The bottleneck in genomic research has shifted from generating DNA sequences to processing the massive amounts of data they produce. While sequencing costs have dropped from $95 million per genome in 2001 to as little as $200 today, research institutions now face a different challenge: they have more genomic data than their computing infrastructure can handle. A single human genome generates roughly 100 gigabytes of raw sequencing data, and major research initiatives like UK Biobank have already accumulated more than 27 petabytes of genomic data from analyzing 500,000 individual genomes.

Why Is Computing Power Becoming the Limiting Factor?

Modern genomic research requires far more than just storing sequences. Researchers must perform complex analytical tasks including variant calling (identifying genetic differences), genome assembly, multi-omics analysis (studying multiple biological systems simultaneously), and machine learning pipelines that train artificial intelligence models on genomic data. These workflows demand parallel processing across thousands of computing cores and large-scale storage environments that most research labs have not built intentionally.

The global high-performance computing (HPC) market reflects this growing demand. The HPC market was valued at approximately $57 billion in 2024 and is projected to reach $87.31 billion by 2030. Industry analysts expect HPC specifically for life sciences to grow at double-digit rates through 2031, underscoring how critical computing infrastructure has become for genomics, proteomics, and biomedical research.

How Are Research Organizations Building Computing Infrastructure for Genomics?

• Strategic Planning: Rather than adding computing resources reactively in response to individual projects, research leaders are forecasting compute needs based on sequencing volume and pipeline complexity, then allocating resources across research programs to maintain consistent throughput.
• Elastic Capacity: Organizations are leveraging cloud-based and hybrid computing environments that scale automatically during peak workloads without requiring permanent infrastructure expansion, helping maintain predictable costs.
• Governance and Security: Genomic datasets contain some of the most sensitive health information, requiring role-based access controls, encrypted storage, secure data transfer, and audit-ready transparency for regulatory compliance.
• Reproducibility Controls: Standardized compute environments, containerized workflows, and automated orchestration ensure that genomic analysis pipelines produce consistent results across research teams, preventing infrastructure inconsistencies from distorting scientific findings.

Without intentional infrastructure design, research organizations often face a familiar problem: oscillating between resource shortages that slow analysis and expensive overprovisioning that inflates budgets without improving scientific output.

How Is the Billion Cell Atlas Using Computing Power to Advance Drug Discovery?

One major initiative demonstrates how computing infrastructure and genomic data are converging to accelerate drug development. Illumina announced on July 16, 2026, that its Billion Cell Atlas alliance has expanded to include three new member companies, including AI-native drug developer Formation Bio. The Billion Cell Atlas is the world's largest genome-wide genetic perturbation dataset, capturing how one billion individual cells respond to genetic changes via CRISPR across more than 200 disease-relevant cell lines.

Over 350 million cells have been sequenced to date, generating upwards of six petabytes of genomic data. This unprecedented scale enables researchers to discover and validate novel drug targets, characterize how drugs and diseases work at the cellular level, and explore potential new medical indications.

"We are building the cell atlas to address key bottlenecks across the drug discovery and development continuum," said Rami Mehio, senior vice president and general manager of BioInsight at Illumina. "We are creating the foundational framework to train virtual cell models and solve some of the most fundamental challenges in biology."

Rami Mehio, Senior Vice President and General Manager of BioInsight at Illumina

For Formation Bio, the Atlas provides insight into why a drug might work or fail in specific patient populations. Single-cell perturbation data at scale enables researchers to build more precise models of how candidate drugs interact with disease biology, improving their ability to identify which patient subgroups are most likely to respond and inform clinical trial design.

"The next frontier of AI in biology hinges on the creation of foundational training datasets," explained Kyle Farh, vice president of Artificial Intelligence at Illumina. "Up until now, most single cell data has been observational. We aim to change that, and in the process, reimagine what is possible in biology."

Kyle Farh, Vice President of Artificial Intelligence at Illumina

The alliance, which launched in January 2026 with founding members AstraZeneca, Merck, and Eli Lilly and Company, now includes Formation Bio and two additional AI-driven drug discovery partners. This expansion signals that pharmaceutical and biotech companies recognize computing infrastructure and large-scale genomic datasets as essential competitive advantages in drug development.

As genomic research continues to accelerate, the organizations that treat high-performance computing as a strategic capability rather than a technical afterthought will gain a significant advantage. They will be able to analyze more data faster, maintain lower costs, protect sensitive patient information, and ultimately translate genomic insights into medicines that reach patients more quickly.