0.60 as the final reference set

Summary

Introduction

Essential genes are critical for the survival and development of organisms (Mushegian and Koonin, 1996). Due to the cost and difficulties of experiments, computational identification of essential genes presents an important alternative approach (Mobegi et al, 2017). Features including evolutionary conservation (Nigatu et al, 2017; Dilucca et al, 2018), domain information (Lu et al, 2015), network topology (Jeong et al, 2001; Zhang et al, 2016; Karthik et al, 2018; Li et al, 2019), function (Lei and Yang, 2018), and expression level (Dong et al, 2018) are used in predicting gene essentiality via the approaches of bioinformatics.

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Conclusion