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,dive...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.展开更多
Advances in DNA sequencing technology have sparked a genomics revolution,driving breakthroughs in plant genetics and crop breeding.Recently,the focus has shifted from cataloging genetic diversity in plants to explorin...Advances in DNA sequencing technology have sparked a genomics revolution,driving breakthroughs in plant genetics and crop breeding.Recently,the focus has shifted from cataloging genetic diversity in plants to exploring their functional significance and delivering beneficial alleles for crop improvement.This transformation has been facilitated by the increasing adoption of whole-genome resequencing.In this review,we summarize the current progress of population-based genome resequencing studies and how these studies affect crop breeding.A total of 187 land plants from 163 countries have been resequenced,comprising 54413 accessions.As part of resequencing efforts 367 traits have been surveyed and 86 genome-wide association studies have been conducted.Economically important crops,particularly cereals,vegetables,and legumes,have dominated the resequencing efforts,leaving a gap in 49 orders,including Lycopodiales,Liliales,Acorales,Austrobaileyales,and Commelinales.The resequenced germplasm is distributed across diverse geographic locations,providing a global perspective on plant genomics.We highlight genes that have been selected during domestication,or associated with agronomic traits,and form a repository of candidate genes for future research and application.Despite the opportunities for cross-species comparative genomics,many population genomic datasets are not accessible,impeding secondary analyses.We call for a more open and collaborative approach to population genomics that promotes data sharing and encourages contribution-based credit policy.The number of plant genome resequencing studies will continue to rise with the decreasing DNA sequencing costs,coupled with advances in analysis and computational technologies.This expansion,in terms of both scale and quality,holds promise for deeper insights into plant trait genetics and breeding design.展开更多
基金Fund for International Young Scientists by the National Natural Science Foundation of China (32150410354)to Naresh Vasupallithe Department of Biotechnology,Government of India,for the Ramalingaswami Fellowship Award (BT/PR38279/GET/119/351/2020)to Humira SonahHaryana State Council for Science Innovation and Technology (HSCSIT)for the research grant PI ID 1270,HSCSIT/R&D/2024/511 to Rupesh Deshmukh and Humira Sonah.
文摘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.
基金supported by the National Key Research and Development Program of China(2020YFE0202300)Science and Technology Major Project of Guangxi(GuiKeAA20108005-2)+1 种基金Guangdong Innovation Research Team Fund(grant number:2014ZT05S078)National Key Research and Development Program of China(2019YFA0707000).No conflict of interest declared.
文摘Advances in DNA sequencing technology have sparked a genomics revolution,driving breakthroughs in plant genetics and crop breeding.Recently,the focus has shifted from cataloging genetic diversity in plants to exploring their functional significance and delivering beneficial alleles for crop improvement.This transformation has been facilitated by the increasing adoption of whole-genome resequencing.In this review,we summarize the current progress of population-based genome resequencing studies and how these studies affect crop breeding.A total of 187 land plants from 163 countries have been resequenced,comprising 54413 accessions.As part of resequencing efforts 367 traits have been surveyed and 86 genome-wide association studies have been conducted.Economically important crops,particularly cereals,vegetables,and legumes,have dominated the resequencing efforts,leaving a gap in 49 orders,including Lycopodiales,Liliales,Acorales,Austrobaileyales,and Commelinales.The resequenced germplasm is distributed across diverse geographic locations,providing a global perspective on plant genomics.We highlight genes that have been selected during domestication,or associated with agronomic traits,and form a repository of candidate genes for future research and application.Despite the opportunities for cross-species comparative genomics,many population genomic datasets are not accessible,impeding secondary analyses.We call for a more open and collaborative approach to population genomics that promotes data sharing and encourages contribution-based credit policy.The number of plant genome resequencing studies will continue to rise with the decreasing DNA sequencing costs,coupled with advances in analysis and computational technologies.This expansion,in terms of both scale and quality,holds promise for deeper insights into plant trait genetics and breeding design.
