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利用互作组大数据破解遗传谜团,提升未来作物育种水平。

Using Interactome Big Data to Crack Genetic Mysteries and Enhance Future Crop Breeding.

机构信息

National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.

Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China.

出版信息

Mol Plant. 2021 Jan 4;14(1):77-94. doi: 10.1016/j.molp.2020.12.012. Epub 2020 Dec 16.

Abstract

The functional genes underlying phenotypic variation and their interactions represent "genetic mysteries". Understanding and utilizing these genetic mysteries are key solutions for mitigating the current threats to agriculture posed by population growth and individual food preferences. Due to advances in high-throughput multi-omics technologies, we are stepping into an Interactome Big Data era that is certain to revolutionize genetic research. In this article, we provide a brief overview of current strategies to explore genetic mysteries. We then introduce the methods for constructing and analyzing the Interactome Big Data and summarize currently available interactome resources. Next, we discuss how Interactome Big Data can be used as a versatile tool to dissect genetic mysteries. We propose an integrated strategy that could revolutionize genetic research by combining Interactome Big Data with machine learning, which involves mining information hidden in Big Data to identify the genetic models or networks that control various traits, and also provide a detailed procedure for systematic dissection of genetic mysteries,. Finally, we discuss three promising future breeding strategies utilizing the Interactome Big Data to improve crop yields and quality.

摘要

表型变异的功能基因及其相互作用代表了“遗传之谜”。理解和利用这些遗传之谜是缓解人口增长和个人食物偏好对农业当前威胁的关键解决方案。由于高通量多组学技术的进步,我们正步入一个交互组大数据时代,这必将彻底改变遗传研究。在本文中,我们简要概述了探索遗传之谜的当前策略。然后,我们介绍了构建和分析交互组大数据的方法,并总结了目前可用的交互组资源。接下来,我们讨论了如何将交互组大数据用作剖析遗传之谜的通用工具。我们提出了一种综合策略,通过将交互组大数据与机器学习相结合,可以彻底改变遗传研究,其中包括挖掘大数据中隐藏的信息,以识别控制各种性状的遗传模型或网络,并为系统剖析遗传之谜提供详细的步骤。最后,我们讨论了三种利用交互组大数据提高作物产量和质量的有前途的未来育种策略。

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