Omics big data for crop improvement: Opportunities and challenges
Naresh Vasupalli,
Javaid Akhter Bhat,
Priyanka Jain,
Tanu Sri,
Md Aminul Islam,
S.M. Shivaraj,
Sunil Kumar Singh,
Rupesh Deshmukh,
Humira Sonah,
Xinchun Lin
Affiliations
Naresh Vasupalli
State Key Laboratory of Subtropical Silviculture, Zhejiang A & F University, Lin’an 311300, Zhejiang, China; Bamboo Industry Institute, Zhejiang A & F University, Lin’an 311300, Zhejiang, China
Javaid Akhter Bhat
Research Center for Life Sciences Computing, Zhejiang Lab, Hangzhou 311121, Zhejiang, China
Priyanka Jain
Amity Institute of Molecular Medicine and Stem Cell Research (AIMMSCR), Amity University Uttar Pradesh, Sector 125, Noida 201313, Uttar Pradesh, India
Tanu Sri
Gurdev Singh Khush Institute of Genetics, Plant Breeding and Biotechnology, Punjab Agricultural University, Ludhiana 141004, Punjab, India
Md Aminul Islam
Department of Botany, Majuli College, Majuli 785106, Assam, India
S.M. Shivaraj
Department of Science, Alliance University, Bengaluru 562106, Karnataka, India
Sunil Kumar Singh
Stress Resilient Agriculture Laboratory, Department of Botany, University of Allahabad, Prayagraj 211002, Uttar Pradesh, India
Rupesh Deshmukh
Department of Biotechnology, Central University of Haryana, Mahendragarh 123031, Haryana, India
Humira Sonah
Department of Biotechnology, Central University of Haryana, Mahendragarh 123031, Haryana, India; Corresponding authors.
Xinchun Lin
State Key Laboratory of Subtropical Silviculture, Zhejiang A & F University, Lin’an 311300, Zhejiang, China; Bamboo Industry Institute, Zhejiang A & F University, Lin’an 311300, Zhejiang, China; Corresponding authors.
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.
