Genomics is the branch of science that studies the entire genome of an organism, which is the complete set of genetic material in the organism. The genome serves as a blueprint for an organism’s proper growth and development. In eukaryotic organisms, genetic information is stored in multiple genomes, including the nuclear, mitochondrial, and chloroplast genomes. The term “genomics” was first used by a geneticist, T.H. Roderick, in 1986. It gives insight into genome size, sequence, arrangement, and organization. Comparisons of genomic sequences of various organisms could help understand variations at a sequence level (single nucleotide polymorphism, genome rearrangement, duplications, transpositions, microsatellites, etc.) to gain insight into genetic and functional diversity, evolution, and adaptation across the tree of life. Genomics also serves as the foundation for other omics approaches, which include transcriptomics and proteomics, among others. The multi-omics approaches help in understanding gene function, regulation, and their role and involvement in various biological processes and pathways. It is an interdisciplinary field that works in combination with genetics, molecular biology, bioinformatics, and computational biology. Over the years, genomics has undergone significant advances with the evolution of sequencing technologies, from first-generation, next-generation, to third-generation sequencing methods. These sequencing technologies have increased sequencing speed, accuracy, and cost-effectiveness. As sequencing technologies continue to evolve, genomics remains at the cutting edge of biological research, driving discoveries with profound implications for fields such as medicine, agriculture, and evolutionary biology. The future of genomics holds immense promise, and it continues to be an exciting time in the field of genomics research.
Genomic Workflow
The genomic research workflow begins with sample identification and collection, followed by the estimation of genome size, ploidy, GC content and heterozygosity of the organism. Genomic DNA is then extracted, purified, and undergoes quality and quantity checks. High quality DNA can be sequenced using various sequencing technologies depending upon the objective to generate sequence data for genome analysis. Genomic data analysis begins with read quality assessment and pre-processing which is followed by read mapping and genome assembly to determine the contiguity, completeness, and accuracy of sequenced reads. The structure of the genome is subsequently evaluated by scaffold elongation and chromosome anchoring. The genomic features are then identified, followed by functional annotations to reveal biological roles. Subsequently, the sequence data and annotations are made available to the wider research community through public data submission to enable access to the research information.
Moreover, genomic research relies on several data analysis software, tools and pipelines to extract meaning from the huge amounts of data obtained. Notably, genomic data analysis comprises numerous steps starting from quality assessment to read alignment, variant calling, annotation, and downstream analysis. Overall, the ultimate goal of genomic data is generation insight from the genetic data or understanding the function of genes, uncover the variations and discover the associations within the genome components. Hence, the analysis provides an essential link between raw genetic data and actionable insight.
APPLICATION OF GENOMICS
Genomics has revolutionized our understanding of biology through various applications. These include gene identification and cloning, predicting and discovering new genes, mapping genetic traits, manipulating genomes, and developing molecular markers for marker-assisted selection. Additionally, genomics enables the analysis of quantitative trait loci (QTLs) and fine mapping of genes, facilitates comparative genomics studies, and contributes to the development of gene banks and chromosome stocks. These applications have transformative impacts on medicine, agriculture, and evolutionary biology, driving ongoing discoveries and innovations in these fields.
References:
- Kim, J. H. (2019, April 30). Genome Data Analysis. Springer.
- Huang, E. S., Nevins, J. R., West, M., & Kuo, P. C. (2004). An overview of genomic data analysis. Surgery, 136(3), 497–499. https://doi.org/10.1016/j.surg.2004.05.037
- Bhargava, A., & Srivastava, S. (Eds.). (2023). Integrative Approaches to Biotechnology. CRC Press.
