Biomolecules are the backbone of any living organism, and DNA, RNA, protein, and metabolites are the key molecules that define the organism's phenotype. The exponential growth of science and related technologies offers drastic progress in knowledge and the development of high-throughput techniques to understand these biomolecules. Shifting of genes to the genome (-ome, complete set of genes) and their complete study brings the term genomics (-omics, the study of the complete genome). Later, on the basis of the same concept, other -omics branches, i.e., transcriptomics, proteomics, and metabolomics, has emerged that focus on the complete study of transcriptome, proteome, and metabolome. As -omics branches focused on the complete study of interesting -ome, they mainly rely on high-throughput techniques, e.g., next-generation sequencing, microarray, mass spectrometry etc., and provide a meaningful insight into biomolecules. For almost the last one and a half decades, scientists are utilizing these omics in an individual manner to understand the particular biological process and related circumstances in fact, at some level, they succeeded in their objectives, but still, they were unable to clearly understand the role and association of biomolecules in different biological process and effects on phenotype, e.g., disease phenotype. Later, it has been realized that none of the omics is complete in itself and the use of individual omics cannot provide the real picture of any biological process. The emergence of system's biology is based on the concept that assimilation of multiomics data can help us to get understood any kind of physiological process, e.g., diseases. Though there are various dimensions and probable scopes for integrative omics, health science and agriculture are the field where scientists are most exploring it. This chapter aims to discuss various aspects and approaches for multi-integrative genomics i.e., needs of integrative genomics, current status, data mining techniques, and challenges.

At a specific level, omics is described as analyzing the enormous amount of data that portrays the structure and function of the complete makeup of a given biological system. High-throughput biochemical probes that evaluate extensively and concomitantly the same type of molecules from biological samples are defined as Omics technologies. In the beginning, omics experiments provides single-omics data, apart from this type of data, researchers merged different assays from the same set of samples and generated an integrated omics data known as multiomics data. From the novel organism of unknown molecules of a cell, the biological information is utilized to generate different databases for annotation. An expanded number of biological datasets have been developed with the eruption of biological data. Biological databases amalgamate extensive amounts of omics data, performing as extremely important resources and developing vitally necessary for researchers from experimental lab biologists to in silico bioinformaticians. Biological database clasps ample avail for human research and can be considered as a signal to translate large data into huge discoveries. Using web-based resources for the curation, interpretation, and functional relevance of biological data, the available biological tools are utilized. To examine and analyze the extinction and mechanism of the molecule of a cell, the bioinformatics tools are specific. The biological tools help with the recognition of the significant pathways and for prioritizing biomarkers from datasets.

A representation of a collection of relationships between items is called a network. In general, the relationships are referred to as edges, links, or connections, while the entities are termed nodes or vertices. Nodes can represent specific physical objects (like proteins, metabolites, people, computers, etc.) or intricate concepts (such cell type, disease, developmental stage). Biomolecular networks have previously proven to be extremely advantageous in the characterization of intricate biological systems that originate from the interactions between individual biomolecules. Biomolecular networks, though gain popularity only in the past few years, are now frameworks that enable many molecular biology discoveries. A single gene or a small group of genes is unable to fully comprehend many occurrences, including those connected to diseases, due to the strong interaction between the molecular components of living systems. These vast interdependencies are embodied in molecular networks, which depict all kinds of interactions between molecular entities and provide a framework for their mining from a systemic perspective to extract information. Such networks include gene regulatory networks (GRNs), protein-protein interaction (PPI) networks, metabolic networks, cell signaling networks, neuronal networks, disease drug interaction and so on. These networks are used for exploring the tangled phenomena of human diseases from an interdisciplinary viewpoint. They are frequently produced from high-throughput omics datasets.

Pharmacogenomics, nutrigenomics, and microbial omics are rapidly evolving fields with the potential to revolutionize personalized medicine, nutrition, and microbiology. Pharmacogenomics aims to optimize drug therapy based on an individual's genetic makeup, reducing adverse reactions and improving treatment effectiveness. Nutrigenomics explores how genetic variations influence nutritional requirements and health outcomes, leading to personalized dietary recommendations. Microbial omics techniques analyze the collective genomes, transcriptomes, proteomes, and metabolomes of microbial communities, providing insights into their diversity and functions. These fields hold great promise for advancing personalized medicine, nutrition, and microbiology through tailored treatments, personalized nutrition interventions, and a deeper understanding of microbial communities.

Advancements in high-throughput omics technologies enabling rapid profiling of genes, mRNA, proteins, metabolites, metagenomes, etc. have accelerated the research in ecological and environmental omics. Ecological and environmental omics focus on a better understanding of the environmental and genetic factors, chemical toxicity mechanisms/pathways, biomarkers, and modes of action in response to exposure to a single or mixture of chemicals that result in the development of environmental diseases as long-term effects. Environmental omics also aims to investigate the identification of unknown environmental target organisms, environmental monitoring enabling risk assessment, diverse human health outcomes, environmental impacts, ecological functions, and environmental adaptation. Environmental and ecological omics explore acceptable levels and potential impacts of environmental toxicants on environmental target species and ecosystems. Multiomics technologies are also being used in accessing the environment to revise the existing law related to environmental protection. To date, single omics such as transcriptomics (∼43%) are being used frequently compared with multiomics (∼13%) in environmental research, showing the urgent need for multiomics technologies in environmental research. This chapter focuses on the use of various multiomics studies in accessing the environment, exploring the toxicity mechanisms due to exposure to single and mixture of chemicals to the target organisms. This chapter also focuses on the effect of exploring dietary and environmental factors on an organism's genome, environmental monitoring of health risks assessment, etc.

In the era of the “omics revolution,” there is a growing trend in analyzing biological samples, such as genomics, transcriptomics, proteomics, and metabolomics, through the use of high-throughput omic approaches. High-throughput technologies have revolutionized modern research and allowed for the exploration of the connections between food, nutrition, human health, and diseases, giving rise to the field of “Foodomics.” Foodomics utilizes high-throughput technology to enhance nutrition in humans by investigating the science of food. Various domains, including transcriptomics, proteomics, and metabolomics, are dynamic and can be influenced by external factors like food and diet. By studying the effects of food and nutrition on transcriptomes, proteomes, and metabolomes (known as integrative omics), researchers gain insights into how different food products impact us individually, ultimately contributing to the betterment of human health and well-being. This article covers the following topics: Introduction to foodomics, high-throughput technologies in foodomics, understanding the functionality of food components, and foodomics relationship to human health.

In the rapidly evolving field of Omics research, computational tools play a crucial role in analyzing and interpreting vast amounts of data generated by high-throughput technologies. However, with an ever-increasing number of tools available, it has become essential to evaluate their performance systematically. We present an overview of the systematic benchmarking of Omics computational tools. The methodology for benchmarking is discussed, including selecting representative tools, identifying benchmark datasets, and establishing performance metrics. Various criteria for evaluation, such as accuracy, computational efficiency, user-friendliness, and compatibility with different Omics data types, are outlined. The benchmarking results and analysis provide insights into the strengths and limitations of each tool, facilitating informed tool selection. Additionally, case studies demonstrate the practical applications of benchmarking results in specific Omics domains. The article also discusses future directions, challenges, and the impact of benchmarking on advancing Omics research and applications. Systematic benchmarking of Omics computational tools enhances the reliability and reproducibility of analyses, leading to improved data interpretation and scientific discoveries in various fields, including genomics and proteomics.

In a typical world of biological data, like other natural languages, omics can be viewed as another set of biologically meaningful languages be it the 4 alphabet-based DNA or RNA or the 20 alphabet-based protein. Therefore, computationally, there was never an end of challenge in extracting meanings from such omics language. Conventionally, machine learning methods under the paradigm of artificial intelligence are supposed to address the problems of systems of higher complexity in comparison with that commonly addressed by statistical methods. However, the earlier machine learning attempts for extracting information from omics data were not found to be promising due to higher degree of difficulty as already pointed out and the lack of researchers to interface both the area of biology and computation. This chapter is an effort to bridge this gap by offering the primary knowledge of machine learning and its utility to explore complex omics data.

Significant progress in sequencing technologies and bioinformatics tools has facilitated the progress of genomics in recent years. Next-generation sequencing (NGS) platforms along with artificial intelligence, bioinformatics, and automation helped in understanding and analyzing the genomes of complex biological systems. The NGS acts as a molecular microscope to study the macromolecules such as candidate genes, variants like single nucleotide polymorphisms (SNPs), copy number variation (CNV), structural variation (SV), insertion-deletion (INDEL), and simple sequence repeats (SSRs). Similarly, the transcriptome profiling reveals the expression of gene in the genome and widely used to study the gene under biotic and abiotic stresses. In addition, the noncoding RNA molecules like microRNAs (miRNAs), long noncoding RNAs (lncRNAs), DNA methylation, and alternative splicing event can be investigated from the transcriptome data. Bioinformatics analyses of huge volume of data require a wide range of tools, databases, and diverse skills in computing, statistics, and technologies such as cloud computing and high-performance computing.

An important goal of omics-based research is to apply molecular knowledge from omics studies to address medical issues, consequences, and challenges such as survival time in prostate cancer, risk of breast cancer reappearance, or reaction to treatment. For instance, genomics examines a huge number of DNA sequences, while transcriptomics, proteomics, and metabolomics investigate vast amounts of proteins and metabolites, respectively. Complex high-dimensional data are generated by evaluating many factors per sample through omics research. These data can be utilized to construct a computational model, which is designed for future analysis of specific patient samples in a healthcare context and may distinguish a health-related characteristic of therapeutic importance. A developed computational model may be more relevant and accurate for the samples used for the discovery study but may lead to incorrect findings and outcomes for the other sample due to the high dimensionality of data. The omics-based test may be very useful for clinical decision-making, patient management, and treatment. Omics-based tests are very useful in prognosis, diagnosis, and patient stratification, recommending the drug and dose, and analyzing the outcomes of therapies. Clinically validated, highly specific, and sensitive omics-based tests may be a potential basis for the successful implementation of personalized therapy.