Biological Insights of Multi-Omics Technologies in Human Diseases

"Biological Insights of Multi-Omics Technologies in Human Diseases´ provides detailed information about the basics of multi-omic technologies including ethics, historical perspective, science, drug discovery, and development and metabolism. With a strong focus on the practical application of omics approaches in cancer, cardiovascular, neurology, respiratory, viral, gastroenterology, autoimmune diseases, PCOS and tuberculosis, this book also includes special topics related to COVID-19 and Machine learning approaches.

In 13 chapters this book provides comprehensive coverage of the challenges and opportunities facing the therapeutic implications of multi-omics from academic, regulatory, pharmaceutical, socio-ethical, and economic perspectives. The chapters are designed in a well-defined chronology such that readers will intuitively understand the central idea. This book is an ideal resource for health professionals, scientists and researchers, nutritionists, health practitioners, students, and all those who wish to broaden their knowledge in the allied field.

• Explains the in-depth role of multi-omics on drug discovery/metabolism, diseases, and highlights progress in both the research and clinical areas of computation, as well as relevant implementation experience and challenges. 
• Describes the practice of multi-omic technologies in the treatment of several diseases.
• Includes practical application and machine learning approaches of multi-omics.

• Language: English
• Publisher: Academic Press
• Release date: May 23, 2024
• ISBN: 9780443239700

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Chapter 1: Multiomics approaches in human diseases

Mashooq Ahmad Dar, and Urszula Wojda∗     Laboratory of Preclinical Testing of Higher Standard, Nencki Institute of Experimental Biology of Polish Academy of Sciences, Warsaw, Poland

∗ Corresponding author. E-mail address: [e-mail] 

Abstract

Human diseases involve a complex crosstalk between factors like genetic, environmental, demographic, and lifestyle. In complex diseases, this crosstalk between different factors is very crucial for the disease outcome. The publication of human genome project has brought a revolution in the medical research. Post human genome publication, researchers started to look into the genetic mechanisms involved in human diseases. Omics approaches such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, etc., have been utilized for understanding the basic molecular mechanisms of a disease. Multiomics approach, which integrates different omics technologies in order to have a better understanding of genetics of human diseases, has revolutionized the fields of disease diagnosis and prognosis related to biomarker detection. Scientists have discovered single nucleotide polymorphism locations in almost all the human chromosomes to identify the genes that are related to diseases. For understanding the relationship between genetic variations and disease risks, genome wide association studies play a crucial role. By high-throughput sequencing platforms, differences between the gene expressions patterns of diseased and healthy controls can be studied. Over the last few decades, transcriptomic profiling is gaining much popularity for studying human diseases. The role of various noncoding RNAs like long noncoding RNAs, microRNAs, circular RNAs, etc., in human diseases is an emerging research area. Proteomics, metabolomics, and lipidomics approaches have explained the role that different biological molecules carry out in an organism. Discovery of different novel genes and pathways, which have a role in a disease, has been possible only because of the omics approaches. The multiomics approach has paved the way for integration of the omics data in order to facilitate and manage data analysis for identification of disease biomarkers. In this chapter, we will discuss the role of multiomics approach as a promising and potential tool for studying human diseases.

Keywords

Biomarkers; Human diseases; Omics; Personalized medicine

1.1. Introduction

Human diseases involve complex interactions between genes, lifestyle, and environment. Diseases are accompanied by various changes in the cellular and molecular dynamics such as gene and protein expression or metabolic pathways that can be the cause or consequence of the underlying pathological process. Most human diseases have multifactorial causes and there is a need to understand the different interactions that are going on within an organism during disease. In the recent past, a huge number of innovations have been witnessed by biological sciences from micro to macro levels. These innovations have enabled a better understanding of different phenomena that control various cellular processes and functions. In the year 2003, biological and genomic research saw a breakthrough in the form of human genome project publication that led to a great revolution in the field of biomedical sciences (Dar et al., 2023). The reference human genome, high-throughput genotyping, and rigorous statistical methods have allowed researchers to map hundreds of genetic variants that contribute to different diseases (Hasin et al., 2017). Further advancements in omics approaches (genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, etc.) have made personalized medicine possible at the molecular level. Omics refers to different techniques that assist in investigating the role and function of different cellular molecules. The omics technologies include genomics (DNA), transcriptomics (RNA), proteomics (proteins), metabolomics (metabolites), lipidomics (lipids), epigenomics (epigenetic modifications), etc. The omics techniques have a great role in the discovery and identification of various disease biomarkers, and therapeutic agents and have also helped in vaccine and drug development/management (Dar et al., 2023). The omics approaches utilize different high-throughput technologies for the discovery of disease biomarkers (Fig. 1.1). With the inclusion of high-throughput technologies in the omics field we have been able to do cost-effective, extensive analysis of different biological samples. These technologies have been extensively used for the discovery, effectiveness, and cellular toxicity of drugs (Chen et al., 2020; Goff et al., 2020). The omics data provides details regarding the differences associated with the diseases which are vital for the discovery of biomarkers and for monitoring the disease's progression. Further, this data helps us to have a much deeper understanding of the different pathways and processes that are altered in the diseased state compared to the healthy controls. However, the analysis of the data from just one of the omics approaches limits our conclusions regarding the pathophysiology of a particular disease. Therefore, there is a need for integration of different omics data types to have a better understanding of the dysregulations that lead to the disease and also to identify different therapeutic targets (Hasin et al., 2017). This integration of different omics data is referred to as multiomics. In this chapter, we will discuss different omics technologies and their role in various human diseases as shown in Fig. 1.1.

Figure 1.1  Biomarker discovery using omics approaches. CircRNA , circular RNA; GWAS , genome-wide association studies; lncRNA , long noncoding RNA; miRNA , microRNA; mRNA , messenger RNA; MS , mass spectrometry; WES , whole exosome sequencing; WGS , whole genome sequencing.

1.2. Multiomics strategies

Multiomics aims to integrate two or more data sets from omics approaches to have a clearer data analysis and interpretation to elucidate the mechanism of a particular biological or pathological process. Multiomics approaches are the area of hot research in biomedical sciences, as researchers are now able to discover novel biomarkers and provide new insights into biological processes and pathways.

1.2.1. Genomics

Genomics refers to the study of the organism's total genome. In humans, the haploid genome consists of around 20,000 genes encoded by around three billion base pairs (Manzoni et al., 2018). Genomics is the most mature omics field and has been utilized in biomedical science for the identification of different genetic variants that are associated with disease diagnosis, response to medication, and prognosis (Hasin et al., 2017). Human health is influenced by many factors, and it is a well-established fact that the genetic background has an important role in determining the health state of an individual. The study and examination of the genetic background of an individual is therefore very important to identify mutations or variations that discriminate health and disease (International Human Genome Sequencing Consortium, 2004). Genomics methods have been very helpful in the identification of markers for different cancers, cardiovascular diseases (CVDs), and metabolic and neurodegenerative disorders. In many human populations, GWAS studies have been utilized for the identification of different genetic mutations that have been further linked to human diseases. In GWAS, thousands of individuals are genotyped and different genetic markers are assessed between the diseased and the healthy controls. GWAS studies provide invaluable information to understand the genetic background of diseases. Some of the technologies associated with the GWAS are genotype arrays, whole genome sequencing, and exosome sequencing (Hasin et al., 2017). GWAS in most of the cancer types have been performed and approximately 450 genetic variants that are associated with high cancer risks have been identified (Sud et al., 2017). The Genomics approach is believed to be the most important approach for the diagnosis of CVDs. Genome-wide association studies for SNP determination have helped the researchers to identify the candidate genes for CVDs. For generating precise therapies for CVDs, genomics-related profiling of CVD risk seems to be a promising approach. DNA sequence variation is the outcome of a single gene variation and using next-generation sequencing (NGS), these gene abnormalities were illustrated in CVDs (Roberts et al., 2013). Moreover, genetic analysis studies have enabled the identification of many genes that are responsible for the development of different neurodegenerative disorders. Recently with advancements in the GWAS and NGS, GWAS studies were carried out and an allele of the apolipoprotein E (ApoE) gene was identified to have a link with Alzheimer's disease (AD) (Annese et al., 2018). GWAS helps us to identify loci that harbor casual variants, but it is unable to distinguish these variants from the neighboring variants that are associated with the disease by being linked to the causative variants. Further, the identified loci mostly contain multiple genes that might be involved in the disease progression. So, although GWAS data may be helpful in risk predictions, it does not directly suggest a particular gene or pathway as a therapeutic target. To meet this limitation, transcriptomics employing expression array or RNA sequencing has been used for the identification of causal genes at the GWAS locus.

1.2.2. Epigenomics

Epigenomics is the study of modifications in the DNA or DNA-related proteins. Modifications like DNA methylation, histone modification, and chromatin interactions are investigated using the epigenomics approach. The epigenetic control of DNA is crucial for determining the fate and function of the cell. Epigenetic modifications can act as markers for many diseases like cancer, cardiovascular diseases, metabolic disorders, neurological disorders, and many more. These changes can be tissue or cell-specific and can be observed in both healthy and diseased states. Epigenomics explains the changes that occur in the gene expression without modification of the gene sequences (Piunti and Shilatifard, 2016). Further, it also involves the characterization of the chromatin structure and methylation status of the nucleic acids (Wang and Chang, 2018). Different techniques are being employed to study different alterations that take place in the DNA and histone proteins. Hi-C is a widely used technique that can capture chromosome conformation. Similarly, chemical modifications such as DNA methylation can have a great impact on gene expression. For the detection of DNA methylations, whole genome bisulfite sequencing is the standard approach. Illumina short-hand sequencing and immunoprecipitation have been used together for the identification of epigenetic modifications in a wide range of genomic regions. Further, long-read sequencing technologies that include PacBio and Oxford nanopore sequencing have also been utilized for the investigation of epigenetic alterations (Dai and Shen, 2022). These studies have reinforced the view that molecular changes leading to cancer are not only limited to genomic mutations but also occur in the epigenome. For example, the silencing of various tumor suppressor genes can be achieved by aberrant DNA methylation and histone modifications (Berdasco and Esteller, 2010). Epigenomics sequencing techniques have enabled epigenomic molecular phenotyping of the human brain, and have also aided in epigenomic diagnosis of brain cancer (Euskirchen et al., 2017; Farik et al., 2015). Cancerous cells have abnormal epigenomes, i.e., aberrant DNA methylations, histone modifications, and 3-D chromatin structures. These alterations in the epigenome can be utilized for the detection and classification of various cancers (Dar et al., 2023). Comparative studies of genome histone modifications have made it possible to have a deep understanding of epigenetic dysregulations in certain cancers (Chakraborty et al., 2018).

Epigenetic regulation plays a particularly great role in the regulation of neurological processes. In the cortex of the brain, DNA methylation alterations have been linked to Alzheimer's disease (Sanchez-Mut and Gräff, 2015).

1.2.3. Transcriptomics

The transcriptome refers to the total RNA transcripts present in a cell and consists of both coding RNA (1%–4% messenger RNA) and noncoding RNA (>95% ribosomal, small nuclear, transferring, small interfering, micro and long noncoding, circular RNAs) (Manzoni et al., 2018). Transcriptomics refers to the study of RNA transcripts both quantitatively and qualitatively, i.e., in terms of the expression changes (quantitative) or some changes like alternative splicing/posttranscriptional modifications (qualitative) (Wu et al., 2021; Flynn et al., 2021). Transcriptomic studies are performed by either using mRNA expression microarrays or by RNA sequencing to quantify all the transcripts that are present in a given sample. The main application of these approaches is to study the comparative gene expression levels between the diseased and the healthy controls. Such types of studies not only help in the comprehensive understanding of the disease pathogenesis but also the identification of disease biomarkers. For clinical practices, techniques like microarray, real-time quantitative polymerase chain reaction (RT-qPCR), and RNA sequencing are employed (Dar et al., 2023). RNA sequencing could help in the detection of early and high molecular risk mutations thereby aiding in the discovery of novel cancer biomarkers, and therapeutic agents and designing treatment strategies in cancer patients. It was through RNA sequencing that isocitrate dehydrogenase 1 and mesenchymal–epithelial transition (MET) exon 14 mutations were identified as potential therapeutic targets in chondrosarcoma and lung adenocarcinoma patients respectively (Davies et al., 2019; Nakagawa et al., 2019). The dysregulation of the mRNA levels as well as other noncoding RNAs has been shown in many diseases like cancer, diabetes, neurodegenerative disorders, stroke, left ventricular dysfunction, atherosclerosis, atrial fibrillation, etc. Likewise, the noncoding RNAs have been linked to different diseases (Mohammadi-Shemirani et al., 2023; Dar et al., 2023). In colorectal cancer (CRC), an alteration in the different RNA types has been shown to increase the potential of these RNAs as CRC biomarkers (Yang et al., 2018). MicroRNAs (miRs) such as miR-106b, miR-221, and miR-20a were confirmed to be the early biomarkers in gastric cancer (Cai et al., 2013). In cardiovascular diseases, the role of RNA molecules as biomarkers has also been explained. Further, RNA expression patterns in different groups under study have enabled the researchers to distinguish between different CVDs such as hemorrhagic stroke or ischemic stroke from healthy groups (Stamova et al., 2010). Another example is transcriptomics studies which have enabled the identification of candidate gene gap junction protein alpha 1 (GJA1) gene as a target for Alzheimer's disease (AD) (Xia et al., 2022). For neurodegenerative disorders like AD many biomarkers that include different mRNAs and miRNAs have been identified from different samples (CSF, blood, plasma, cells, etc.) have been reported (Nagaraj et al., 2019; Lake et al., 2021).

Much evidence is now available that supports the use of these RNA molecules as biomarkers. Furthermore, these findings can provide new therapeutic targets at the mRNA and miRNA levels for the synthetic antisense oligonucleotides (ASOs). Recent advances in the design, synthesis, and targeting of brain mRNA and microRNA with various ASOs including miRNA mimics or antagomiRs allow therapeutic interventions at posttranscriptional regulation and may offer even enhanced effects over protein-targeted therapeutic strategies (Grabowska-Pyrzewicz et al., 2021). Another approach broadly employed for the identification of new therapeutic targets is proteomics which focuses on the comparative analysis of protein levels or/and their posttranslational modifications in norm and disease states.

1.2.4. Proteomics

Proteomics is a term referring to the study of all the proteins that are expressed and their interaction network in a cell, tissue, or organism. The exact number of peptides/proteins is still unknown but the approximate number is believed to be about a few hundred 1000 (Nalbantoglu and Karadag, 2019). In a proteomics approach, extensive studies are employed for the detection, identification, and characterization of different proteins produced in the cell and/or secreted to the circulation. The most commonly used proteomics approaches for isolating differentially expressed proteins between diseased and healthy samples involve a combination of two-dimensional (2D) electrophoresis and mass spectrometry (MS). After protein isolation by 2D electrophoresis, MS can be used to identify the isolated protein by surface-enhanced laser desorption/ionization-time of flight (SELDI-TOF) or matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF). To identify the original protein, the isolated proteins are broken down into peptides, and then their mass-to-charge ratio is estimated based on their time of flight in the presence of an electric field. The results obtained are then compared with a known protein database. Quantitative proteomics is a very crucial aspect in proteomics studies because the fundamental rule for the selection of a biomarker depends upon the differences in the abundances of such protein in the control and diseased samples (Dar et al., 2023). Although MS is not inherently quantitative, various techniques have been developed to obtain quantitative results. Liquid chromatography–mass spectrometry (LCMS) and gas chromatography–mass spectrometry (GCMS) are MS techniques coupled with chromatography with several advantages over MS alone. LC-MS has been utilized for the characterization of different types of proteins with a broad range of physiochemical properties and molecular weight. The use of GCMS is rare and is limited to characterizing relatively volatile molecules with lower molecular weight (Dai and Shen, 2022). Using proteomics approaches biomarkers for many diseases that include cancer, Parkinson’s disease, Alzheimer’s disease, diabetes, cardiovascular diseases, etc., have been identified (Amiri et al., 2018). For example, comparative proteomic studies between blood plasma samples from CVD patients and healthy controls showed altered protein expression in the CVD patients thus highlighting the potential role of these proteins in CVDs (Lygirou et al., 2018).

1.2.5. Metabolomics

The study of the metabolome in cells, biofluids, tissues, or an organism is known as metabolomics. It is also defined as the study involving small molecules and their interactions within a defined biological state under a given set of nutritional, genetic, and environmental conditions. The interactions and changes in the expression of genes/proteins and the environment are straightforwardly reflected in the metabolome, thus making it relatively more complex (both physically and chemically) than other omics for analysis (Dar et al., 2023). Metabolomics may be divided into targeted and untargeted metabolomics. Targeted metabolomics refers to the quantitative analysis, i.e., it reveals the quantity of a metabolite, while untargeted metabolomics reveals all the metabolites that are present in a given sample (Zhang et al., 2023).

The different methods that are employed for the metabolome analysis are Fourier transform infrared (FITR) spectroscopy (Neto et al., 2022), Raman spectroscopy (Lin et al., 2020), nuclear magnetic resonance (NMR) spectroscopy (Scott et al., 2021), LCMS (Carriot et al., 2021), and GCMS (Taya et al., 2021). Of all the omics approaches, metabolomics is the closest to the phenotype and therefore may be the best representative of the molecular phenotype of the healthy and diseased state. In this context, the metabolome is regarded as an excellent source of disease-related biomarkers (Gujjas et al., 2018). Metabolome profiling has been used for the identification of metabolites and their associated candidate genes (Meng et al., 2021), and also for revealing the metabolic mechanism of the therapeutic efficiency (Qi-Shun et al., 2021). For example, using an LC-electrochemistry array, a significant reduction in homovannilic acid, L-dopa, dihydroxyphennlacetic acid, and dihydroxyphenylglycol in Parkinson disease (PD) patients has been reported (Shao and Le, 2019). Furthermore, the levels of 8-hydoxy-2-deoxyguaonosine and 8-hydoxyguanosine were reported to be significantly higher in PD patients concerning healthy controls, thus making them useful as PD biomarkers. All the recent advances that have taken place in metabolomics-based research in PD have been summarized in (Shao and Le, 2019).

Nuclear magnetic resonance–based metabolic profiling of blood samples revealed a potential role of metabolomics in many diseases like cardiovascular diseases, Alzheimer's disease, diabetes, COVID-19, and others (Buergel et al., 2022). Also, metabolomics techniques were used for the study of the metabolome in different samples of autoimmune liver disease (AILD) patients; this study demonstrated that AILD was gender-dependent and women were found to be more susceptible than men (Adil et al., 2022). The Metabolomics approach seems to have a promising potential for the identification of metabolites as disease markers in different biological fluids and thereby provides an opportunity for assessing disease progression, and biomarker selection and also helps in understanding the pathophysiology of the disease. A good example is the study in which the data from metabolomics analysis in the serum of gastric cancer patients showed differences in comparison to healthy controls. Twelve different metabolites were identified that could help diagnose gastric cancer and further, these metabolites could also be used for distinguishing early and advanced gastric cancer stages. In addition, altered citric acid cycle, linoleate metabolism, and amino acid metabolism were found in gastric cancer patients (Yu et al., 2023).

1.2.6. Lipidomics

Lipids play an essential role as a constituent of cellular and plasma membranes. They constitute the cell membrane as the main barrier separating a cell from its environment and are important for efficient cell growth, proliferation, differentiation, and signaling. Lipidomics is an evolving field that links lipid biology, technology, and medicine to build an all-exclusive atlas of cellular and tissue lipidome (Lam et al., 2017). In lipidomics, lipids are studied using analytical chemistry principles in addition to technological tools. Alterations in the lipid profile of an organism, tissue, or cell are associated with many cellular processes like cell growth, differentiation, signaling, and cell death (Santos and Schulze, 2012). The application of mass spectrometry–based shotgun lipidomics (MS-ML) has been widely used for discovering lipid biomarkers that may be helpful in cancer diagnosis. Lipids, particularly phospholipids, are the main component of all cells, so during cancerogenesis there are alterations in the lipid profiles. Consistent with this observation, in prostrate tumors, an upregulation of fatty acid biosynthesis and reprogrammed composition in the phospholipids were reported (Li et al., 2016). In addition, dynamic remodeling of lipidome during tumor progression has been reported using global liquid chromatography–mass spectrometry (LCMS)-based lipidomics (Li et al., 2016). In another study, multidimensional mass spectrometry–based shotgun lipidomics (MDMS-SL) was used to study the relationship among aberrant lipid metabolism, oxidative stress, and proinflammatory cytokines production in systemic lupus erythematosus (SLE) patients. It was found that the lipid profile alterations were evident in the serum of SLE patients (Hu et al., 2016). The lipid profile study has been also very beneficial for the identification of different lipids in cardiovascular diseases (CVDs). Studies have found an association between lipid profile and risk for CVD using different lipidomics techniques (Ding and Rexrode, 2020). In brain research, lipidomics has been also used for the discovery of early biomarkers. So far various lipid types and their dysregulation have been linked to neurodegenerative disorders (NDs) and other mental illnesses (Yoon et al., 2022). No doubt brain lipidomics is still in its infancy across the globe but the huge amount of data that is obtained through lipidomics may serve as a steppingstone for data-driven clinical brain research.

In summary, omics approaches have been applied in various aspects of basic research and clinical applications ranging from pharmaceuticals, diagnostics, therapeutics, pharmacogenomics, disease prevention, to gene therapy. Some of the utilization of some omics approaches for disease diagnosis, prevention, drug discovery, and biomarker validation has been graphically shown in Fig. 1.2.

Figure 1.2  Integration of omics approaches for disease diagnosis, prevention, drug discovery, and biomarker validation.

1.3. Ethics

Ethics is a branch of philosophy that is concerned with the values connected to human behavior in terms of the rightness and wrongness of actions, the goodness and badness of motives and purposes, and an individual's moral beliefs (Reynnells, 2004). The use of the term bioethics was first applied to the ethical challenges that arose from the developments in biology and medicine, including policy and practice. Through genomics practices, we can modify the gene pool of a particular (target) species in favor of better traits. The final consequence of this gene alteration in the gene pool may be predicted but its actual effects on the species in their natural habitat are not clear. Therefore, it is a moral obligation of the researchers to have careful consideration before performing such activities. Further, the risks associated with such modified species include changes in the biotic components of the ecosystem (Sharma et al., 2021). To prevent such risks, many countries have introduced strict ethical and legal regulations, as well as requirements for research standards, especially research on genetically modified organisms and genetically modified microorganisms. Omics technologies have a great impact on the biological sciences especially in the medical science field. Many of these techniques need further refinement and the data obtained needs to be carefully integrated for meaningful conclusions. Further, we need to pay attention to some biosafety measures and the ethical issues associated with omics technology. The majority of the new approaches in any discipline of research have some controversies and ethical issues related to it. Many people believe that the new approaches in omics technologies can harm societies. Among these people, some have well-founded arguments to support their claims, so their point of view is valid (Wang et al., 2016; Dalakouras and Papadopoulou, 2020). The use of omics data poses important ethical questions about data privacy, informed permission, and the potential for stigmatization and discrimination (Takashima et al., 2018). Omics technologies generate vast amounts of data, which consequently give rise to numerous concerns regarding privacy and discrimination. The accessibility of this data will significantly impact patient–physician relationships and may lead to an increase in litigation (Brothers and Rothstein, 2015). To address these challenges, it is crucial for researchers, organizations, governmental entities, and other institutions engaged in developing, sharing, and operating various databases/repositories to be aware of the sensitivity of the issue and the potential risks associated with sharing data, particularly familial data.

The principles of ethics in scientific research and the protection of sensitive data are currently the subject of special attention within the scientific community, as well as being governed by legal regulations and requirements for research projects. With technological advancements, legal standards regarding ethical issues should be improved and adapted accordingly.

1.4. Historical perspective

Omics is a field of science and engineering that examines the functions of biological information entities and their interactions in different –ome layers or clusters of life. The addition of omics to a molecular term signifies an in-depth analysis of a set of molecules (http://omics.org/). One of the main driving forces behind omics research has been the development of technologies that enable low-cost, high-throughput analysis of biological molecules. For example, the development of the expression array in the late 1990s was based on the phenomenon of cDNA hybridization with oligonucleotide probes. With advancement, array-based technologies have become able to quantify all the protein-coding transcripts within a tissue. Many areas of biology, especially the evaluation of disease, immediately found a use for the ability to examine global gene expression patterns. Expression quantitative trait loci (eQTL), which have proven essential for understanding/interpreting GWAS and modeling biological networks, were also mapped using array technologies in the early 2000s. Since that time, numerous omics technologies having capabilities to cross-examine whole pools of genome (genomics), transcripts (transcriptomics), expressed proteins (proteomics), and metabolites (metabolomics) have been developed.

Genomics was the first omics field to arise, centered on studying the full genome as opposed to genetics that evaluated individual variations or single genes. A very important framework was provided by genomic researchers to map and study specific gene variations that contributed to Mendelian as well as complex disorders (Hasin et al., 2017). Transcriptome studies examine RNA levels in the entire genome both qualitatively and quantitatively (RNA editing sites, transcript presence, discovery of novel splice sites). According to the central dogma of molecular biology, RNA is a molecular bridge connecting DNA and proteins, which is considered to be the main functional read-out of DNA. Extensive transcriptomic investigations in the past have demonstrated that almost 80% of the genome is transcribed and only 3% of the transcribed genome codes for proteins (ENCODE Project Consortium, 2012). RNA sequencing studies have led to the discovery of numerous novel isoforms and further revealed the complexity of the protein-coding transcriptome (Trapnell et al., 2010). However, with the advances in noncoding RNA research, these complexities became more and more easier to understand. Currently, it is a well-established fact that a large number of long noncoding RNAs that are transcribed play important roles in numerous biological processes. These noncoding RNAs are involved in endocrine regulation (Knoll et al., 2015), neuronal development (Yao et al., 2016), brown adipose differentiation (Alvarez-Dominguez et al., 2015), etc. Long noncoding RNA dysregulation is linked to various illnesses like myocardial infarction (Ishii et al., 2006), diabetes (Arnes et al., 2016; Morán et al., 2012), cancer (Gupta et al., 2010), and others (Schmitz et al., 2016). Coming to the protein levels, mass spectrometry–based protein quantification methods are employed to quantify peptides and study their modification and interactions. This quantification and interaction analysis is further aided by proteomics technologies. Mass spectrometry has revolutionized the protein quantification analysis methods and recently these techniques have been adapted for high-throughput investigations of hundreds of proteins within an organism (Walhout et al., 2012; Selevsek et al., 2015). Conventional unbiased techniques like yeast two-hybrid system and phage display can be used to determine protein interactions. Metabolite concentrations and relative ratios represent metabolic activity, and abnormal changes are often a sign of disease. To study metabolite flux, metabolomics, and modeling have been employed extensively. MS-based methods to measure relative and targeted small molecule abundances are among the related technologies (Dettmer et al., 2007; Madsen et al., 2010; Steuer 2006; Patti et al., 2012; Joyce and Palsson, 2006; Dunn et al., 2011).

Each kind of omics data provides a set of differences in the samples that are related to the illness. In addition to serving as indicators of the disease process, these data can also help us to locate different cellular pathways that are altered in the diseased state. Correlations, which often reflect reactive processes rather than causative ones, are the only results possible from a study of a single data type. To identify probable disease-causing changes or potential therapeutic targets that may be investigated in additional molecular research, the integration of several omics data types is frequently used. Omics investigations on both human and animal models offer crucial details about a disease. Various human-centric consortia have produced a sizable body of epigenomics and transcriptomics data in different tissues, for instance, the Roadmap Epigenomics Project (www.roadmapepigenomics.org/) and GTEx (www.gtexportal.org/home/) analyzed epigenomic signatures and transcriptomics in various tissues and cell types in humans. Furthermore, several sizable biobanks have been established to gather, preserve, and examine thousands of human samples associated with diseases (Hasin et al., 2017). For example, the National Institute of Health and Care in Finland established a nationwide network of biobanks (http://bbmri-eric.eu/) to gather samples and measurements from patients with various disorders. To understand the molecular networks controlling the change from health to disease, multiomic analysis can also aid in defining links between different forms of genomic data (Hasin et al., 2017).

1.5. Advantages and disadvantages of omics technologies

The genomics approach which employs different molecular techniques like RFLP, ALFP, PCR, and DNA microarrays has been crucial for the identification of SNPs. These identified SNPs are helpful for the early detection, diagnosis, and prevention of some common diseases. Further studies related to metabolic enzyme polymorphisms have been very advantageous in demonstrating the susceptibility of an individual to certain drugs. Using genomic techniques like karyotyping, fluorescence in situ hybridization, microsatellite instability (MSI), etc., we can analyze whole chromosomes, and specific genes/alleles. Transcriptomics approaches have identified different genes and pathways responsible for different diseases. The quantification of the RNA abundance can be exploited for the discovery of disease biomarkers. Proteomics approaches have the advantage of searching for disease biomarkers as they can be used for quantification of proteins that have low abundance in a complex sample in a fast, simple, and reproducible procedure. In biomarker research, metabolomics has comparatively more advantages than other omics approaches as the endogenous metabolites are lesser in number and there is comparatively less data to be analyzed and interpreted.

Although omics technologies offer a lot of advantages, still there are some disadvantages like the inability to predict the DNA changes that occur because of the posttranscriptional and posttranslational modification in the case of the genomics approach. Further, transcriptomic approaches are costly and require fresh samples and expensive instruments (Karahalil, 2016; Lay et al., 2006). Other disadvantages are the generation of huge amount of data that needs much more rigorous handling. Some of the advantages and disadvantages of omics approaches have been discussed in