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Upstream regulatory architecture of rice genes: summarizing the baseline towards genus-wide comparative analysis of regulatory networks and allele mining

机译:水稻基因的上游调控结构:总结基线对整个调控网络和等位基因挖掘进行属类比较分析

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摘要

Dissecting the upstream regulatory architecture of rice genes and their cognate regulator proteins is at the core of network biology and its applications to comparative functional genomics. With the rapidly advancing comparative genomics resources in the genus Oryza, a reference genome annotation that defines the various cis-elements and trans-acting factors that interface each gene locus with various intrinsic and extrinsic signals for growth, development, reproduction and adaptation must be established to facilitate the understanding of phenotypic variation in the context of regulatory networks. Such information is also important to establish the foundation for mining non-coding sequence variation that defines novel alleles and epialleles across the enormous phenotypic diversity represented in rice germplasm. This review presents a synthesis of the state of knowledge and consensus trends regarding the various cis-acting and trans-acting components that define spatio-temporal regulation of rice genes based on representative examples from both foundational studies in other model and non-model plants, and more recent studies in rice. The goal is to summarize the baseline for systematic upstream sequence annotation of the rapidly advancing genome sequence resources in Oryza in preparation for genus-wide functional genomics. Perspectives on the potential applications of such information for gene discovery, network engineering and genomics-enabled rice breeding are also discussed.
机译:剖析水稻基因及其同源调控蛋白的上游调控结构是网络生物学的核心,并将其应用于比较功能基因组学。随着Oryza属中比较基因组学资源的迅速发展,必须建立一个参考基因组注释,该注释定义了将每个基因座与各种内在和外在信号相联系的各种顺式和反式作用因子,以促进生长,发育,繁殖和适应在监管网络的背景下促进对表型变异的理解。此类信息对于建立挖掘非编码序列变异的基础也很重要,该变异定义了水稻种质中巨大表型多样性中的新等位基因和表位等位基因。这篇综述基于其他模型和非模型植物的基础研究中的代表性实例,对定义水稻基因的时空调控的各种顺式和反式作用成分的知识状态和共识趋势进行了综述,以及最近有关水稻的研究。目的是总结在Oryza中快速推进的基因组序列资源的系统上游序列注释的基线,以准备全属功能基因组学研究。还讨论了此类信息在基因发现,网络工程和基因组学育种中的潜在应用前景。

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