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Chow CN, Yang CW, Chang WC. Databases and prospects of dynamic gene regulation in eukaryotes: A mini review. Comput Struct Biotechnol J 2023; 21:2147-2159. [PMID: 37013004 PMCID: PMC10066511 DOI: 10.1016/j.csbj.2023.03.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 03/18/2023] [Accepted: 03/19/2023] [Indexed: 04/05/2023] Open
Abstract
In eukaryotes, dynamic regulation enables DNA polymerases to catalyze a variety of RNA products in spatial and temporal patterns. Dynamic gene expression is regulated by transcription factors (TFs) and epigenetics (DNA methylation and histone modification). The applications of biochemical technology and high-throughput sequencing enhance the understanding of mechanisms of these regulations and affected genomic regions. To provide a searchable platform for retrieving such metadata, numerous databases have been developed based on the integration of genome-wide maps (e.g., ChIP-seq, whole-genome bisulfite sequencing, RNA-seq, ATAC-seq, DNase-seq, and MNase-seq data) and functionally genomic annotation. In this mini review, we summarize the main functions of TF-related databases and outline the prevalent approaches used in inferring epigenetic regulations, their associated genes, and functions. We review the literature on crosstalk between TF and epigenetic regulation and the properties of non-coding RNA regulation, which are challenging topics that promise to pave the way for advances in database development.
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Singh D, Chaudhary P, Taunk J, Singh CK, Singh D, Tomar RSS, Aski M, Konjengbam NS, Raje RS, Singh S, Sengar RS, Yadav RK, Pal M. Fab Advances in Fabaceae for Abiotic Stress Resilience: From 'Omics' to Artificial Intelligence. Int J Mol Sci 2021; 22:10535. [PMID: 34638885 PMCID: PMC8509049 DOI: 10.3390/ijms221910535] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 09/17/2021] [Accepted: 09/23/2021] [Indexed: 11/16/2022] Open
Abstract
Legumes are a better source of proteins and are richer in diverse micronutrients over the nutritional profile of widely consumed cereals. However, when exposed to a diverse range of abiotic stresses, their overall productivity and quality are hugely impacted. Our limited understanding of genetic determinants and novel variants associated with the abiotic stress response in food legume crops restricts its amelioration. Therefore, it is imperative to understand different molecular approaches in food legume crops that can be utilized in crop improvement programs to minimize the economic loss. 'Omics'-based molecular breeding provides better opportunities over conventional breeding for diversifying the natural germplasm together with improving yield and quality parameters. Due to molecular advancements, the technique is now equipped with novel 'omics' approaches such as ionomics, epigenomics, fluxomics, RNomics, glycomics, glycoproteomics, phosphoproteomics, lipidomics, regulomics, and secretomics. Pan-omics-which utilizes the molecular bases of the stress response to identify genes (genomics), mRNAs (transcriptomics), proteins (proteomics), and biomolecules (metabolomics) associated with stress regulation-has been widely used for abiotic stress amelioration in food legume crops. Integration of pan-omics with novel omics approaches will fast-track legume breeding programs. Moreover, artificial intelligence (AI)-based algorithms can be utilized for simulating crop yield under changing environments, which can help in predicting the genetic gain beforehand. Application of machine learning (ML) in quantitative trait loci (QTL) mining will further help in determining the genetic determinants of abiotic stress tolerance in pulses.
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Affiliation(s)
- Dharmendra Singh
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India
| | - Priya Chaudhary
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India
| | - Jyoti Taunk
- Division of Plant Physiology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India
| | - Chandan Kumar Singh
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India
| | - Deepti Singh
- Department of Botany, Meerut College, Meerut 250001, India
| | - Ram Sewak Singh Tomar
- College of Horticulture and Forestry, Rani Lakshmi Bai Central Agricultural University, Jhansi 284003, India
| | - Muraleedhar Aski
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India
| | - Noren Singh Konjengbam
- College of Post Graduate Studies in Agricultural Sciences, Central Agricultural University, Imphal 793103, India
| | - Ranjeet Sharan Raje
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India
| | - Sanjay Singh
- ICAR- National Institute of Plant Biotechnology, LBS Centre, Pusa Campus, New Delhi 110012, India
| | - Rakesh Singh Sengar
- College of Biotechnology, Sardar Vallabh Bhai Patel Agricultural University, Meerut 250001, India
| | - Rajendra Kumar Yadav
- Department of Genetics and Plant Breeding, Chandra Shekhar Azad University of Agriculture and Technology, Kanpur 208002, India
| | - Madan Pal
- Division of Plant Physiology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India
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