Keywords

1 Introduction

Translational research utilises scientific findings produced in the lab, clinic, or field and turns them into novel therapies and medical care methods that directly enhance human health. Translational research aims to transfer fundamental scientific discoveries into application more rapidly and efficiently. When it comes to productivity and translating research into new healthcare advancements, it provides a wide range of specialised resources. Research that fosters and supports multidisciplinary collaboration between laboratory and clinical researchers considers the requirements of communities, and discovers and promotes the adoption of best medical and healthcare practices are all part of translational research (Gochhait et al., 2021).

According to the stage in the translation process (from the commencement of research to social application and effect), translational research is categorised. The T-Spectrum (Translational Spectrum) below depicts the many stages of translational research and development. Transcription, translation, and mRNA and protein turnover are all part of gene expression. What happens when this dependence breaks down? (Buccitelli & Selbach, 2020). A post-translational modification (PTM) affects the fate of proteins in eukaryotic cells. Many web-tool predictors for different PTMs are launched to help diagnose and prevent illnesses (Mohabatkar et al., 2017).

Information technology and databases are used in bioinformatics research to solve biological questions. Genomic and proteomic bioinformatics applications are of global importance. The study of genomes is known as genomics or genome research. A whole genome is a collection of DNA sequences that contain the genetic information that has been passed down from one generation to the next over the ages. It is a physical and functional unit of heredity that is passed from parents to offspring via genetic inheritance. To summarise: Genomic research includes the sequencing and analysis of all genetic material in an organism—from genes to transcripts. To put it another way: Proteomics studies all proteins together called the proteome. Beyond genomics and proteomics, bioinformatics is utilised in a wide range of biological disciplines (i.e. metabolomics, transcriptomics). Bioinformatics is a branch of study that aims to understand complex biological processes via the use of computers. Protein structural alterations, activities, and functions are regulated by post-translational modifications (PTMs) in almost every biological process and activity. Understanding cellular and molecular processes begin with protein PTM identification. However, unlike tedious trials, PTM prediction utilising different bioinformatics methods may offer accurate, convenient, and efficient techniques while also providing important information for future studies (Liu et al., 2015). The introduction of next-generation sequencing (NGS) technology has accelerated the identification of prostate cancer biomarkers Prostate cancer diagnosis and prognosis remains difficult despite the deluge of sequencing data. Chen et al. discussed high-throughput sequencing’s recent advances in prostate cancer biomarkers (Chen et al., 2013a).

Many scientists now refer to systems biology as the next wave in bioinformatics. Integrating genetic, proteomic, and bioinformatics data creates a holistic perspective of a biological entity. Systems biology may study how a signalling pathway operates in a cell. Systems biology can simulate the genes involved in the process, their interactions, and how changes affect downstream consequences. Bioinformatics may be used in any system that can express information digitally. From single cells to entire ecosystems, bioinformatics may be used. The full “parts lists” of a genome help scientists better understand complicated biological processes. Identifying how these components interact in a genome or proteome is the subsequent stage of intricacy in the research process.

2 Translational Bioinformatics

Translational bioinformatics (TBI) is a new area that applies biological research to patient care and medication discovery. Develop and analyse clinical and biological data to study illness heterogeneity using computer methods. The search for disease gene(s) requires a thorough understanding of the complex network of biological mechanisms involved in disease progression. This chapter aims to outline the biological and clinical data integration strategy. It also explains the key datasets and techniques used in translational bioinformatics to treat illnesses.

Translational bioinformatics focuses on utilising current research to connect biological data with clinical informatics. Translational bioinformatics now covers the biological and healthcare industries, bridging the gaps between the bioinformatics and medical informatics. Translational bioinformatics has made several databases available to researchers. These databases are useful for physicians, biologists, clinical researchers, bioinformaticians, and health care researchers. These databases help biologists comprehend illness management and medication development techniques, which help them, generate novel hypotheses. Gene variations, enzymes, and descriptive genomics databases are examples of translational bioinformatics databases.

2.1 Categorising Translational Bioinformatics Research

Translational bioinformatics research can be basically sorted into four categories (Denny, 2014):

  • The utilisation of Clinical “big data” or data from electronic health records (HER) for genomic discovery

  • Regular clinical use of genomics and pharmacogenomics

  • Drug discovery based on OMICS data and development

  • Individual-level genetic testing to address ethical, legal, and societal challenges related to such services.

Translational bioinformatics integrates biostatistics, molecular bioinformatics, clinical informatics, and statistical genetics (Chen et al., 2013b). The field is quickly developing, and many related topics have been suggested. Among them, pharmacogenomics is a branch of genomics concerned with genetic differences in drug response. This branch is vital for future precision medicine design. Translational bioinformatics is a recent subject that has gained significance in the era of personalised and precision medicine. Based on curating large amounts of scientific literature, TBI may identify erroneous research, derive fresh insights into underlying genetic mechanisms of disease, enhance estimations of pathogenicity of human genetic variants, and find possible new treatment targets. However, interpreting the data to establish a clinical diagnosis or treatment plan is far more difficult than sequencing an exome. Many of the thousands of discovered variations will need to be examined clinically. Some Mendelian disorders require just one variation to be found and examined, such as basic Mendelian disorders. Multivariate analysis will be required for more complicated disorders (such as cancer, diabetes, and neurodegenerative diseases). Getting accurate results requires asking the proper questions about patients and diseases, as well as using the right computational tools. “Translational genomics” is the use of new findings from the Human Genome Project to enhance diagnostics, prognostics, and treatments for complex illnesses.

Biology and information technology have merged to create a new area of translational study called translational bioinformatics (TBI) (Ritchie et al., 2020). Aside from basic DNA sequence alterations, epigenomic data now contains information on methylation and histone modifications as well as DNA methylation (data above the genome). Through information technology, it is possible to acquire and analyse the proteome (the total amount of proteins in the cell, tissue or organism), transcriptome (the total amount of mRNA in a cell), and metabolome (complete set of small molecules, called metabolites, in the cell). Bioinformatics aims to characterise and quantify molecular groups that contribute to an organism’s structure, function, or dynamics. This means that every person’s OMICS profile should be linked with their clinical observations, medical images, and physiological signals.

3 Bioinformatics Interventions in Translational Research

The National Institutes of Health (NIH) defines translational research as having two areas of translation. There are two ways to achieve this: one is to adapt findings acquired during laboratory and preclinical research to the creation of clinical trials and human studies. It’s also important to note that the second area of translation involves research targeted at boosting the adoption of best practices in the community. In translational research, the cost-effectiveness of preventive and treatment methods is equally essential. From the scientist to the user, translational research shifts the emphasis. User involvement is increasingly important in the translational research paradigm. They have an impact on the priorities of academics.

3.1 Translational Biomedicine

Translational biomedical research has recently been a hot topic in the biomedical research field. Translational research seeks to “translate” current biological knowledge into methods and instruments for treating human illness. Beyond that, it’s just medical applied research, with its own obscure name. Translational research covers medical genetics, cancer, and cardiology. Currently studied hereditary diseases include microorganisms, plants, animals, and humans. Mendelism established genetics aims to improve our knowledge of basic, preclinical, clinical, epidemiological, and healthcare research understandings are improved.

3.2 Translational Clinical Research

Several recent studies have highlighted a 20-year gap between top clinical research knowledge and its use in our health system. Translational science aims to accelerate the application of scientific discoveries in clinical settings and foster collaboration between researchers and clinicians. Preclinical research, clinical trials, and health technology evaluation, including Alzheimer’s disease and dementia prevention and treatment, may all benefit from the findings.

3.3 Translational Stroke Research (TSR)

TSR includes fundamental, translational, and clinical research. Modern methods for evaluation, prevention, treatment, and repair after stroke and various types of neurotrauma are being developed. Basic and clinical scientists and doctors alike may benefit from translational stroke research. This includes neuroscientists and physicians alike. Preclinical and clinical neuroprotective effectiveness differences have become more concerning in translational stroke research.

3.4 Translational Neuromedicine

To deliver new treatments with quantifiable results to patients with neurological disorders, translational neurology studies all technological advancements. Conceived to help those at risk of or suffering from neurological illness convert the vast amount of fundamental neuroscience, neuropathogenesis, and neuroengineering knowledge into treatments and quantifiable benefits, bringing together fundamental and clinical neuroscientists, Translational Neuroscience aims to improve our understanding of brain anatomy, function, and illness.

3.5 Translational Oncology

Work in both the laboratory and the clinic to improve oncology patient care. Clinical trials evaluating new treatment paradigms for cancer are the outcome of Translational Oncology research. In addition, it includes the most sophisticated clinical tests of both traditional and novel cancer treatments. Research in Translational Cancer Treatment promotes facilities for cancer treatment and notable programmes in relevant areas to combined interdisciplinary and translational cancer control companies. Laboratory discoveries are translated into new cancer treatments for patients via translational cancer research (TCR). Because this study often leads to effective treatments for patients as quickly as possible, it is a boon to society. Researchers develop instruments for clinical trials with the use of clinical observations made by doctors. Conversely, doctors utilise clinical observations to guide their efforts.

3.6 Translational Imaging

Clinical and scientific applications of biomedical imaging. These methods are being used in clinical investigations to uncover chemical imbalances linked with serious mental disorders and drug addictions. To evaluate traits in animal models, imaging can detect them, and vice versa. These studies attempt to “translate” ideas from micro-imaging laboratories to preclinical settings.

3.7 Discovery Biology

In the areas of cancer and neglected diseases, discovery biology performs basic and applied drug discovery research. The field of discovery biology is concerned with basic and practical drug discovery research, especially in cancer and other neglected diseases. Discovery biology services include biosafety and ad hoc virus testing (Yin et al., 2021).

3.8 Medical Biotechnology

Medical biotechnology focuses on developing technologies for the health and pharmaceutical industries. Biotechnology in medicine enables researchers and doctors to find new drugs and prevention of diseases. Most medical biotechnologists work in academia or industry. Biotechnologists have discovered novel medicines and developed and tested diagnostic technologies to treat and prevent disease. Depending on their expertise, medical biotechnologists work in academia or industry. Academic biotechnologists help medical researchers conduct tests, whereas industrial biotechnologists create pharmaceutical drugs and vaccines. Medical biotechnology has created microbial insecticides, insect-resistant crops, and environmental cleaning techniques.

3.9 Orthopaedic Transition

Translational research in orthopaedics is a fast-expanding area. Cellular and molecular research must be used properly in the therapeutic environment to really enhance people’s health. In addition to bringing cutting-edge information to the forefront, this project will enable pioneers of orthopaedic translation to share and mutually improve skills.

3.10 Translational Stem Cell Medicine

To improve the therapeutic use of cellular and molecular biology of stem cell, Stem Cell Translational Medicine (SCTM) was STEM CELLS Translational Medicine will assist improve patient outcomes by bridging stem cell research and speeding translation of new lab findings into clinical trials. The research of stem cells has developed quickly, yet at an astonishing pace. This chapter aimed to provide a comprehensive list of stem cell types.

3.11 Translational Proteomics

Translation to decipher complicated disease processes, proteomics uses multidisciplinary methods. It emphasises fast distribution of new findings. It untangles complicated disease processes utilising multidisciplinary methods. Proteins are essential components of the physiological metabolic processes of cells and are essential components of living organisms. Most human diseases are caused by functional protein interaction dysregulation. In recent years, advancements in science and technology have enabled the study of protein interactions inside cells.

3.12 Translational Neuroscience

Developing novel treatments for neurodegenerative, neuropsychiatric, and developmental disorders is the goal of Translational Neuroscience. Brain anatomy and function research influence the development of novel treatments for neurological disorders. It is the process of bringing new treatments with quantifiable results to neurological illness patients. Conceived to help those at risk of or suffering from neurological illness convert the vast amount of fundamental neuroscience, neuropathogenesis, and neuroengineering knowledge into treatments and quantifiable benefits.

3.13 Molecule Therapy

The term “molecular therapy” refers to molecular alterations in cells. Vaccine development, preclinical target validation, clinical trials, and safety/efficacy studies are all subjects addressed in the study. Molecular targeted treatments utilise drugs to target particular molecules on the surface or within damaging cells. These chemicals help provide signals to cells to divide or grow. The medicines work by slowing the growth and spread of cancer cells while sparing healthy cells. Targeted treatments employ several medicines with varying effects. Scientists are trying customised treatments on both animals and people (clinical trials). But just a few targeted treatments have FDA clearance. The long-term efficacy and safety of targeted therapy are unknown.

4 Advances in Translational Bioinformatics

Translational bioinformatics involves in development of storage, analytical, and interpretative techniques that maximise the conversion of increasingly large biological and genetic data into proactive, preventative, predictive, and participatory health. The development of novel techniques for integrating biological and clinical data, as well as the evolution of clinical informatics methodology to include biological observations, are all part of translational bioinformatics research. Butte and Chen popularised the phrase when they published “Finding disease-related genomic research within an international repository: initial steps in translational bioinformatics” (Butte & Chen, 2006; Wilson et al., 2022). TBI has grown in prominence over the last decade, attracting a large professional community that has published results in high-impact journals and presented findings at national and international conferences.

Informatics was also emphasised during the annual AMIA Summit on Translational Bioinformatics, which was hosted in San Francisco. TBI has been studying how computational tools and techniques may be utilised to understand, analyse, and manage clinical data since its inception, advancing the discipline of systems biology in the process. To assist define this new subject, Altman organised a yearly review session at the AMIA annual conference (Altman, 2012). Altman emphasised the scope of translational research in health care, which encompasses illness treatment, prevention, and monitoring, as well as the discovery and evaluation of biomarkers and their application to areas like rare disorders (Burton & Underwood, 2007) or gene-disease correlations (Caufield et al., 2022; Denny et al., 2010). Translational bioinformatics, he claims, combines translational medicine with bioinformatics. Translational bioinformatics connects the two disciplines by developing algorithms to analyse fundamental molecular and cellular data in order to improve therapeutic outcomes. To enhance patient treatment and our knowledge of biology, TBI research combines data from molecular (DNA, RNA, proteins, small molecules, and lipids) and clinical entities (patients) (Altman, 2012).

5 Prospects of Translational Bioinformatics

Precision medicine relies on translational bioinformatics to support genetic, environmental, and clinical profiles of people, allowing genomic data to be turned into individualised therapy. The individuals working in this field tackle the scientific and statistical difficulties presented by genetic data in an unusual way. Translational bioinformatics includes clinical genomics, genomic medicine, pharmacogenomics, and genetic epidemiology. Precision medicine in translational biotechnology has ramifications in both clinical and therapeutic areas, such as drug discovery. Introducing new medicines would require the multimodal cooperation of clinical personnel, physicians, laboratory staff, biostatisticians, and bioinformaticians. Clinical genomics assists in the discovery of novel molecular biomarkers that are verified by clinically relevant genetic testing (Pagonet al., 2002).

Pharmacogenomics may be concerned with the genomic/clinical phenotypic connections with pharmacologically active drugs (Rubin et al., 2005). A few new techniques are being explored in clinical studies. Drugs for diseases like cancer, AIDS, cardiovascular disease, asthma, and Alzheimer's will be created utilising pharmacogenomics. Currently, pharmacogenomics studies factors that influence a drug's concentration reaching its targets. The use of gene expression from cell lines to predict patient drug response is currently controversial due to cell line variability. The accuracy of predicting in vivo medication response using a patient's baseline gene expression profile varied between 60 and 80%. A mix of inherited and nongenetic factors affects cancer growth and treatment resistance. Comparatively to public health and environmental registries, genetic epidemiology collects genome-based data (Little & Hawken, 2010). It is a process of converting fundamental research into a therapeutic environment. The complexity of the human physiology and the variety of the human population would be a limiting factor for real translation into clinical practice, but they would provide some inputs to future medical advances (Hopkins et al., 2021).

5.1 Translational Genomics in Clinical Care

While genetics examines single functioning genes, genomics analyses our whole DNA, recognising non-coding DNA’s regulatory role and the intricate connections between many genes and the environment. Precision medicine seeks to promote health and treat illness more accurately by combining predictive, preventative, personalised, and interactive components. Recent advances in fundamental research have revealed new genetic variations and biomarkers. Over the coming decade, many anticipate significant advances in genetic testing and genome sequencing.

A genetics medicine service will depend on general practitioners to assist patients with diagnosis, treatment, and illness prevention. More doctors may explore adopting genetic testing and genome sequencing in the future years, with some expecting complete integration into routine medical treatment within 10 years. It may assist in diagnosis, prognosis, and therapy. Inhibitors of BRAF and Herceptin® (trastuzumab) are two examples. PARP drugs are more successful in treating ovarian cancer in individuals with BRCA gene mutations.

In addition to high-risk DNA variants, thorough genotyping may also help identify milk and gluten intolerances, as well as mucoviscidosis. Assembling genetic and HAS data may help uncover low penetrant variants. MFS is caused by mutations in fibrillin 1 (FBN1). MFS patients have significant clinical heterogeneity within and between families due to the disease’s aetiology. TGFBR1, TGFB2, TGFBR2, MYLK1, MYH11, ACTA2, and SMAD3may assist identify individuals at risk for aortic aneurysms. Studying these high-risk individuals’ aorta shapes may assist predict disease progression.

Translational genomics may potentially investigate gene networks of people with various disorders to learn more about their relationships. That is why almost half of Down’s syndrome individuals exhibit an overprotection against heart problems linked with connective tissue. Recent research indicates FBN1 is increased in Down’s Syndrome (usually downregulated in MFS). The creation of genomic networks will help clarify the connections between various diseases. Understanding linked syndrome gene networks may lead to targeted gene therapy for illnesses.

Baby with long QT syndrome at Lucile Packard Children’s Hospital Stanford. In this instance, the baby’s heart stopped many times just after delivery. Gene mutations may cause Long QT syndrome. Finding the mutation’s gene is essential to therapy. WGS revealed a previously characterised mutation, as well as variation of a new copy number in the TTN gene that targeted genotyping alone, would not have detected. It also took hours or days rather than weeks to get the response.

NGS whole-genome sequencing is vital in the study of complex diseases like cancer. The fact that drugs work differently in different patients with same cancer has long been a problem in cancer treatment. Drugs that target the unique signalling patterns of individual patients are currently being discovered using large-scale pharmacogenomics and personal genomics datasets. These include databases for the cancer cell lines. The NIH’s Cancer Genome Atlas Project analysed the genomic profiles of over 10,000 people to discover new cancer subtypes. The variability of drug response is thought to be caused by patients with specific genomic aberrations. Large-scale datasets can be used to predict drug combinations, reposition of drugs, and delineate mechanisms of action. They are becoming increasingly important in drug development. Precision medicine can thus be tailored to individual patients’ genomic profiles.

6 OMICs for Drug Repurposing and Discovery

Over the past 60 years, the cost of creating new medicines has increased significantly, with each new medication costing approximately 80 times more in 2010 than it did in 1960. The long FDA clearance procedure has also been widely addressed. The time it takes for a lead to be discovered and approved by the FDA is estimated to be 12 years. Consequently, a growing number of researchers are looking at high-throughput and computational drug development and repurposing. Recent efforts have concentrated on utilising omics data, particularly genomics, to identify novel therapeutic targets and develop new applications for existing medicines (drug repositioning).

Many new large-scale biological databases, in addition to the Human Genome Project, will aid researchers in better understanding illness origins and progressions. Biomolecular structural data can be found in the RCSB Protein Data Bank, as well as links to other biological resources including gene and pharmacology databases. Using mass spectrometry, ProteomicsDB, for example, identifies organ-specific proteins and translated long intergenic non-coding RNAs in the human proteome derived from tissues, cell lines, and bodily fluids.

The Human Metabolome Database currently contains approximately 40,000 annotated metabolites entries because of these advancements. It uses mass spectrometry and NMR spectroscopy to provide experimental and analytical metabolite concentration data. Databases are believed to aid in the transformation of clinical practice, specifically in metabolic disorders, such as coronary artery disease and diabetes. In fact, metabolomics is a rapidly expanding research area that encompasses both endogenous metabolites as well as chemical and biological substances that interact with the human body. Compounds from meals, medications, TCM, and the gut bacterial flora are being fingerprinted by researchers. These will eventually aid in our understanding of the host–pathogen-environment connection. These databases aid researchers in improve understanding the progression of complex diseases. Pattern mining and clustering can allow for the identification of new biomarkers. Clusters that are partitional (hard) or hierarchical (tree-like nested structure). These methods may be sped up by utilising multicore CPUs, GPUs, and FPGAs in parallel.

One way to describe the process of employing an FDA-approved medicine for a condition other than what it was originally approved for is “drug repurposing”. Off-label use has mostly been motivated by chance in the past. Viagra, for instance, was originally designed to treat heart issues but is now used to treat erectile dysfunction. Early phase clinical trials are avoided by using a pre-approved medication, saving time and money.

Association research may lead to the discovery of new pharmaceutical targets. Sanseau et al. (2012) looked at prior GWAS results and discovered that 15.6% of them are already pharmaceutical targets (in comparison with 5.7% of the general genome). In 103,638 patients and controls, Okada et al. discovered 101 overall RA (rheumatoid arthritis) risk loci, 18 of 27 current RA therapeutic target genes, and three approved cancer medications that might be active against RA. Khatri et al. (2013) identified a comparable module of 11 genes in eight previous organ rejection datasets. Scientists discovered two non-immunosuppressive medications that may be repurposed to regulate these genes in a mouse model. Drug-Gene Interaction Database (DGI) and PharmGKB are two resources that may assist in translating genomic research results into effective medications. The resources for TBI may be found in the Table 1.

Table 1 Resources for translational bioinformatics that are open to the public
Full size table

Finally, an expanding set of computational and experimental techniques based on genetic and clinical data enables medication repositioning. An increasingly wide range of drug repositioning approaches may be used quickly and efficiently by combining translational bioinformatics, statistical methodologies, chemoinformatics, and experimental procedures. There are currently efficient techniques for systematic drug repositioning utilising huge libraries of biologically active molecules. Medicinal chemists and other translational experts can help reposition drugs.

7 Systems Biomedicine

Systems biology is a new multidisciplinary study that combines biology, mathematics, computer science, physics, and engineering. Most biological systems are too complicated for even the most sophisticated computer models to capture all system characteristics. A useful mode should be able to correctly comprehend the system under investigation and give trustworthy prediction results. To do this, a certain degree of abstraction may be needed, focusing on the system behaviours of interest while ignoring other aspects. Systems biology does not study individual genes or proteins one at a time, as has been the case for the last 30 years. Rather, it studies the interactions of all components in a biological system in action. With the goal of building formal algorithmic models for predicting process outcomes from component input, systems biomedicine is an emerging approach to biomedical research. Several important characteristics define the systems approach:

  • Pursuit of quantitative and accurate data

  • The datasets’ comprehensiveness and completeness

  • Willingness to define, quantify, and alter biological complexity

  • Focus on component interconnection and networks

  • Obsession with mathematically predicting outcomes.

Network studies have been done mostly on cell-based systems like immunology and cancer, or on homogenous tissues like the heart and liver. Using new bioinformatics methods, scientists discover microRNA-gene networks that are important in human inflammatory disorders and cancer.

7.1 Personalised Genomics

Personalised medicine treatment is crucial for patients to achieve the best possible outcomes while minimising adverse effects and high direct medical expenditures. Personalised medicine makes use of genetic and genomic testing. Whole-genome sequencing (WGS) examines the expression and interactions of all the genes in the human genome rather than just one. Rather of providing gene signature profiles based on the expression levels of individual component genes, genetic testing looks for single gene mutations or overexpression. Breast cancer genes BRCA-1 and -2, melanoma gene BRAF, and non-small cell lung cancer gene EGFR are examples. Breast, colon, and prostate cancer Oncotype DX tests, as well as the 70-gene test for breast cancer WGS, have grown easier, quicker, and less expensive since its beginnings. It’s so easy that it might become a standard test for healthy people in primary care. However, interpreting WGS results may be difficult.

7.2 Genetic Testing

Genetic testing utilises human DNA, RNA, or proteins to look for gene variations, chromosomal abnormalities, or proteins linked to illnesses or disorders. An individual’s risk of getting or passing on a genetic disease can be assessed using the results of a genetic test. There are already over 1,000 genetic testing available in the United States.

Most medical genetic tests result in changes in medical treatment, based on data from clinical trials and other medical practice.

  • Identify a genetic illness.

  • The risk of having a specific genetic disorder.

  • Forecast the likelihood of adverse effects or an atypical reaction to a medication.

  • Find a common disease’s elevated risk.

Genomic tests must be accurate and clinically relevant to be useful. Analytic, clinical, and utility validity are required for genomic tests. In clinical validation, identifying and quantifying possible causes of biologic variation is a key aim. Patients gain from tests that are clinically useful. Using genetic data to drive patient treatment is changing our health care systems, but there are still obstacles. It examines how people are using personal genetic information for enhanced diagnoses, tumour profiling, and genomic risk assessments. Examples of how genomics is being used in everyday patient care are interwoven with the daily difficulties still confronting genomics integration into clinical practice, as well as methods being developed to overcome these hurdles.

7.3 Genomic Testing for Individuals to Inform Health Care

Several companies began offering direct-to-consumer genetic testing in 2008, providing information on genes for both health and leisure. Individuals can now acquire genetic testing without a doctor’s prescription thanks to the availability of DTC genetic testing from companies like 23andMe (Mountain View, CA). People received test results as well as personalised information about their genetic origin, disease risk, and response to therapy.

DTC genetic testing raises several fascinating ethical, legal, and social considerations. For years, there was debate on whether or not these exams should be regulated. In November 2013, the FDA ordered 23andMe to stop marketing and offering health-related information services. The FDA designated these tests as medical devices, requiring formal testing and FDA approval for each test. The FDA accepted 23andMe’s Bloom syndrome test application in February 2015 (http://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/UCM435003), and the firm stated in October 2015 that it would begin giving health information in the form of 36 gene carrier status. A 23andMe client can download their raw genetic data and assess the results using information from other websites, like Geneticgenie, Promethease, Interpretome, and openSNP.

Genetic testing may aid patients, according to a case presented at the 2014 American Neurological Association meeting. Alzheimer’s disease runs in the family of one of the patients’ mothers. She didn’t know if she was a carrier or not. She didn’t want to pass that mutation on to her children, though. Her doctors were able to choose embryos that did not carry the Alzheimer’s disease gene mutation because to PGD testing. The patient was never tested, and the number of affected embryos (if any) was never revealed.

The ability to simultaneously investigate several genes or the entire genome brings up new possibilities in genomic medicine. Patients are challenging doctors about the applicability of genetic and genomic medicine to their own care, since new technologies promise better diagnoses and treatments. Others believe that incorporating genetics and genomics into routine clinical practice will be difficult.

7.4 Computational Health Informatics (CHI)

Computational health informatics (CHI) is a relatively recent area of study in and out of medicine. Information technology (IT) is an interdisciplinary that incorporates aspects of biological science as well as medicine. CHI studies how computers affect health care. Health informatics is the study of how to forecast a patient’s health by gathering and analysing data from all areas of healthcare. Patient care is at the heart of health informatics research (HCO). The amount of medical and healthcare data available has grown dramatically during the last few years.

The fast development of new technologies has increased the amount of digital health data in recent years. The digital health data is massive and complicated in structure for conventional hardware and software. Some of the reasons why conventional systems fail to handle large datasets include:

  • A wide range of organised and unstructured data including medical records, handwritten doctor notes, MRI, CT, and radiographic films.

  • Healthcare informatics has noisy, heterogeneous, complicated, varied, longitudinal, and big datasets.

  • Big data analytics and visualisation challenges.

  • The requirement to increase data storage capacity, calculation capacity, and processing power.

  • Improving patient care, data security, sharing, and lowering healthcare costs.

Thus, methods are required to handle and analyse such large, varied, and complicated information efficiently. Big data analytics, a common phrase for big and complicated datasets, is critical in handling enormous healthcare data and enhancing patient care. It also has the potential to save healthcare costs, improve treatments, customise medication, and assist clinicians in making individualised choices.

Transcriptome and proteome profiling have established how genetic information is expressed to determine phenotypes. The analysis of the connection between protein and mRNA levels shows the intricacy of gene expression regulation during dynamic transitions, particularly during steady-state and long-term state changes. It is important to note that the connection between protein levels and coding transcripts is significantly influenced by mRNA spatial and temporal fluctuation. In this section, we explain how protein concentrations may buffer mRNA variation (Liu et al., 2016). Proteomics-based mass spectrometry is a large and complicated field including numerous mass spectrometers, spectra, and search results. Quantitation in various scanning modes at various MS levels adds to the complexity. The most difficult task is quantifying post-translational modifications (PTM). Many various quantification methods have been published, some of which may be directly used for PTM quantification (Allmer, 2012). Translation regulates the proteome composition by converting mRNA coding sequences into polypeptide chains. For example, translatomics has revolutionised the study of cancer, bacterial stress response, and biological rhythmicity. The translational design may increase recombinant protein output by thousands of folds.

8 Related Work

Translational bioinformatics, systems biomedicine, clinical informatics, statistical genetics, and genomic medicine are all being enticed to play an increasingly important role in accelerating the translation of genome-scale studies to hypothesis-driven biological modelling, effective treatment, and tailored disease management or prevention. Over the last decade, technological improvements in high-throughput sequencing have resulted in a growing global capacity for easily creating nucleotide sequences. The 1000 Genomes Project was created in order to compile comprehensive genetic variation maps of individuals from distinct groups (1000 Genomes Project Consortium, 2015). For the integration of genetic data with clinical information, data from primary care, hospitals, outcomes, registries, and social care records should first be gathered using controlled clinical terminologies such as SNOMED Clinical Terms and the Human Phenotype Ontology (Köhler et al., 2017). The Global Alliance for Global Health (GA4GH) is developing a shared framework of concepts for adoption with the goal of accelerating human health advancements, increasing efficiency, and lowering costs in order to ensure global interoperability of medical genetic data (Aronson & Rehm, 2015).

Genomic data enabled the Pan-Cancer Analysis of Whole Genomes (PCAWG) study, which is an international collaboration aimed at identifying common patterns of mutation in more than 2,800 cancer whole genomes from the International Cancer Genome Consortium. PCAWG proposes to induce genomic, transcriptomic, and epigenomic changes in 50 distinct tumour types and/or subtypes. This research has established the utility of merging data from several individuals’ genomes, which can result in the identification of novel targets and disease mechanisms, as well as improved diagnostic and treatment outcomes for specific patients (Vamathevan & Birney, 2017).

Large-scale innovative research initiatives, such as the International Human Cell Atlas Initiative10, which aims to create comprehensive reference maps of all human cells, as well as devices, applications, wearables, and implantable technology, will add to this data explosion (Vamathevan & Birney, 2017).

8.1 The Ongoing Research Works Are Looking for:

  • Developing novel approaches for analysing and merging large-scale datasets derived from transcriptomic, proteomic, genomic, and signalling pathways and networks analyses.

  • Using machine learning and other modern computational tools to analyse large-scale biological data sets.

  • Identifying and statistically evaluating molecular biomarkers for the diagnosis, prognosis, and classification of illnesses.

  • Making use of cutting-edge bioinformatics technologies such as Blockchain, Internet of Things, and big data analytics.

  • Demonstrates the value of translational bioinformatics approaches in viroinformatics, drug development, and repurposing.

  • Includes translational healthcare and clinical uses of next-generation sequencing (NGS).

  • Demonstrates translational medicine systems and their potential for healthcare improvement.

  • Conducts research on medical image analysis, with a particular emphasis on CT scans and the detection of novel coronavirus infections.

8.2 Research Gap

Discovering, reusing, sharing, and analysing data are all dependent on metadata and data standards in Translational bioinformatics research. Several recent assessments imply that lack of acceptance of such standards is often related to difficulties in understanding, accessing, and using them. (Vamathevan & Birney, 2017).

The informatics literature currently lacks research on “active management” of data assets during the lifetime of a research project. Translational research produces and administers data “in the wild”, meaning researchers rarely anticipate how the data can be used beyond the original intent. Leaving data management planning to the last minute diminishes the value of research data assets and restricts reuse possibilities.

8.3 Future Research Perspective

Patients with genetic disorders account for a sizable proportion of the world’s population with unique healthcare demands. Recent estimates place the incidence of chromosomal diseases at 3.8 per 1000, single gene disorders at 20 per 1000, and multifactorial disorders at 646.4 per 1000. For example, in India, around 2% of new-borns have single gene or chromosomal problems, and 3% of couples have children with recurrent illnesses. Around 30% of major chronic illnesses and over 1,400 common single gene disorders have genetic variations (Chakrabarty et al., 2016). The laboratory service should be required to deliver cutting-edge genetic and genomic testing and analysis. For the reasons stated above, the spectrum of variations/mutations in faulty gene(s) is not uniform across the population, necessitating the use of modern genomic technology. Precision medicine aims to apply the right dose of the right treatment to the right patient at the right time. This requires integrating cutting-edge genetic technologies into healthcare system. Integrative perspectives on fundamental principles of genomic, proteomic, and computational biology help researchers as they apply qualitative systems methods to medical challenges (Tiberti et al., 2022).

9 Conclusion

The biological and healthcare industries are now covered by translational bioinformatics, which bridges the gap between bioinformatics and medical informatics. Current research is used to connect biological data with clinical informatics in translational bioinformatics. It requires analysing and sequencing an organism’s whole genetic code, from genes to transcripts. The translational bioinformatics databases help biologists to learn about disease management and therapeutic development. A signalling pathway’s operation in a cell can be studied using systems biology. A comprehensive view of a biological entity is created by combining genomic, proteomic, and bioinformatics data. Bioinformatics methods can be used to replicate the appearance of specific human diseases or healthy states. HGP discoveries are used in translational genomics to improve the diagnosis, prognosis, and therapy of complicated disorders. These innovations have revolutionised both healthcare and biomedical research. New tools and methodologies are needed to turn massive databases into usable knowledge.