Salinity Stress: “Omics” Approaches

Khatri, Nisha; Mudgil, Yashwanti

doi:10.1201/9781315365930-10

Cited by 2 publications

(1 citation statement)

References 144 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This strategy helps the researcher to observe the differential expression of genes in vitro to understand the first layer function of a particular gene. Initially, transcriptome dynamics were analyzed using traditional profiling, cDNAs-AFLP, differential display-PCR (DD-PCR), and SSH, but these techniques provided low resolution (Nataraja et al, 2017). Soon after, the introduction of robust techniques made it possible for RNA expression profiling utilizing microarrays, digital gene expression profiling, NGS, RNAseq, and SAGE (Kawahara et al, 2012;De Cremer et al, 2013;Duque et al, 2013).…”

Section: Transcriptomicsmentioning

confidence: 99%

Applications of Multi-Omics Technologies for Crop Improvement

et al. 2021

View full text Add to dashboard Cite

Multiple “omics” approaches have emerged as successful technologies for plant systems over the last few decades. Advances in next-generation sequencing (NGS) have paved a way for a new generation of different omics, such as genomics, transcriptomics, and proteomics. However, metabolomics, ionomics, and phenomics have also been well-documented in crop science. Multi-omics approaches with high throughput techniques have played an important role in elucidating growth, senescence, yield, and the responses to biotic and abiotic stress in numerous crops. These omics approaches have been implemented in some important crops including wheat (Triticum aestivum L.), soybean (Glycine max), tomato (Solanum lycopersicum), barley (Hordeum vulgare L.), maize (Zea mays L.), millet (Setaria italica L.), cotton (Gossypium hirsutum L.), Medicago truncatula, and rice (Oryza sativa L.). The integration of functional genomics with other omics highlights the relationships between crop genomes and phenotypes under specific physiological and environmental conditions. The purpose of this review is to dissect the role and integration of multi-omics technologies for crop breeding science. We highlight the applications of various omics approaches, such as genomics, transcriptomics, proteomics, metabolomics, phenomics, and ionomics, and the implementation of robust methods to improve crop genetics and breeding science. Potential challenges that confront the integration of multi-omics with regard to the functional analysis of genes and their networks as well as the development of potential traits for crop improvement are discussed. The panomics platform allows for the integration of complex omics to construct models that can be used to predict complex traits. Systems biology integration with multi-omics datasets can enhance our understanding of molecular regulator networks for crop improvement. In this context, we suggest the integration of entire omics by employing the “phenotype to genotype” and “genotype to phenotype” concept. Hence, top-down (phenotype to genotype) and bottom-up (genotype to phenotype) model through integration of multi-omics with systems biology may be beneficial for crop breeding improvement under conditions of environmental stresses.

show abstract