Interdisciplinary Sciences: Computational Life Sciences ( IF 3.9 ) Pub Date : 2021-05-19 , DOI: 10.1007/s12539-021-00439-2 Hugo López-Fernández 1, 2, 3, 4, 5 , Cristina P Vieira 4, 5 , Pedro Ferreira 4, 5 , Paula Gouveia 4, 5 , Florentino Fdez-Riverola 1, 2, 3 , Miguel Reboiro-Jato 1, 2, 3 , Jorge Vieira 4, 5
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The identification of clinically relevant bacterial amino acid changes can be performed using different methods aimed at the identification of genes showing positively selected amino acid sites (PSS). Nevertheless, such analyses are time consuming, and the frequency of genes showing evidence for PSS can be low. Therefore, the development of a pipeline that allows the quick and efficient identification of the set of genes that show PSS is of interest. Here, we present Auto-PSS-Genome, a Compi-based pipeline distributed as a Docker image, that automates the process of identifying genes that show PSS using three different methods, namely codeML, FUBAR, and omegaMap. Auto-PSS-Genome accepts as input a set of FASTA files, one per genome, containing all coding sequences, thus minimizing the work needed to conduct positively selected sites analyses. The Auto-PSS-Genome pipeline identifies orthologous gene sets and corrects for multiple possible problems in input FASTA files that may prevent the automated identification of genes showing PSS. A FASTA file containing all coding sequences can also be given as an external global reference, thus easing the comparison of results across species, when gene names are different. In this work, we use Auto-PSS-Genome to analyse Mycobacterium leprae (that causes leprosy), and the closely related species M. haemophilum, that mainly causes ulcerating skin infections and arthritis in persons who are severely immunocompromised, and in children causes cervical and perihilar lymphadenitis. The genes identified in these two species as showing PSS may be those that are partially responsible for virulence and resistance to drugs.
Graphic Abstract
中文翻译：
使用 Auto-PSS-Genome 在全基因组水平上鉴定临床相关的细菌氨基酸变化
临床相关细菌氨基酸变化的鉴定可以使用不同的方法进行，这些方法旨在鉴定显示阳性选择氨基酸位点 (PSS) 的基因。然而，这样的分析非常耗时，并且显示 PSS 证据的基因频率可能很低。因此，开发允许快速有效地识别显示 PSS 的基因组的管道是有意义的。在这里，我们展示了 Auto-PSS-Genome，这是一个基于 Compi 的管道，作为 Docker 映像分发，它使用三种不同的方法（即 codeML、FUBAR 和 omegaMap）自动识别显示 PSS 的基因的过程。Auto-PSS-Genome 接受一组 FASTA 文件作为输入，每个基因组一个，包含所有编码序列，从而最大限度地减少进行积极选择位点分析所需的工作。Auto-PSS-Genome 管道识别直系同源基因集并纠正输入 FASTA 文件中可能阻止自动识别显示 PSS 的基因的多个可能问题。包含所有编码序列的 FASTA 文件也可以作为外部全局参考给出，从而在基因名称不同时简化跨物种结果的比较。在这项工作中，我们使用 Auto-PSS-Genome 来分析麻风分枝杆菌（导致麻风病）和密切相关的嗜血杆菌属，主要导致严重免疫功能低下的人发生溃疡性皮肤感染和关节炎，在儿童中导致颈部和肺门周围淋巴结炎。在这两个物种中鉴定出的显示 PSS 的基因可能是那些对毒力和抗药性负有部分责任的基因。
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