eChapter Name: Fast Tracking Plant Breeding through Bioinformatics
9789372191417
eBook Name: NEXT GENERATION PLANT BREEDING
Introduction
The integration of bioinformatics into plant breeding has been shaped by several key milestones over the past few decades. Concepts like Breeding 4.0 (Wallace et al.,2018) and, 5G Breeding (Varshney et al., 2020) have gained global prominence, strengthened by the availability of extensive omics data. The advent of DNA sequencing technologies marked a key milestone in this field, catalyzing numerous genome sequencing projects worldwide such as Arabidopsis, rice, tomato, maize, wheat, etc. Next-generation sequencing (NGS) platforms, such as Illumina, PacBio, and Oxford Nanopore, have enabled rapid sequencing of entire plant genomes, facilitating the identification of genetic variations, including single nucleotide polymorphisms (SNPs), insertions, deletions, and structural variants (Deschamps & Campbell, 2010; Fuentes-Pardo et al.,2017). These advancements have significantly improved comparative genomics, enabling researchers to compare the genetic makeup of different plant species or varieties and identify genes associated with agronomic traits, such as yield, disease resistance, and environmental stress tolerance (Khan et al., 2024). As a result, these developments have revolutionized plant genetic research and crop improvement strategies, providing unprecedented opportunities to enhance agricultural productivity (Bellare et al., 2018; Shendure and Ji, 2008).
Bioinformatics has revolutionized plant biology by combining computational and statistical tools to analyze genomes, transcriptomes, proteomes, and metabolomes facilitating insights into genome structure, function, and evolution (Yadav et al., 2015; Saha et al., 2023; Roychowdhury et al., 2023). Genomics, emerged in the 1980s primarily focussing on mapping and sequencing genomes and there after laid the groundwork for functional genomics, which systematically explored gene functions (Hieter and Boguski, 1997; (Jackson et al., 2011). Further, advances in sequencing technologies, such as long-read platforms, have enabled detailed analyses of structural variants in crop genomes, crucial for identifying agronomic traits.
The large volumes of genomic sequence data have allowed researchers to identify genetic markers for crop improvement and candidate genes for trait development. However, the full potential of these genomic sequencing efforts depends on assigning functions to the thousands of genes with unknown roles. Model organisms such as humans, mice, Arabidopsis, rice, tomato, Medicago, and wheat have been instrumental in advancing genomic investigations (Bult, 2006). A notable example is the model organism Arabidopsis thaliana, which has provided a foundation for exploring fundamental biological phenomena using resources such as the TAIR database (Lamesch et al., 2012). Leveraging advanced bioinformatics techniques, researchers have enhanced crop productivity, mitigated diseases, and optimized resource management in agriculture. Advanced approaches like genome-wide association studies (GWAS) and transcriptome analysis continue to refine the understanding of genetic underpinnings, revolutionizing modern agricultural strategies (Mai et al., 2023).