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

文献类型： 外文期刊

第一作者： Wu, Leiming

作者： Wu, Leiming;Han, Linqian;Li, Qing;Li, Lin;Wang, Guoying;Zhang, Hongwei

作者机构：

关键词： genetic mystery; interactome big data; machine learning; crop breeding

期刊名称：MOLECULAR PLANT （ 影响因子：13.164； 五年影响因子：16.357 ）

ISSN： 1674-2052

年卷期： 2021 年 14 卷 1 期

页码：

收录情况： SCI

摘要： 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 highthroughput 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.

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