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The Rise (And Fall?) of Genomics: The Omics Revolution

As technology advances, we’re used to hearing about the latest in a seemingly never-ending list of ‘omics’. With each one hailed as more promising than the last, opportunities in the life sciences field have never been more abundant than they are now.

Where does this leave genomics? Known to many as the original omic, genomics laid the groundwork for most other techniques. However, with many researchers experimenting with more novel technology these days, are simple genomics experiments becoming less exciting?

In this feature, we explore the evolution of omics and assess whether genomics research is a dwindling trade, or if we’re only just beginning.

The genomics era

We’ll start with a brief intro to genomics. As the name suggests, genomics focuses on the study of the entire genome, encompassing everything from understanding gene interactions, to evolution and disease.

Genomics has become such a cornerstone of the life sciences world that it can be easy to forget that this field is also in its relative infancy. Simple genetics experiments, exploring heterogeneity at a basic level, emerged in the wake of the discovery of the DNA double helix. As technology advanced, attention shifted to larger parts of, and even the entire, genome. The first sequencing method, Sanger sequencing, was invented in 1977, and the term ‘genomics’ was first coined in the Jackson Laboratory in 1986.

But it would be another two decades before genomics would begin to truly replace genetics in the research lab. The completion of the Human Genome Project, alongside reference genomes for several other organisms, catapulted genomics into the spotlight, and decreased sequencing costs led to the practice eventually becoming a staple in research.

Where has genomics taken us?

Understanding the entire genome has revolutionised biology and healthcare. Genomics has allowed us to decipher complex traits and the molecular mechanisms underpinning them, and so it’s hard to name any area of biology that has not benefited from genomic technology. Genomics is the cornerstone of precision medicine, pathogen surveillance and even agricultural improvements. In addition, the rise of genome editing using tech like CRISPR-Cas9 has furthered our understanding of different genetic components, opening up a route for new therapies. In fact, genomic research has spurred significant economic growth thanks to its wide-reaching potential.

Genomics forms the fundamentals of most research in the life sciences – cancer research, for example, is a genomics subject at its core. In healthcare, several diseases are detected using genetic testing, not least in infants. There are still calls for large-scale sequencing efforts to further enhance health records, and to the general public, direct-to-consumer testing kits are still all the rage. But to a scientist, is genomics becoming boring?

Did you know?

The Human Genome Project wasn’t truly completed until 2022! The original project saw around 92% of the genome mapped, but technology at the time fell short when tackling complex regions. The Telomere-to-Telomere consortium presented a complete, gapless assembly for all but the Y chromosome in 2022. There’s certainly still a lot of information to be obtained through genomics research!

The limitations

Despite its transformative impact on the life sciences world, genomics is not without its shortcomings. Crucially, genomics can only provide a snapshot of what is going on in a cell at any given time.

The truth is that biological systems are complicated, and the raw DNA sequence obtained from a mass of cells is not necessarily reflective of the mechanisms underpinning the encoded traits. For example, complex regulation mechanisms, epigenetics and other contextual information all play a role in the journey from DNA to RNA to protein, and beyond.

In addition, differential gene expression, alternative splicing, protein interactions and even environmental factors can impact the state of the cell and organism. Genomics alone cannot elucidate all of this complexity, leading to the rise of other ‘omics’, each providing additional layers of information that help to build a more comprehensive understanding of biological systems.

The other omics

The main ‘omics’ we see in the life sciences field are transcriptomics, proteomics and metabolomics. Extensive other omics exist, such as epigenomics, lipidomics, phenomics and glycomics, but let’s focus on the first three.

Transcriptomics is the study of RNA transcripts, particularly focusing on patterns of gene expression. Transcriptomics involves measuring the levels of RNA to understand which genes are active under specific conditions. Transcriptomics can help elucidate gene regulation, cellular responses to different stimuli and disease mechanisms by comparing gene expression profiles in healthy and diseased states.

Proteomics is the large-scale study of proteins, including their structures, functions and interactions. Proteomics can be used to identify biomarkers for disease, understand cellular processes and signalling pathways, and help in the discovery of new drug targets.

Metabolomics is often referred to as the most complete representation of phenotype at any given moment. Metabolomics is a comprehensive analysis of all the small molecules involved in metabolism within a cell, tissue or organism. The practice can be used to understand metabolic pathways, diagnose diseases, monitor responses to treatment, and study the effects of drugs, diet and environmental changes on health.

Figure 1: Image detailing the various different ‘omics’ used in life sciences research at present. Taken from Roychowudry et al., 2023.

Each of these omics provide a distinct layer of biological information, contributing to a more comprehensive understanding of biological systems and their regulation. Furthermore, the integration of these fields provides an unprecedented understanding of molecular mechanisms.

Vast technological advancements have brought these techniques to the forefront of the life sciences field. For a more comprehensive overview, check out our Multi-Omics Playbook.

The potential of the transcriptome

You may have noticed that here at Front Line Genomics, we focus on much more than our namesake. In fact, it’s hard to find a week where our news summaries don’t feature cutting-edge updates that rely on other omics. Ultimately, this is because a wider range of data types leads to better and more complete outcomes. Genomics may be used to point us in the right direction, but details hidden within the deeper layers of the cell often hold the key to our understanding.

Take transcriptomics, for example. One prominent area where transcriptomics has complemented genomics is in cancer research. Genomics may have been used to identify key drivers of disease such as BRCA mutations, but transcriptomics has provided crucial advantages by offering a detailed and functional perspective on gene activity. The ability to classify tumours, which can often be heterogenous, based on gene expression patterns has led to more accurate prognoses and personalised treatment plans. By classifying tumours into distinct subtypes based on their transcriptomic profiles, clinicians can tailor treatment strategies more effectively, improving patient outcomes.

A notable example of this is the PAM50 assay. The PAM50 gene expression assay is a transcriptomic test used to classify cancers into intrinsic subtypes based on the expression levels of a number of genes. This transcriptomic data can then be used to inform treatment decisions, in a way that genomic data alone could not.

The drug discovery world

Another area where a different omic is taking over is drug discovery. Whilst it is well-cited that drugs are more likely to make it through the approval process when backed by genomics data, proteomics data is further transforming the field. The large-scale study of proteins has significantly impacted drug discovery by providing insights into functions, interactions and modifications.

A model example of this is the discovery of venetoclax. Venetoclax is a drug discovered using proteomic data, for the treatment of chronic lymphocytic leukaemia and other blood cancers. It specifically targets the B-cell lymphoma 2 (BCL-2) protein, which is a key regulator of apoptosis. Overexpression of BCL-2 in blood cancers was discovered using proteomics, as was its role in apoptosis. Not only was proteomics used in the identification of the target, further studies assessed the protein interactions between BCL-2 and candidate drugs, with venetoclax eventually gaining clinical validation. Genomics alone would not have allowed for such a comprehensive screening.

Is genomics a dying trade?

So, given the limitations of genomics and the comprehensive analyses afforded by newer omics, has genomics become boring to scientists?

As much as it may seem like every new paper exploits a different ome, genomics is still a key player in our understanding of healthcare, evolution and every facet of life itself. In fact, a quick look at Google trends shows that genomics is still more widely searched for than transcriptomics, proteomics and metabolomics.

Figure 2: Graph showing Google trends for genomics, transcriptomics, proteomics and metabolomics search results. Data covers June 2023-June 2024.

Different omics may be front and centre in high impact journals, but to the general population, genomics is still the star in everyday news. This is in large part due to the time it takes to get therapies from bench to bedside, so treatments using genetic and genomic technology can still have transformative impacts on patient lives today – even if the work to develop the therapy began years ago.

Take, for example, the recent CRISPR-based therapy, Casgevy. Approved in the UK and US in late 2023, this treatment for sickle cell disease marked the first time the technology had been used in a clinical setting. So, whilst the average researcher may be excited about advancements in other fields, genomics is still stealing the spotlight when it comes to major breakthroughs in healthcare.

Where next?

It’s hard to deny the impact of these other omics – spatially resolved transcriptomics did, after  all, win Method of the Year in 2020. And in this feature, we haven’t even touched upon the potential of single-cell and spatial transcriptomics specifically – as of 2020, over 1,000 papers had been published on single-cell transcriptomics, and that number has only grown in the last four years. We’re well into the midst of the single-cell transcriptomics revolution, and it’s only a matter of time until we see the translation of this research in the clinic.

Proteomics is also taking off – as of 2023, over 93% of the predicted proteins encoded by the human genome had been credibly detected as part of the Human Proteome Project. But you may be surprised to know that proteomics has, in some form, been seen to have diagnostic potential for quite some time. This excellent write-up from the Broad Institute details early ‘proteomics’ work and the increase in research output in the field. And what about metabolomics? This article shows the growing importance of metabolomics in nutritional research, due in part to a better understanding of the metabolome and more advanced tools.

With ever-decreasing sequencing costs, better editing techniques and the rise of fields such as metagenomics, genomics might still have the edge in an experimental and clinical setting for now. But ultimately, it looks like a combination of omics will be the way forward in the transformation of healthcare. Integration strategies still require improvement, but this has been a hot topic in recent years, and scientists have been working hard behind the scenes to develop the necessary tools and infrastructure. If you want a comprehensive overview of the multi-omics landscape, check out our recent Playbook.