An introduction to Omics

Different branches of science, whose names end with “-omics”, provide the possibility of the comprehensive and holistic study of various layers of cellular information. Branches like genomics, transcriptomics, proteomics, and metabolomics allow studying an organism’s complete set of DNA, RNAs, proteins, and metabolites within a cell, respectively. By obtaining data related to each layer, we can illustrate a much clearer image of the state of the cell. We need high throughput technologies with a well-designed protocol to obtain high-quality data related to each omics. For example, next-generation sequencing made genomics studies possible. Microarray and RNA sequencing have provided a wealth of information about the transcriptome of different cells and diseases for years.

Following is a timeline of important events and innovations that have shaped the evolution of omics fields:

Genomics

• 1977 – Frederick Sanger develops the chain-termination method of DNA sequencing (Sanger sequencing).
• 1986 – The term “genomics” is coined by Tom Roderick.
• 1990 – The Human Genome Project (HGP) is launched.
• 1995 – First complete genome of a free-living organism, Haemophilus influenzae, is sequenced.
• 2001 – Draft sequence of the human genome is published.
• 2003 – Human Genome Project is completed, providing a complete human genome sequence.
• 2005 – Introduction of next-generation sequencing (NGS) technologies revolutionizes genomic research.

Proteomics

• 1975 – Introduction of two-dimensional gel electrophoresis by O’Farrell and Klose.
• 1985 – Development of mass spectrometry (MS) techniques for protein analysis.
• 1997 – The term “proteomics” is coined by Marc Wilkins.
• 2000s – Advancements in liquid chromatography-mass spectrometry (LC-MS/MS) enhance proteome analysis.

Transcriptomics

• 1995 – Microarray technology is developed, enabling large-scale gene expression profiling.
• 2005 – Introduction of RNA-Seq, a high-throughput sequencing method for transcriptome analysis.

Metabolomics

• 1973 – Paul Lauterbur develops the principles of nuclear magnetic resonance (NMR) imaging.
• 1998 – The term “metabolomics” is introduced by Stephen Oliver.
• 2000s – Advancements in mass spectrometry and NMR spectroscopy drive metabolomics research.

Epigenomics and Other Omics

• 2003 – The term “epigenomics” became widely used following the completion of the Human Genome Project.
• 2003 – The Human Epigenome Project is launched to map DNA methylation patterns.

The impact of omics on systems biology

Omics fields have profoundly impacted systems biology by enabling a comprehensive understanding of biological systems at multiple levels. These fields of study, encompassing genomics, transcriptomics, proteomics, metabolomics, and other omics fields, provide large-scale data sets that capture the complexity of biological processes. Utilizing data from various omics layers, researchers can build more accurate models of cellular and organismal functions, identify disease biomarkers, and uncover the underlying mechanisms of health and disease. This holistic approach facilitates studying interactions and regulatory networks within cells, leading to novel insights and more effective disease prevention, diagnosis, and treatment strategies.

Importance of Public databases in Omics research

Public databases play a crucial role in omics research. They have provided many open-source biological datasets that researchers around the globe can use to enhance their studies. These databases contain vast amounts of genomic, transcriptomic, proteomic, and metabolomic data essential for understanding the complex interactions within biological systems. Public databases accelerate scientific discovery by promoting data sharing and collaboration among researchers. For instance, databases such as Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and cBioPortal allow researchers to access vast amounts of data on different diseases, especially cancer. Many studies are conducted based on datasets stored in these databases. We should consider that high-throughput technologies might be too expensive for small research teams, or finding biological samples in some conditions might be challenging due to their low availability; therefore, these datasets are valuable sources for conducting new studies. Since different approaches can be used to analyze these available datasets, various conclusions can be made using the same datasets. Consequently, public databases enhance research efficiency and contribute to science’s advancement by ensuring that these data are widely available.

What is the Direction of Omics?

From its advent, omics sciences have impacted several research areas in biology. Our understanding of disease and biological phenomena has dramatically changed due to the data provided in different omics fields. Omics is evolving continuously and trying to make all aspects of biology more transparent. For example:

Omics in Cancer Research

Due to its inherent complexity, cancer offers a substantial challenge in biomedical research. It is characterized fundamentally by uncontrolled cell growth and a build-up of various genetic, epigenetic, and molecular alterations. These alterations manifest in intricate and variable patterns, leading to the diverse clinical symptoms observed in patients depending on the type of cancer involved. Understanding the fundamental mechanisms of cancer requires a detailed and sophisticated approach because of the intricate nature of cancer biology.

By providing information about various cancers, omics technologies helped us study them much better than classic approaches. By employing genomics, we can identify driver mutations, copy number variations, and epigenetic modifications that cause specific cancers. Proteomics has helped us understand critical signalling pathways, post-translational modifications, and therapeutic resistance. Transcriptomics made us understand cellular heterogeneity within tumors, uncovering subpopulations with distinct functional states and metastatic potentials. Metabolomics, through sophisticated analytical platforms like NMR and LC-MS, has elucidated the reprogramming of metabolic networks in cancer, identifying key metabolites and enzymatic targets that support tumor growth and survival. Epigenomics, with high-throughput DNA methylation and histone modification patterns sequencing, has provided insights into the regulatory alterations causing cancer and their implications for chromatin dynamics and gene expression.

Omics in Neurobiology

Recent advancements in omics technologies have significantly impacted neurobiology by providing a comprehensive understanding of the molecular underpinnings of the nervous system. These high-throughput approaches have enabled researchers to identify genetic mutations linked to neurological disorders, uncover the complex interactions of proteins and metabolites in neural pathways, and map gene expression patterns with unprecedented detail. Omics data has revealed novel biomarkers for early diagnosis and progression monitoring of neurodegenerative diseases such as Alzheimer’s and Parkinson’s. Additionally, omics studies have illuminated the molecular responses to neural injuries and the mechanisms of neuroplasticity, facilitating the development of targeted therapies and personalized medicine approaches. Overall, the application of omics in neurobiology is revolutionizing our knowledge of brain function and disease, paving the way for breakthroughs in diagnosis, treatment, and prevention.

Omics in other disease

Similar to cancer and neurobiology, omics have touched many diseases and biological phenomena. Using omics data, several diseases have been understood much better. Different markers, signalling pathways, important components, and functions of biological molecules related to various diseases have been revealed.

Multi-Omics Data Integration

Of course, omics data have made biology much more transparent, but today, we need more than single omics studies to conclude our research. Different molecules related to various layers of data in cells (including DNA, RNAs, proteins, and metabolites) do not work separately; they interact in complex biological systems. Therefore, multi-omics data integration has recently gained attention.

Multi-omics data integration combines diverse biological data types to understand complex biological systems and disease mechanisms more efficiently. Traditional single-omics approaches, such as genomics, proteomics, or transcriptomics, offer valuable insights but not enough to understand the nature of biological processes. By integrating data from multiple omics layers, researchers can better uncover interactions and regulatory mechanisms than single-omics approaches. Several strategies have been used to integrate omics data, including:

• Matrix factorisation techniques
• Bayesian methods
• Network-based approaches
• Multiple-step approaches (it involve analyzing each omics dataset individually before combining the results. This method might focus on overlapping features like genes in molecular signatures or correlational patterns across datasets)

Choosing the best integration method in multi-omics approaches is crucial. Thus, many customized strategies have been developed recently, especially by employing machine learning and deep learning.

Improving High-throughput Technologies

Omics-based studies require high-quality data. Various omics fields exist owing to powerful laboratory technologies that will generate relevant data. Therefore, in addition to evolving different strategies for omics analysis and integration of omics data, high-throughput technologies must evolve in parallel. Technologies like single-cell and spatial omics have emerged in recent years, and most studies from 2020 to now are also focused on these types of omics. Considering the tremendous impact of single-cell and spatial omics on biology, improving high-throughput techniques can yield more in omics studies.