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三种用于预测细菌必需基因的计算工具。

Three computational tools for predicting bacterial essential genes.

作者信息

Guo Feng-Biao, Ye Yuan-Nong, Ning Lu-Wen, Wei Wen

机构信息

Computational, Comparative, Evolutionary and Functional Genomics Group (CEFG), School of Life Science and Technology, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, 610054, China,

出版信息

Methods Mol Biol. 2015;1279:205-17. doi: 10.1007/978-1-4939-2398-4_13.

Abstract

Essential genes are those genes indispensable for the survival of any living cell. Bacterial essential genes constitute the cornerstones of synthetic biology and are often attractive targets in the development of antibiotics and vaccines. Because identification of essential genes with wet-lab ways often means expensive economic costs and tremendous labor, scientists changed to seek for alternative way of computational prediction. Aiming to help to solve this issue, our research group (CEFG: group of Computational, Comparative, Evolutionary and Functional Genomics, http://cefg.uestc.edu.cn) has constructed three online services to predict essential genes in bacterial genomes. These freely available tools are applicable for single gene sequences without annotated functions, single genes with definite names, and complete genomes of bacterial strains. To ensure reliable predictions, the investigated species should belong to the same family (for EGP) or phylum (for CEG_Match and Geptop) with one of the reference species, respectively. As the pilot software for the issue, predicting accuracies of them have been assessed and compared with existing algorithms, and note that all of other published algorithms have not any formed online services. We hope these services at CEFG will help scientists and researchers in the field of essential genes.

摘要

必需基因是指任何活细胞生存所不可或缺的那些基因。细菌必需基因构成了合成生物学的基石,并且在抗生素和疫苗研发中常常是有吸引力的靶点。由于通过湿实验方法鉴定必需基因往往意味着高昂的经济成本和巨大的人力投入,科学家们转而寻求计算预测的替代方法。为了帮助解决这个问题,我们的研究团队(CEFG:计算、比较、进化和功能基因组学团队,http://cefg.uestc.edu.cn)构建了三个在线服务来预测细菌基因组中的必需基因。这些免费可用的工具适用于无注释功能的单基因序列、有明确名称的单基因以及细菌菌株的完整基因组。为确保预测可靠,所研究的物种应分别与其中一个参考物种属于同一科(对于EGP)或同一门(对于CEG_Match和Geptop)。作为该问题的先导软件,已对它们的预测准确性进行了评估,并与现有算法进行了比较,且注意到所有其他已发表的算法都没有形成任何在线服务。我们希望CEFG的这些服务能帮助必需基因领域的科学家和研究人员。

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