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Co-Inheritance Analysis within the Domains of Life Substantially Improves Network Inference by Phylogenetic Profiling

机译:生命域内的共继承分析可通过系统发育分析显着改善网络推断

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摘要

Phylogenetic profiling, a network inference method based on gene inheritance profiles, has been widely used to construct functional gene networks in microbes. However, its utility for network inference in higher eukaryotes has been limited. An improved algorithm with an in-depth understanding of pathway evolution may overcome this limitation. In this study, we investigated the effects of taxonomic structures on co-inheritance analysis using 2,144 reference species in four query species: Escherichia coli, Saccharomyces cerevisiae, Arabidopsis thaliana, and Homo sapiens. We observed three clusters of reference species based on a principal component analysis of the phylogenetic profiles, which correspond to the three domains of life—Archaea, Bacteria, and Eukaryota—suggesting that pathways inherit primarily within specific domains or lower-ranked taxonomic groups during speciation. Hence, the co-inheritance pattern within a taxonomic group may be eroded by confounding inheritance patterns from irrelevant taxonomic groups. We demonstrated that co-inheritance analysis within domains substantially improved network inference not only in microbe species but also in the higher eukaryotes, including humans. Although we observed two sub-domain clusters of reference species within Eukaryota, co-inheritance analysis within these sub-domain taxonomic groups only marginally improved network inference. Therefore, we conclude that co-inheritance analysis within domains is the optimal approach to network inference with the given reference species. The construction of a series of human gene networks with increasing sample sizes of the reference species for each domain revealed that the size of the high-accuracy networks increased as additional reference species genomes were included, suggesting that within-domain co-inheritance analysis will continue to expand human gene networks as genomes of additional species are sequenced. Taken together, we propose that co-inheritance analysis within the domains of life will greatly potentiate the use of the expected onslaught of sequenced genomes in the study of molecular pathways in higher eukaryotes.
机译:系统发育谱分析是一种基于基因遗传图谱的网络推断方法,已被广泛用于构建微生物中的功能基因网络。但是,其在高级真核生物中进行网络推断的效用受到限制。具有对途径进化的深入理解的改进算法可以克服该限制。在这项研究中,我们调查了四种查询物种中使用2,144种参考物种的分类结构对共遗传分析的影响:大肠杆菌,酿酒酵母,拟南芥和智人。基于系统发育谱的主成分分析,我们观察到了三类参考物种,它们对应于生命的三个域(Archaea,细菌和真核生物),建议这些途径主要在物种形成过程中在特定域或低等分类组内遗传。因此,通过混淆来自无关分类组的继承模式,可以侵蚀分类组内的共继承模式。我们证明,域内的协同遗传分析不仅在微生物物种中而且在包括人类在内的高等真核生物中都大大改善了网络推断。尽管我们在真核生物中观察到了两个参考物种的子域簇,但是在这些子域分类组内的协同遗传分析只能稍微改善网络推断。因此,我们得出结论,域内的协同继承分析是与给定参考物种进行网络推理的最佳方法。一系列人类基因网络的构建,每个域的参考物种的样本数量不断增加,这表明,随着包含其他参考物种基因组的增加,高精度网络的规模也随之增加,这表明域内共继承分析将继续随着其他物种的基因组测序,扩展人类基因网络。两者合计,我们建议在生活领域内的共同继承分析将大大增强预期的序列化基因组的突击在高级真核生物分子途径研究中的使用。

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  • 期刊名称 other
  • 作者

    Junha Shin; Insuk Lee;

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  • 年(卷),期 -1(10),9
  • 年度 -1
  • 页码 e0139006
  • 总页数 12
  • 原文格式 PDF
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