A combined method of genomics and bioinformatics for plant breeding programs

Document Type : Research Paper

Authors

1 Department of CS & IT, Kalinga University, Raipur, India.

2 Department of CS & IT, Kalinga University, Raipur, India.

Abstract

Objectives
Given the rapid growth of the global human population, it is imperative to enhance agricultural productivity to fulfill the increasing demand for crops. Enhancing crops through selective plant breeding (PB) is a sustainable method to augment both the quantity and consistency of yields without escalating the need for fertilizers and pesticides. Moreover, data generation in agriculture and biotechnology has greatly increased in recent years due to the very rapid development of high-performance technologies. Similar to advancements in genomics, there are encouraging progressions in plant phenotyping technologies, including mechanized phenotyping apparatus and sophisticated picture analysis. This has led to an unparalleled understanding of PB, structure, and function. Therefore, the aim of this study was to investigate a combined method of genomics and bioinformatics for plant breeding programs.
 
Results
The latest advancements in genomics and bioinformatics offer possibilities for expediting crop enhancement. Third-generation sequencing (TGS) techniques are aiding in the resolution of difficulties in plant genome assembly arising from polyploidy and the presence of repetitive regions. There is a growing availability of superior crop-referencing genomes, which significantly aids in the analysis of genetic variations and the identification of specific PB objectives within the genome. Machine learning (ML) aids in identifying genomic areas of agricultural significance by assisting in the annotation of genomes and allowing for the efficient measurement of agronomic variables in controlled and natural environments.
 
Conclusions
Crop datasets that combine the increasing amount of genotype and phenotypic data offer a valuable tool for breeders and a chance for data mining methods to discover new candidate genes related to traits. Moreover, with the increasing understanding of agricultural genetics, the techniques of genomic selections and genome engineering provide the potential for developing crops resistant to illnesses and adaptable to stress while achieving high production.

Keywords

References

Agrawal N, Tripathi R, Jain M (2020) Molecular marker tools for breeding program in crops. Plant Ecophysiology and Adaptation under Climate Change: Mechanisms and Perspectives II: Mechanisms of Adaptation and Stress Amelioration, Springer Singapore, pp. 567-582.
Alavi M, Mozafari MR, Ghaemi S et al. (2022) Interaction of Epigallocatechin Gallate and Quercetin with Spike Glycoprotein (S-Glycoprotein) of SARS-CoV-2: In Silico Study. Biomedicines 10(12), e3074.
Amiri Roudbar M, Mohammadabadi MR, Ayatollahi Mehrgardi A, et al. (2020) Integration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controls. Heredity 124(5), 658-674.
Barazandeh A, Mohammadabadi MR, Ghaderi-Zefrehei M, Nezamabadi-Pour H (2016) Genome-wide analysis of CpG islands in some livestock genomes and their relationship with genomic features. Czech J Anim Sci 61(11), 487-495.
Barbosa IDP, da Silva MJ, da Costa WG, de Castro Sant'Anna I et al. (2021) Genome‐enabled prediction through machine learning methods considering different levels of trait complexity. Crop Sci 61(3), 1890-1902.
Bhattacharya S, Barseghyan H, Délot EC, Vilain E (2021) nanotatoR: a tool for enhanced annotation of genomic structural variants. BMC Genomics 22, 1-16.
Bohra A, Chand Jha U, Godwin ID, Kumar Varshney R (2020) Genomic interventions for sustainable agriculture. Plant Biotechnol J 18(12), 2388-2405.
Bordbar F, Mohammadabadi M, Jensen J et al. (2022) Identification of candidate genes regulating carcass depth and hind leg circumference in simmental beef cattle using Illumina Bovine Beadchip and next-generation sequencing. Animals 12(9), e1103.
Brière G, Darbo É, Thébault P, Uricaru R (2021) Consensus clustering applied to multi-omics disease subtyping. BMC Bioinformatics 22, 1-29.
Camgözlü Y, Kutlu Y (2023) Leaf Image Classification Based on Pre-trained Convolutional Neural Network Models. Natural and Engineering Sciences 8(3), 214-232.
Cao Y, Geddes TA, Yang JYH, Yang P (2020) Ensemble deep learning in bioinformatics. Nat Mach Intell 2(9), 500-508.
Chen C, Wu Y, Li J, Wang X et al. (2023) TBtools-II: A “one for all, all for one” bioinformatics platform for biological big-data mining. Mol Plant 16(11), 1733-1742.
Dessy A, Ratna D, Leni S, Yadi Heryadi D et al. (2023) Using Distance Measure to Perform Optimal Mapping with the K-Medoids Method on Medicinal Plants, Aromatics, and Spices Export. J Wirel Mob Netw Ubiquitous Comput Dependable Appl 14(3), 103-111.
Ghotbaldini H, Mohammadabadi MR, Nezamabadi-pour H et al. (2019) Predicting breeding value of body weight at 6-month age using Artificial Neural Networks in Kermani sheep breed. Acta Scientiarum Anim Sci 41, e45282.
Gladkov EA, Gladkova OV (2021) Plants and Maximum Permissible Concentrations of Heavy Metals in Soil. Arch Tech Sci 2(25), 77–82.
Heidarpour F, Mohammadabadi MR, Zaidul ISM et al. (2011) Use of prebiotics in oral delivery of bioactive compounds: a nanotechnology perspective. Pharmazie 66(5), 319-324.
Kamusoko R, Jingura RM, Parawira W, Chikwambi Z (2021) Strategies for valorization of crop residues into biofuels and other value‐added products. Biofuels Biofpr 15(6), 1950-1964.
Khabiri A, Toroghi R, Mohammadabadi M, Tabatabaeizadeh SE (2023) Introduction of a Newcastle disease virus challenge strain (sub-genotype VII. 1.1) isolated in Iran. Vet Res Forum 14(4), e221.
Mikheenko A, Prjibelski AD, Joglekar A, Tilgner HU (2022) Sequencing of individual barcoded cDNAs using Pacific Biosciences and Oxford Nanopore Technologies reveals platform-specific error patterns. Genome Res 32(4), 726-737.
Mohammadabadi M, Kheyrodin H, Afanasenko V et al. (2024) The role of artificial intelligence in genomics. Agric Biotechnol J 16 (2), 195-279.
Pour Hamidi S, Mohammadabadi MR, Asadi Foozi M, Nezamabadi-pour H (2017) Prediction of breeding values for the milk production trait in Iranian Holstein cows applying artificial neural networks. J Livestock Sci Technol 5 (2), 53-61.
Safaei SMH, Dadpasand M, Mohammadabadi M et al. (2022) An Origanum majorana Leaf Diet Influences Myogenin Gene Expression, Performance, and Carcass Characteristics in Lambs. Animals 13(1), e14.
Sayers EW, Bolton EE, Brister JR, Canese K et al. (2023) Database resources of the National Center for Biotechnology Information in 2023. Nucleic Acids Res 51(D1), D29-D38.
Srinivasa Rao M, Praveen Kumar S, Srinivasa Rao K (2023) Classification of Medical Plants Based on Hybridization of Machine Learning Algorithms. Indian J Inform Source Serv 13(2), 14-21.
Su J, Li C, Ou X, Wen S et al. (2024) CmWAT6. 1, mined by high-density genetic map-based QTL mapping, enhances waterlogging tolerance in the chrysanthemums. Environ Exp Bot 219, e105612.
Surendar A, Saravanakumar Veerappan, Sindhu S, Arvinth N (2024) A Bibliometric Study of Publication- Citations in a Range of Journal Articles. Indian J Inform Source Serv 14(2), 97-103.
Suruliandi A, Mariammal G, Raja SP (2021) Crop prediction based on soil and environmental characteristics using feature selection techniques. Math Comp Model Dyn 27(1), 117-140.
Swarup S, Cargill EJ, Crosby K, Flagel L et al. (2021) Genetic diversity is indispensable for plant breeding to improve crops. Crop Sci 61(2), 839-852.
Torniainen J, Afara I.O, Prakash M, Sarin JK et al. (2020) Open-source Python module for automated preprocessing of near-infrared spectroscopic data. Anal Chim Acta 1108, 1-9.
Veerasamy K, Fredrik ET (2023) Intelligent Farming based on Uncertainty Expert System with Butterfly Optimization Algorithm for Crop Recommendation. J Internet Serv Inf Secur 13(3), 158-169.
Wan YK, Hendra C, Pratanwanich PN, Göke J (2022) Beyond sequencing: machine learning algorithms extract biology hidden in Nanopore signal data. Trends Genet 38(3), 246-257.
Wang Y, Zhao Y, Bollas A, Wang Y et al. (2021) Nanopore sequencing technology, bioinformatics, and applications. Nat Biotechnol 39(11), 1348-1365.
Williamson A, Leiros HKS (2020) Structural insight into DNA joining: from conserved mechanisms to diverse scaffolds. Nucleic Acids Res 48(15), 8225-8242.
Xiao J, Liu B, Yao Y, Guo Z et al. (2022) Wheat genomic study for genetic improvement of traits in China. Sci China Life Sci 65(9), 1718-1775.
Xiao T, Zhou W (2020) The third generation sequencing: the advanced approach to genetic diseases. Transl Pediatr 9(2), e163.
Agricultural Biotechnology Journal
Volume 16, Issue 3
September 2024
Pages 211-228

Files

Share

How to cite

Statistics