Omics big data for crop improvement: Opportunities and challenges

The application of advanced omics technologies in plant science has generated an enormous dataset of sequences, expression profiles, and phenotypic traits, collectively termed "big data" for their significant volume, diversity, and rapid pace of accumulation. Despite extensive data generation, the process of analyzing and interpreting big data remains complex and challenging. Big data analyses will help identify genes and uncover different mechanisms controlling various agronomic traits in crop plants. The insights gained from big data will assist scientists in developing strategies for crop improvement. Although the big data generated from crop plants opens a world of possibilities, realizing its full potential requires enhancement in computational capacity and advances in machine learning (ML) or deep learning (DL) approaches. The present review discuss the applications of genomics, transcriptomics, proteomics, metabolomics, epigenetics, and phenomics "big data" in crop improvement. Furthermore, we discuss the potential application of artificial intelligence to genomic selection. Additionally, the article outlines the crucial role of big data in precise genetic engineering and understanding plant stress tolerance. Also we highlight the challenges associated with big data storage, analyses, visualization and sharing, and emphasize the need for robust solutions to harness these invaluable resources for crop improvement. (c) 2024 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NCND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).