Tipping the genomic scales: Preparing for the integrated multiomics era

This paid-for advertorial by Sampled appeared in DDW Volume 27 – Issue 2, Spring 2026

How sequencing innovation and integrated workflows are enabling reproducible multiomic studies.

Genomics has always played a central role in advancing research and drug discovery, but utilising genomic data to inform decisions in the clinic has not always been possible. Progress in this area has been limited by the high cost of sequencing workflows and a lack of flexible infrastructure capable of delivering clinically actionable insights at scale. Recent developments have begun to lower these barriers: sequencing costs have fallen, technologies have become more powerful, and infrastructure has improved to support faster turnaround times. As a result, opportunities for applying genomics are rapidly expanding across precision medicine, clinical diagnostics, and large-scale studies. For many programmes, the question is shifting from “Can we sequence?” to “How can we generate clinically actionable genomic insights at scale?” 

Advancing Research and Clinical Genomics

Short-read sequencing has long been a cornerstone of genomics, forming a crucial part of both research and clinical diagnostic toolkits. It remains a cost-efficient workhorse for large studies,  but its limitations leave room for complementary technologies to expand sequencing capabilities and enable deeper biological insights. Genetic variation in non-coding regions is increasingly recognised as an important contributor to disease, while high-fidelity sequencing of protein-coding regions with established clinical relevance remains essential for diagnostic and translational applications.  

Blended whole-genome and whole-exome sequencing address both needs by providing comprehensive genome-wide data alongside deeper coverage of protein-coding areas. This “breadth plus depth” strategy can reduce the tradeoff between discovery and clinical relevance without requiring separate workflows. Another major advancement improving sequencing insight is long-read sequencing, which enables accurate detection of larger structural variants, repetitive regions, and complex genomic rearrangements that are often difficult to resolve with shorter reads.

Preparing for the Near Future – Multiomics & AI

The increasing throughput of sequencing workflows, along with the growing integration of genomics into research and clinical practice, is placing greater demands on our ability to process data and extract actionable insights. However, another recent development is helping to address this analytical bottleneck. AI and machine learning tools are emerging that can rapidly process the large datasets generated by genomics, particularly whole-genome sequencing. As these capabilities advance and prove their ability to integrate and analyse increasingly large datasets, researchers are turning their attention toward generating more comprehensive multiomic datasets from individual samples to gain deeper biological insights. 

A recent study published in Cancer Research paired integrated multiomics with machine learning to identify subtype-specific drug vulnerabilities and candidate biomarkers linked to responses to targeted and combination therapies in T-cell leukaemias and lymphomas. The broader implication is that studies like this increasingly depend on both sides of the equation: multiomics to generate a complete molecular picture from each sample, and AI to integrate high-dimensional datasets into interpretable biomarkers and drug-response hypotheses.

Operationalising Integrated Multiomic Studies  

New sequencing technologies, multiomics, and AI are expanding opportunities for discovery in research and accelerating translation into clinical decision-making. However, realising the full potential of these capabilities requires an end-to-end operational foundation that supports  standardised sample collection, timely processing, proper analysis, and long-term storage in secure, compliant facilities. When these steps are distributed across multiple vendors, each handoff can introduce variability, delays, or sample loss, making it harder to integrate datasets confidently across sites and time points.  

While vendors exist to support these needs individually, a centralised provider offering custom collection kits, sample processing, a full suite of multiomics tools, and sample storage offers significant advantages, including reduced costs associated with transferring samples between multiple third parties and greater consistency across large cohorts and different collection time points.  

Sampled brings multiple -omic technologies together within a single operational environment, including Illumina-based short-read sequencing, PacBio long-read sequencing, Olink proteomics, and all three 10x Genomics platforms (Chromium, Visium, and Xenium). These capabilities sit alongside a large-scale biorepository designed to accommodate ambient, refrigerated, and multiple frozen-temperature storage conditions, enabling coordinated sample stewardship from receipt through data generation. As integrated, multiomic study designs become more common, adopting an end-to-end model early can help research teams standardise their workflows, reduce coordination and handoffs, and generate datasets that are better suited for downstream AI-driven interpretation.  

Visit sampled.com/ddw to learn more. 

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

The post Tipping the genomic scales: Preparing for the integrated multiomics era appeared first on Drug Discovery World (DDW).