A Cloud-Optimized Bioinformatics Pipeline for WGS/WES in Metabolic Disease Research
Introduction
As data generation outpaces traditional analysis workflows, more biotech companies are realizing the need for automated, scalable solutions that transform raw sequencing data into interpretable results quickly and reproducibly. Our clients trust us to build these critical pipelines because we consistently deliver tools that reduce manual effort, increase reliability, and scale across teams and studies. In this case study, you’ll see how we partnered with a clinical-stage biotech company to do just that—dramatically improving their ability to analyze WGS/WES data and accelerate discovery in metabolic disease research.
Situation
Our Client, a clinical-stage biotechnology company advancing next-generation treatments for metabolic disease, needed a robust, scalable approach to identify genomic variants from whole-genome and whole-exome sequencing (WGS/WES) data. The sheer volume and complexity of the sequencing output presented a bottleneck: variant calling workflows were time-consuming, error-prone, and required significant manual intervention. To maintain scientific rigor while enabling faster insight generation, they partnered with Bridge Informatics to build an automated, cloud-optimized bioinformatics pipeline.
Strategy
We got to work on a modular, high-throughput workflow designed to process both WGS and WES datasets efficiently and reproducibly. We implemented a cloud-integrated pipeline leveraging containerization (Docker, Singularity) and workflow managers (Nextflow, Snakemake) to streamline every step—from quality control and alignment to variant calling and annotation.
Key elements of our strategy included:
Automation to Reduce Hands-On Time:
Minimizing manual steps allowed researchers to go from raw sequencing reads to final variant calls with less than 10 minutes of user intervention.
Cloud-Based Scalability:
Cloud infrastructure ensured rapid analysis of large-scale datasets, dynamically allocating resources to maximize efficiency.
Reproducibility and Validation:
Containerization guaranteed consistent environments, while benchmark comparisons (e.g., GIAB concordance) confirmed variant calling accuracy.
This approach not only accelerated discovery timelines but also allowed our Client’s translational scientists to focus on interpretation rather than processing—transforming genomic data into biological insight.
Results
- 80% reduction in manual effort and ~50% faster turnaround time from sequencing to results.
- High concordance with gold-standard reference datasets, ensuring confidence in variant calls.
- User-friendly pipeline design enabled non-bioinformaticians to independently execute complex analyses.
- Scalable to new projects and adaptable across use cases in precision medicine, population genomics, and functional genomics.
By collaborating with Bridge Informatics, our Client transformed an otherwise labor-intensive task into a streamlined, reproducible, and scalable pipeline for variant detection—unlocking genetic insights that power the future of metabolic disease research.
Conclusion
As the demand for genomic insights continues to rise, so does the need for robust, user-friendly pipelines that keep pace. This case study is one of many examples where our clients have turned to us to solve workflow bottlenecks that hinder progress. By building pipelines that reduce hands-on time by 80%, improve reproducibility, and scale seamlessly, we enable scientists to focus on what matters most: interpreting the biology. We’re proud to be a trusted partner for teams tackling some of the biggest questions in precision medicine.
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