A combined method of genomics and bioinformatics for plant breeding programs

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Biswas Debarghya; Sharma Pooja

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Journal Paper

Paper Information

Journal: AGRICULTURAL BIOTECHNOLOGY JOURNAL Year:2024 | Volume:16 | Issue:3 Page(s): 211-228

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Information Journal Paper

Title

A combined method of genomics and bioinformatics for plant breeding programs

Author(s)

Biswas Debarghya | Sharma Pooja | Issue Writer Certificate

Keywords

bioinformatics

Crops

genomics

Plant breeding

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.

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