eChapters

1. What is Genome Sequencing?

Genome sequencing is the process of determining the complete DNA sequence of an organism’s genome. The genome is the genetic material that contains all the instructions necessary for the growth, development, functioning and reproduction of an organism. It is composed of DNA (deoxyribonucleic acid., which is a long molecule made up of four building blocks called nucleotides adenine (A), thymine (T), cytosine (C), and guanine (G).

2. Why is genome sequencing important in plant pathology?

Genome sequencing is essential in plant pathology for several reasons

i. Genome sequencing in plant pathology identifies and characterizes plant pathogens at the molecular level.

ii. It helps trace the evolution of pathogens and their genetic adaptations.

iii. Genomic data enables the development of precise diagnostic tools for rapid pathogen detection.

iv. Understanding host-pathogen interactions informs disease management strategies.

Introduction
Detailed understanding of the gene structure and functions are achieved in respect of the genes present in the nuclear genome. The knowledge of the gene functions is being applied in plant genetics and plant breeding. The different kinds of genetic variants are well captured and explained in crop improvement programmes. The organelle genomes, namely, chloroplast and mitochondria that are present outside the nucleus have not received much attention to understand this organization, structure, functions and in utilizing their genetic endowments in many of the plant development and functions. The organelle genomes present in the cytoplasm are providing cytoplasmic effect, cytonuclear and inter genome interactions that impact plant development, plant fertility and agronomic traits expression. It is estimated that 80% of the metabolites are affected by the variations in organelle genomes. The organelle genome also influences and regulates the activity of nuclear genes. The The chloroplast genome encodes proteins that are central to plant photosynthetic activity for plant photosynthesis producing sugar and mitochondria genomes as the energy powerhouse for the cells and they are very essential to provide support to the plant functions and biomass production that is converted to the crop yield. Together, the organelle genomes have disproportionate effects on the nuclear gene functions.
These two organelle genomes that exist outside the nuclear genomes, active in synthesizing food and energy for cell functions, are not as much considered and relied upon by the plant breeders although they are basic biological machineries. This chapter on the organelle genomes is designed to present the structure and genetic endowments and functioning of the chloroplast and mitochondrial

4.1 Genetic Improvement of Aquaculture Species

1. Introduction

1.1 Importance of Genetic Improvement in Aquaculture

Aquaculture serves as a critical component of global food production, supplying a substantial portion of seafood consumed worldwide. However, the industry faces challenges such as disease outbreaks, environmental changes, and increasing demand for sustainable practices. Genetic improvement plays a crucial role in addressing these challenges by enhancing the traits that contribute to productivity, disease resistance, and environmental adaptability in aquaculture species.

1.2 Role of Genomics and Biotechnology

Advancements in genomics and biotechnology have revolutionized the way aquaculture species are bred and managed. Genomics enables the detailed study of an organism’s genetic makeup, allowing researchers to identify specific genes associated with desirable traits. Biotechnological tools, including genome editing and molecular markers, facilitate precise manipulation of these genetic traits, accelerating the breeding process and improving the efficiency of trait selection.

Introduction

Animals have delicate balance of heat production and heat loss, which is maintained by thermoregulatory mechanisms in response to environmental temperature and humidity combinations. When the heat loss is overrun by heat gain, heat stress occurs. During heat stress, increase in the core body temperature due to failure of homeostatic mechanism reduces productivity of the animals below their original genetic potential in growth, milk production, milk constituents and reproduction (Ravagnolo and Misztal, 2002). Impending climate change scenario is a unanimously accepted reality. Increase in the anthropogenic greenhouse gases (CO₂, CH₄and NO₂) cause radiation leading to excess warming of the earth’s environmental system. According to IPCC, the predicted temperature rises for the end of 21^st century may be in the range of 1.8^oC to 4^oC (IPCC, 2007). The climate change has complex impact on the livestock production. Temperature or heat stress will be one of the major environmental factor to in?uence the health and productivity of livestock population. The least tropically adapted livestock may be severely affected by heat stress. The manage mental and ameliorative practices can deal with the heat stress in short term, however the long term focus of developing genetic heat stress tolerance in livestock population will be a significant and impactful strategy.

Modern plant genomics generates highly complex and high-dimensional datasets that require advanced computational approaches for effective analysis. This chapter explores the application of artificial intelligence to plant genomic data analysis. Machine learning and deep learning techniques are discussed for identifying complex patterns, classifying genomic information, predicting phenotypes and supporting genomic selection. Convolutional neural networks, long short-term memory networks and transformer models are considered for analyzing genomic sequences and relationships. The chapter presents an AI-based framework integrating data processing, model development and prediction while highlighting the challenges associated with genomic data heterogeneity and dimensionality. It emphasizes the potential of AI for improving genomic research and plant improvement strategies

Introduction

The study of infectious diseases, alongside other clinical and natural strengths, is going through fast change introduced on by the approach of reasonable next generation sequencing (NGS) advancements (Koser et al., 2012).These technologies become adopted as routine methods easily, and laboratory techniques are transformed throughout the process. The amount of genomic bacterial data generated is huge. As of this writing, for example, over 200,000 Salmonella genomes alone are in the public domain with hundreds being added weekly. A whole genomic DNA sequence characterizes the maximum practicable level of structural detail on the individuating traits of an microbial organism or population. It can therefore be used to provide more accurate microbial genome recognition, evolutionary relations and a detailed catalogue of epidemiologically important characteristics. This has a significant role to play for outbreak research, diagnosis and treatment of infectious diseases, microbiology and epidemiology (Allard et al., 2019). Besides, DNA sequences are also a universal dataset which can theoretically be used to determine any biological function. This means that antimicrobial resistant (AMR) can be detected and to track the development and spread of AMR bacteria in a hospital or the community.

Introduction
The method of selective breeding involves selecting particular breeding prospects to accumulate beneficial alleles and increase the prevalence of desired traits in a population. Characteristics, including growth, disease resistance, reproduction, development, and coloration, are frequently addressed in aquaculture (Maluwa, 2022). Selective breeding, whereby people are picked to have offspring with desired traits, is the foundation of genetic improvement initiatives in all organisms. This is a type of directed evolution in which the breeder determines fitness instead of the organism’s innate capacity for survival and procreation. Darwin’s theory of natural selection was affected by selective breeding, which existed before the mechanics of heredity were discovered. Darwin agreed that deliberate human selection used excellent outcomes (Hill, 2017). By choosing better parents, a species’ desirable features can be improved. Common carp (Cyprinus carpio) were first cultivated in ponds in China 4,000–5,000 years ago, marking the beginning of fish farming. Environmental effects, growing competition for water, and increased demand due to population development and declining wild fish sources are some of the issues aquaculture faces as it grows. Controlled mating and lineage monitoring were two early breeding techniques that resulted in severe inbreeding and decreased output. Mendel’s plant experiments established the groundwork for quantitative and contemporary genetics when they were made public in
1900 (Gjedrem and Baranski, 2010). Mendel’s experiments were confirmed in 1900, marking the beginning of selective breeding for plant production. Fifteen years later, Farm animals were treated using the same methods (Hagedoorn, 1950). Genetically modified stocks are used for most plant and farm animal production. Genetic advancement in aquaculture is still sluggish, though. Although the first aquaculture family-based breeding program began in 1975, by 2010, only 8.2% of aquaculture production was based on genetically modified stocks (Neira, 2010; Rye et al. 2010).

Introduction

The expanding number of people around the world necessitates an increase in the amount of food that is yielded. As stated by the Food and Agriculture Organization of the United Nations (FAO), global food production ought to be ramped up by a factor of two in the next few years in to meet the requirements of the growing global population in terms of consumption (FAO, 2006). This is because the global population is expected to continue growing at a rate of approximately one person every two seconds until the year 2050. It is possible that the predicted production disparity could be filled by making improvements in genetics, animal health, and the excellence of animal husbandry.

The production of animals has expanded significantly over the course of the last several decades as a direct result of the application of quantitative genetics in animal breeding. However, due to technological limitations, the use of genetic markers in breeding programmes has only been implemented to a relatively small extent (Deng et al., 2016). The concept of genomic prediction, which was first introduced approximately twenty years ago, has completely transformed the planning and execution of animal breeding operations.

The global human population is increasing exponentially with time, and it is expected to reach 9.6 billion by 2050 (UN 2013). With rapid population growth, food consumption is predicted to more than double in the next 40 years. Due to limited land resources for cultivation and a stabilising trend in agricultural productivity, the role of livestock sector is increasing significantly to meet the rising food demand. Meanwhile, global livestock production has increased significantly in both number and productivity. Unprecedented urbanisation has a significant impact on food consumption patterns in general, as well as demand for livestock products. Animal diseases are a significant factor in reducing livestock productivity, particularly in developed countries. The limitations of traditional animal breeding hinder further improvement of livestock productivity. The use of biotechnological tools has major applications in improving animal health and production, increasing disease resistance, using assisted reproductive techniques, improving rapid diagnostic techniques, and improving vaccines in livestock systems. These interventions cover advancements in feeding, nutrition, genetics, reproduction, animal health control, and overall improvement of animal husbandry. Since genomic tools and selection have been widely adopted in developed countries to improve both productivity and quality of animal products, the prospects for increasing productivity in developing countries using genomic tools and selection have not been adequately investigated.

Introduction

Estimated breeding values (EBVs), often estimated for sires, are used in traditional animal breeding (TAB) programmes to make selections based on an animal’s performance and that of its relatives. Using, typically, the best linear unbiased projections, EBVs are calculated using attributes measured in own performance, pedigree, sib, and/or progeny evaluation schemes in specialised testing stations and/or selected farm conditions (BLUP; Henderson, 1975). One of the main time-consuming challenges for phenotypic evaluation to enable a meaningful estimation of breeding values is generation intervals of 4 to 5 years in beef cattle, more than 5 years in dairy cattle, and 2 to 2.5 years in pig breeding schemes (Schefers and Weigel, 2012). Modern breeding techniques like marker-assisted selection (MAS) and, more recently, genomic selection (GS) are based on the use of genetic DNA markers to aid in selection and breeding (Williams, 2005). The fundamental reason why MAS, that only further uses a few markers as a selection tool, hasn’t made as much progress as was initially anticipated is that finding trustworthy markers is challenging, particularly when dealing with complex traits. Only relatively few consistent markers could be identified, despite successful identification of many quantitative trait loci (QTL; Hu and Reecy, 2007)

Introduction
The early human beings undertook extensive exploration of wild species in different plant species before selection followed by domestication for food and other purposes. The identification and selection of innumerable wild plant species and their domestication formed an integral part of early agriculture. Thus the organized cultivation of plant species is the result of selection in and among numerous wild species followed by domestication. It started around 9000-11000 years ago and has given birth to agriculture. The evolution of numerous human civilisations/settlements across the globe is largely shaped by agriculture. Selection continued during domestication which has led to the improvement in the wild plant species and transformed wild plant species into cultivated crop plants. Subsequently, the crop plants were differentiated into distinct landraces depending on morphological features, adaptation, economic use, etc. across different continents on Earth. Selection by humans and/or by nature is a continuous and dynamic process and will continue in future as well. However, a deep understanding of genetic diversity in crop species is essential for successful selection by human beings (Naqvi et al., 2022). The objective of the selection and domestication were dynamic and varied depending on cultural, social, economic and climatic factors. In general, the major objective of selection was to increase yield and bring desirable changes in valuable traits of economic importance. The significance of selection has increased over the years and will continue to increase in future as well. The procedure and methods followed while selecting traits or genotypes of economic importance have led to the birth of a new discipline, plant breeding. In the early years, plant breeding was more of an art than a science. As science progressed, the increased understanding of the genetics of traits complemented plant breeding with scientific back-up. Subsequently, genetics has become the basis of selection, the essence of plant breeding.
Globally, the demand for food, fodder, fuel, energy etc. is increasing continuously and increased crop production is considered as the most viable and sustainable answer or solution. The major driving force for increased demand is the growing population. However, the challenges to increasing crop production are also many namely, the decreasing arable land for agriculture, changing climate (increased extreme temperatures and altered rainfall patterns etc.), decreasing natural resources like water and energy etc. (Razzaq et al., 2021). Further, global warming also impacts the overall agricultural production across crops adversely. In this scenario, the role of plant breeding is vital to sustain higher crop production on a sustainable basis. The responsibility of plant breeders also increases in terms of the development of new, high-yielding, stress (biotic and abiotic) tolerant, high-quality and higher resource use efficient genotypes continually. Historically, conventional/traditional plant breeding has made significant progress in several crops in terms of increasing production, productivity, stress tolerance, nutritional value, etc. Conventional plant breeding relied fundamentally on extensive evaluation of available germplasm for a range of traits of economic importance and utilization of genetic diversity found in the landraces and closely related species (primary and secondary gene pools) (Prohens, 2011; Mangal et al., 2024). The success in conventional plant breeding largely depends on the expertise of breeders while undertaking phenotypic selection. It mainly involved selection within broad genetic base germplasm like landraces, open-pollinated varieties, exploration of wild and related species, and their evaluation for traits of interest, followed by hybridization and cyclical selection (Hallauer and Carena, 2009). The

4.1 Introduction
4.1.1 Overview of Genomics in Biotechnology
The genome of an organism serves as the ‘blueprint of life’ (Baeza, 2024) and functions as an information repository (Goldman & Landweber, 2016), containing the necessary information for the synthesis of molecules, cells, and tissues (Abera et al., 2017). A study of the entire collection of genes within an organism’s genome is known as genomics (NIH, 2022; WHO, 2025). Genomics encompasses aspects of genetics, focusing on the comprehensive characterization of an organism’s entire set of genes rather than on individual genes (Baeza, 2024). The investigation of living organisms has undergone a transformation through the application of genomics and related fields, providing unique insights that enhance various applications aimed at improving human life, economic conditions (Kalaitzandonakes et al., 2023), and environmental sustainability (Mudaliar et al., 2023).
According to a genomic analysis, 80% of the 6,000 rare disorders have genetic causes (Brittain et al., 2017). Cancer, a genetic disease, has revealed through DNA sequencing the mutational events and oncogenic drivers (D. Wang et al., 2023). Genomic tests contribute significantly to the prediction and prevention of diseases, including familial hypercholesterolaemia (FH) (Brittain et al., 2017). Agricultural industries are currently confronting multiple challenges, including global changes in climate (Bai et al., 2024), growing populations (Putri et al., 2019), resource depletion (Feng et al., 2023), reduction of cultivable land (Mirzabaev et al., 2023), and the prevalence of pathogens (Hossain & Roslan, 2023). Advances in genomic technologies, such as DNA sequencing and gene editing, have transformed the field. DNA sequencing includes; Nextgeneration sequencing (NGS) (Marudamuthu et al., 2023), Ribonucleic acid sequencing (RNA-seq) (Upton et al., 2023), whereas, gene editing methods includes; Clustered Regularly Interspaced Short Palindromic Repeat- CRISPR associated protein 9 (CRISPR/ Cas9) (Q. Liu et al., 2021), Transcription activator-like effector nucleases (TALENS) Zinc-finger nucleases (ZNF) (Malzahn et al., 2017) and Oligonucleotide directed mutagenesis (ODM) (Ravikiran et al., 2025) as well as doubled haploids (Chen et al., 2024), molecular markers (Kumar et al., 2024) and mapping populations (Temesgen, 2021), offers potential solutions against such agricultural challenges (Hossain & Roslan, 2023). Genome sequencing techniques are applied to various fields of industrial biotechnology such as; strain development or improvement (Salazar-Cerezo et al., 2023), enzyme discovery (Zaparucha et al., 2018) and analysis of microbial community (Hosokawa et al., 2022). The potential to enhance microbial strains through the incorporation, deletion, or alteration of

Abstract

Synonymous and non-synonymous substitution result in isomerization and deformity in protein structure. Both play more or less important role in evolution. Interestingly, cyanobacterial phycobiliproteins (PBPs) are composed of proteins which are coded by codons by mRNA translation. These proteins are categorized into three groups e.g., C-PC, C-PE and C-APC having a and b subunits. The skeleton and arrangement of amino acids in subunits can provide structural and functional integrity of PBPs as well as GC₃ associated substitution of synonymous and non-synonymous codons.

Genome Sequencing: A Continuation of Genetics

• The human genome project involves the assembly of a DNA sequence map for the 3 billion base pairs present in all 23 human chromosomes.
• Two groups, the International Consortium and Celera Genomics, used two opposite strategies to obtain a draft sequence of the human genome.
• The use of bacterial artificial chromosomes (BACs), shotgun clones, and positional cloning has yielded a detailed look at the human genome and is facilitating the identification of disease causing genes.

The Sanger Method of DNA Sequencing

The Sanger method of DNA sequencing involves an enzymatic extension reaction to generate complementary copies of an unknown DNA sequence.

Incorporation of “dideoxy” base causes chain termination and separation of extension products on polyacrylamide gels reveals the sequence bases in the unknown DNA.

DNA sequencing occurs on a vast scale today through the use of automated DNA sequencing machines.

Introduction

Due to global population growth, rising wages, and urbanisation, the demand for animal products has been expanding quickly in many areas of the world (FAO, 2018). Meeting the rising demand for animal products, which is expected to reach 8.6 billion by 2030 (FAO, 2018) as the world’s population is projected to increase by 80 million per year (Preisinger, 2012), is one of the biggest difficulties facing modern farm animal industry (Eggen, 2012). Animal products and value-added foods are being consumed as a result of urbanisation and income growth (Hoffmann, 2005; FAO, 2011). By 2050, the world’s population will be close to 10 billion, even as the economic conditions continue to improve the demand for animal products will significantly rise. In order to boost animal production, it will be necessary to acquire a more in-depth understanding of animal biology through genomics and other related f ields of study. Understanding animal physiology remains a significant problem in the context of sustainable agriculture and animal husbandry, particularly for developing animal farming systems that cater to the new and diversified consumer demand. Livestock and poultry productivity has been greatly increased through traditional selection based on genetic merit estimated from phenotypic and pedigree data. Most breeding plans do not take population impacts on genetic diversity into consideration, and selection is geared more towards the best genetic response for the upcoming generation than the best long-term response (Meuwissen, 1997). Breeders have employed genetic analyses to find superior animals for more than 40 years.

Fruits contribute to nutritional security and thus are an important part of food system. To meet the future demand, development of varieties with improved yield and quality is the focus of researchers involved in fruit breeding. Though conventional breeding has resulted in the development of improved commercial varieties of different fruit crops, however conventional breeding is time consuming and faces several challenges due to long juvenile phase, highly heterozygous nature of most of the fruit crops. Challenges like changing climate, consumer preference, socio-economic conditions like reduced availability of land, labour and increasing cost of inputs, require fast and efficient methods to develop the varieties to meet the demands, which is not feasible with conventional breeding. Marker assisted breeding has given impetus to breeding efforts in several fruit crops, however it’s use is limited only for the traits controlled by a few major QTLs.
During last few years, enormous genomic resources have been developed for fruit crops using next generation sequencing technology. Such advances in genomic research in many fruit crops have paved the way for precision breeding using genomic assisted breeding (GAB) tools. GAB has helped in characterisation and utilisation of genome resources and using allele variation for cultivar development (Varshney et al. 2021). Advances in sequencing technology and affordability has allowed the genome wide analysis (GWAS) on diversity panels of different fruits resulting in identification of allelic variation associated with the traits of interest. Also, identification of superior alleles of candidate genes makes them amenable to genome editing based precision breeding. Genome wide genotyping and phenotyping offers tools for developing strategy for genomic selection (GS). Genomic selection relies on the prediction of breeding values of tested genotypes based on the large number of markers spread over entire genome. These tools promise to further improve breeding efficiency in fruit crops.

Introduction
Recent scientific findings highlight the escalating threats to global agricultural production due to climate change. The surge in extreme weather events, amplified by erratic precipitation patterns, alongside diminishing soil fertility and declining plant productivity, has led to an upsurge in disease and pest pressures. This amalgamation of factors has propelled a distressing trend of crop failures on a global scale. Over the last century, the average global temperature has already risen by 0.74°C, and projections suggest this rise could accelerate dramatically, reaching an alarming 2.6–4.8°C by the close of this century. This impending temperature escalation from climate change endangers agricultural output, thereby posing a signifi cant risk to the world’s food security. Presently, a staggering 794.6 million individuals face undernourishment, with nutrition-related issues contributing to a staggering 45% of deaths in children under fi ve years of age. The situation is particularly dire in developing regions, where 779.9 million people grapple with undernourishment. The repercussions of reduced agricultural production extend beyond hunger; they encompass potential food shortages and infl ationary pressures, predominantly affecting populations in low-income regions. These adverse consequences underscore the imperative of addressing these challenges. While advancements in crop breeding have led to incremental genetic gains in major agricultural crops, the pace of progress remains inadequate to meet the mounting global food demands. It is evident that enhancing the rate of genetic gain through innovative breeding programs is vital. This endeavor holds the potential to expedite the development of crop varieties resilient to changing climates and endowed with higher nutrient densities. Such cultivars are essential for ensuring sustainable food production and bolstering food security for our burgeoning population.

Livestock breeding for genetic improvement faces many challenges including feed conversion efficiency, fertility and adaptation to changing climate. These improvements have to be realized while maintaining or improving the nutritional properties of meat, milk and other animal products while giving emphasis to animal health and wellbeing also. Environmental stresses induced by climate change affect livestock productivity, reproductive efficiency and health, resulting in severe economic losses. Under the current climate change scenario, the viability of producing animals for human consumption is in jeopardy. In order to lessen the negative effects of heat stress in today’s world, it is urgently necessary to develop methods that are centered on breeding animals with increased thermal tolerance and climate resilience. The breeding of animals having genetic potential to sustain under changing environment conditions can be achieved through the various molecular genetics tools.

INTRODUCTION

Throughout the history of agriculture, humans have struggled to reduce the adverse effects of plant disease on their crops. Early agriculturalists realized the benefits of cultural practices such as crop rotation and the use of organic soil amendments in promoting plant productivity. It is now well established that many of these effects are achieved by promoting the natural microbiological processes that keep plant disease in check. Cultural practices were mainstays of traditional agricultural systems and still provide the primary approaches for management of many soilborne diseases today. For example, disease suppressive soils, into which pathogen(s) can be introduced without causing the expected levels of disease severity, can result from alterations in cropping patterns or other cultural practices. Recently,

Genetic improvement of farm animal species is accelerating in developed countries with adoption of Genomic selection (GS) due to availability of complete reference genome sequence i.e. gene sequences that can be used to compare and study animal genetic traits. GS has been considered most promising for genetic improvement of the complex traits controlled by many genes (Quantitative traits) each with minor effects as using the conventional methods of selection and breeding the rate of genetic gain per generation is very slow with respect to human demand for livestock products. With this method, animals or plants can be selected for selective breeding on the basis of their genetic merit predicted by whole genome molecular markers spanning over the entire genome. Genomic selection may allow the identification of superior individuals for traits such as increased disease resistance which are not currently considered in animal breeding plans because of technical difficulties. The method can predict the genetic merit of young bulls with greater accuracy as compared to convention methods of genetic merit estimation. Genetic gain (ΔG) in animal breeding programs depends upon the intensity of selection (i), accuracy of predictions (r), genetic variance (σ2g) and generation interval (IG): ΔG = i * r * σ2g / IG (Ibañez Escriche & Gonzalez-Recio, 2011).