Advances in genomics for drug discovery 

DDW Editor Reece Armstrong speaks to Michelle Carlson about overall advances in genomics and the integration of emerging technologies to create more drug discovery and development opportunities. 

RA: How is long-read sequencing enabling greater clinical research in genomics?
 

MC: Short-read sequencing is effective for identifying single-nucleotide mutations and other small variants that can cause genetic disease. However, many conditions stem from larger genomic changes, such as structural variants and repeat expansions, which short-read methods often miss. Long-read sequencing can capture these complex alterations, enabling more patients to receive an accurate diagnosis, increasing the likelihood that they obtain personalised treatment. 

RA: What about the other omics fields? How has the use of multiomics increased in recent years?

MC: Other major omics fields seeing rapid growth include transcriptomics, proteomics, and epigenomics, across both bulk and single‑cell platforms. These approaches are increasingly central to diagnostics and early disease detection, drug discovery, and personalised medicine, where multi‑layer molecular data helps identify biomarkers, refine therapeutic targets, and tailor treatments to individual patients. 

RA: Where do you anticipate genomics having the greatest clinical impact in the near future?

MC: I believe the greatest clinical impact will come from personalised medicine. We’ll increasingly screen individuals for hereditary genomic variants that raise the risk for conditions such as cancer, cardiovascular disease, and diabetes, then use that information to guide earlier and more frequent surveillance for those at higher risk. Personalised medicine will also expand through tumour genetic profiling to tailor cancer therapies, as well as through pharmacogenomic testing that helps match patients with the most effective and safest medications. 

RA: How is the use of AI impacting genomics research? 

MC: AI excels at detecting patterns in large, complex genomic datasets far beyond what humans can process, and it does so much faster. It can uncover subtle genomic signatures associated with disease, which strengthens disease prediction, improves biomarker discovery, and supports more accurate identification of clinically meaningful variants. 

RA: What are the biggest challenges genomics faces for drug discovery?

MC: Some challenges still slow the translation of genomic discoveries into new drug therapies. Even though researchers are identifying many disease‑associated variants, it remains difficult to determine which ones truly drive the underlying biology. Many disorders are also highly polygenic, shaped by networks of interacting genes rather than a single causal variant. In addition, confirming that a candidate gene or pathway is a viable drug target is still a lengthy and resource‑intensive process, which limits how quickly genomic insights can be turned into effective treatments. 

RA: How can genomics researchers integrate spatial information to provide better insights into cell states and biology overall?

MC: Spatial data reveals patterns of disease that bulk sequencing simply cannot capture. Changes in where specific cell types reside can signal emerging pathology, such as the early loss of defined neural networks in Alzheimer’s and Parkinson’s disease. In cancer, spatial mapping can show immune cells clustered around a tumour but unable to infiltrate it, highlighting barriers to effective immune response. It also exposes cell‑cell interactions and microenvironmental relationships that play a central role in disease biology. 

RA: Why should outsourcing for genomics be an option when costs have come down so much in the last 10-20 years?

MC: The cost of sequencing itself may no longer dominate project budgets, but the operational and analytical demands around it remain substantial. Modern studies often require advanced bioinformatics and multi‑omics integration to make sense of the data, and large sample volumes can necessitate either significant staffing or investment in high‑end automation. Regulatory compliance, quality systems, and ongoing training add further expense. Storing and managing both physical samples and the massive datasets generated by genomic analysis also carries significant long‑term cost. Outsourcing these functions allows organisations to avoid spreading resources across highly specialised operational demands and stay focused on the scientific or commercial work that differentiates them. 

RA: How can researchers integrate multiple technologies to capture better data into disease-associated variants?

MC: Integrating multiple technologies gives researchers a far more complete picture of the biology underlying disease‑associated variants. No single method captures every layer of information, and each technology highlights a different aspect of how variants exert their effects. Gene‑wide association studies can link variants to particular diseases, but they do not establish causality or demonstrate which variants are clinically meaningful. Genome‑editing approaches allow those variants to be introduced in a controlled setting, making it possible to evaluate their functional impact directly. Combining sequencing modalities such as RNA‑seq and methylation profiling helps clarify which variants actually drive changes in gene expression or regulatory state. AI‑based analysis can then detect subtle, multi‑dimensional patterns across these datasets, offering predictions about how combinations of variants may influence disease biology. 

RA: For teams thinking about using genomics technologies, what are the critical factors to consider?

MC: Some key considerations include selecting the genomic technology that best fits the biological question, determining which samples to collect and how to stabilise and process them for optimal performance, identifying the labour and instrumentation required to run the experiments, understanding the regulatory obligations involved, and defining the skills needed to analyse and interpret the resulting data. 

 From DDW Volume 27 – Issue 2, Spring 2026 – Read the digital issue here

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