Abstract

Orthology assignment is a key step of comparative genomic studies, for which many bioinformatic tools have been developed. However, all gene clustering pipelines are based on the analysis of protein distances, which are subject to many artifacts. In this article, we introduce Broccoli, a user-friendly pipeline designed to infer, with high precision, orthologous groups, and pairs of proteins using a phylogeny-based approach. Briefly, Broccoli performs ultrafast phylogenetic analyses on most proteins and builds a network of orthologous relationships. Orthologous groups are then identified from the network using a parameter-free machine learning algorithm. Broccoli is also able to detect chimeric proteins resulting from gene-fusion events and to assign these proteins to the corresponding orthologous groups. Tested on two benchmark data sets, Broccoli outperforms current orthology pipelines. In addition, Broccoli is scalable, with runtimes similar to those of recent distance-based pipelines. Given its high level of performance and efficiency, this new pipeline represents a suitable choice for comparative genomic studies. Broccoli is freely available at https://github.com/rderelle/Broccoli.

中文翻译：

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西兰花：将系统发育分析和网络分析相结合进行矫正。

摘要

正交分配是比较基因组研究的关键步骤，为此已经开发了许多生物信息学工具。但是，所有基因聚类流水线都是基于对蛋白质距离的分析，该距离受许多伪像的影响。在本文中，我们介绍了Broccoli，这是一种用户友好的管道，旨在使用基于系统发育的方法来推断高精度直系同源基团和成对的蛋白质。简而言之，西兰花对大多数蛋白质进行超快速的系统发育分析，并建立直系同源关系网络。然后使用无参数机器学习算法从网络中识别直系同源群。西兰花还能够检测由基因融合事件产生的嵌合蛋白，并将这些蛋白分配给相应的直系同源基团。经过两个基准数据集的测试，西兰花的表现优于目前的矫形器。此外，Broccoli具有可扩展性，其运行时类似于最近的基于距离的管道。鉴于其高水平的性能和效率，该新产品线是比较基因组研究的合适选择。西兰花可从https://github.com/rderelle/Broccoli免费获得。