当前细菌对抗生素耐药性的流行，引发了对新型抗菌药物的迫切需求。由于必需基因编码生命活动中不可或缺的功能，对病原菌必需型药物靶点的研究提供了开发新药的有效途径。本项目在细菌基因组数据不断涌现以及细菌间水平基因频繁转移的背景下，从病原的系统性和动态性角度考虑，统观不同必需基因在泛病原菌中的分布。通过完善综合同源性、功能性、序列特征的必需基因识别算法，预测泛病原菌的必需基因；基于构建的泛病原菌必需基因数据库，采用生物信息学的方法和工具，系统地分析泛病原菌中必需基因的分布规律，理解各类必需基因特别是核心必需基因与物种特异必需基因的生物学意义。整合新发现的知识并进行药靶初筛，以明确必需型抗菌药物靶点的基本特征，为不同抗菌谱的必需型药靶筛选算法的设计奠定基础。此项研究的实施有助于细菌必需基因概念的阐明，亦将有助于新作用机制的抗菌药物的开发与实现。

Infectious diseases caused by various pathogenic bacteria are considered to be a major public health problem globally. The recent emergence of antibiotic-resistant strains of many pathogens has focused the research on the identification of potential drug targets. Essential genes are absolutely required for the survival of an organism and are therefore considered the foundation of life. The investigation of essential genes can be an attractive avenue for developing novel drugs. More work is needed, however, to identify the categories of essential genes and to distinguish between universally essential genes and those indispensable for the survival of a specific group of bacteria. Currently, the increasing availability of genomic data for bacteria and rapid spread of horizontal gene transfer through the microbiome have produced a new concept of 'pan-pathogenic bacteria', allowing presence of pathogenic systemic and dynamic nature. In this research, different databases and bioinformatics tools can be used to find distribution rules of essential genes in pan-pathogenic bacteria, and to explain the 'core essential genes' and 'specific essential genes' for diverse species. The newly obtained knowledge will aim to facilitate the identification and prioritization of candidate drug targets for pan-pathogens. Concretely, bacterial essential genes will be identified using a novel integrated approach of homology, functionality and sequence features. By constructing the database of essential genes from pan-pathogenic bacteria, the distribution of essential genes is able to be examined and analyzed by way of genomics and bioinformatics. The detection of bacterial essential genes that are non-homologous to human genes represents a promising means of identifying novel drug targets. This selection algorithm is based on evidence of essentiality, presence in a selective spectrum of pathogens, no homology to human proteins, druggability and protein structure prediction properties, etc. Consequently, our computational study can be exploited not only to clarify the meanings of essentiality, but also to provide the opportunity to use a knowledge-based approach for the development of new-generation targets for antibacterials.