Geptop 2.0: Accurately Select Essential Genes from the List of Protein-Coding Genes in Prokaryotic Genomes

Qing-Feng Wen; Wen Wei; Feng-Biao Guo

doi:10.1007/978-1-0716-1720-5_23

Geptop 2.0: Accurately Select Essential Genes from the List of Protein-Coding Genes in Prokaryotic Genomes

Methods Mol Biol. 2022:2377:423-430. doi: 10.1007/978-1-0716-1720-5_23.

Authors

Qing-Feng Wen¹, Wen Wei¹, Feng-Biao Guo²

Affiliations

¹ School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
² School of Pharmaceutical Sciences, Wuhan University, Wuhan, China. fbguoy@whu.edu.cn.

PMID: 34709630
DOI: 10.1007/978-1-0716-1720-5_23

Abstract

Computational tool composites alternative way to identify essential genes and it is low-cost and time-efficient. Based on experimental essentiality sets deposited in the databases DEG and OGEE as reference, we developed an automatically computational tool named Geptop to select essential genes from the set of protein-coding genes in a prokaryotic genome, which utilizes the strategy of reciprocally best hit for homology search and evolutionary distance for weight assigning. The latest version of Geptop is 2.0 ( http://guolab.whu.edu.cn/geptop ), which can predict gene essentiality with the mean AUC 0f 0.84 in prokaryotes and is more stable. The chapter is to briefly introduce the tool and tell how to use it.

Keywords: Essential genes; Geptop 2.0; Input and output; Prediction tools.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Computational Biology
Genes, Essential* / genetics
Genome, Bacterial
Prokaryotic Cells*