The Challenges of Scaling Biomarker Discovery Workflows & How Lifebit’s Trusted Research Environment Solved Them

Introduction

Scaling biomarker discovery workflows is now central to modern precision medicine. As research shifts from small cohorts to population-scale whole-genome sequencing (WGS), the scientific opportunity is clear, but so are the operational limits. Massive data volumes, complex pipelines, and escalating compute costs increasingly constrain discovery.

This blog explores the core challenges of scaling biomarker discovery workflows in practice, and how Lifebit’s Trusted Research Environment (TRE) enabled population-scale genomic analysis to run faster, cheaper, and with governance built in.

Challenge 1: Massive Whole-Genome Data Volumes

Whole-genome sequencing produces hundreds of gigabytes per sample. At population scale, this rapidly grows into petabyte-level datasets that are difficult to store, stage, and access efficiently.

Traditional research environments struggle with slow file access and repeated data transfers, creating bottlenecks long before analysis even begins. As data volumes grow, infrastructure-not scientific ambition-becomes the limiting factor.

Challenge 2: Complex, Multi-Stage Biomarker Discovery Pipelines

Biomarker discovery workflows are inherently complex. They often include:

• Alignment of raw sequencing reads
• Genome-wide variant calling
• Variant Effect Prediction (VEP) annotation
• Downstream aggregation and statistical analysis

Each stage is compute-intensive, and inefficiencies compound as workflows scale. Serial execution and rigid pipeline design lead to long runtimes that slow iteration and limit exploratory analysis.

Challenge 3: High I/O Demands and Long Runtimes

Large-scale genomic pipelines place extreme pressure on storage and I/O systems. Poorly optimised file systems result in idle compute, stalled jobs, and unpredictable performance.

For many teams, analyses that should complete in hours instead take days or weeks, making it difficult to test hypotheses, refine parameters, or respond quickly to new insights.

Challenge 4: Escalating Cloud Compute Costs

While cloud infrastructure offers scalability, it also introduces cost risk. Inefficient resource allocation, over-provisioning, and idle compute quickly inflates budgets.

As cohort sizes increase, compute costs can grow faster than scientific output, forcing teams to limit analyses or reduce scope, rather than scaling discovery.

Challenge 5: Slow Iteration Cycles That Delay Insight

At population scale, slow workflows directly impact scientific progress. When end-to-end runtimes stretch into days or weeks, iteration cycles slow dramatically.

This makes it harder to explore alternative models, validate findings, or translate insights into clinical or translational contexts, reducing the real-world impact of genomic research.

Introducing Lifebit’s Trusted Research Environment: Built for Scale

To overcome these challenges in practice, Lifebit’s Trusted Research Environment (TRE) was used by a research team at the University of Cambridge working on large-scale cancer genomics as part of the CYNAPSE project.

The team faced the same constraints common to population-scale biomarker discovery: long runtimes, inefficient resource utilisation, and workflows that struggled to scale beyond small cohorts. By running their workflows within Lifebit’s secure, cloud-native TRE, they were able to overcome these limitations and execute large-scale biomarker discovery faster and at significantly lower cost, without compromising governance or reproducibility.

In a real-world deployment supporting population-scale cancer genomics research, Lifebit enabled the analysis of:

• 2,445 whole-genome tumour–normal pairs
• ~70% reduction in total runtime
• ~50% reduction in compute costs

The work enabled rapid iteration on population-scale cancer genomics data and culminated in a peer-reviewed publication in The Lancet Oncology, demonstrating the clinical potential of whole-genome biomarkers for breast cancer. 

Key Functionalities

Key Functionalities:

High-performance genomic compute in a Trusted Research Environment
Secure, governed access to population-scale whole-genome data without duplicating or exporting sensitive datasets.

Cloud-native, elastic execution
Compute resources scale dynamically with workload demand, enabling parallel execution of alignment, variant calling, and annotation pipelines.

Optimised I/O and data access patterns
High-performance file systems reduce I/O bottlenecks common in large genomics workflows.

Cost-efficient resource orchestration
Targeted instance selection and parallelisation minimise idle compute and unnecessary over-provisioning.

Reproducible, workflow-aware execution
Built-in support for large bioinformatics pipelines enables rapid iteration and consistent results at scale.

Outcome

By addressing infrastructure, performance, and governance together, Lifebit transformed large-scale biomarker discovery from a fragile, resource-intensive process into a repeatable operational capability.

Research teams can now scale biomarker discovery workflows confidently, running population-scale analyses faster, at lower cost, and with the compliance and auditability required for translational and clinical impact.

To learn more about this, read our whitepaper here.