Scalable and cost-effective NGS genotyping in the cloud

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• High costs and computational complexity of next-generation sequencing (NGS) hinder its routine clinical application.
• Realizing the clinical potential of NGS requires rapid, accurate, and cost-effective whole genome sequencing (WGS) data analysis.

Purpose of the Study:

• To develop a computationally efficient and scalable WGS analysis workflow for clinical settings.
• To address the barriers of cost and complexity in WGS data interpretation.

Main Methods:

• Utilized COSMOS, a cloud-enabled workflow management system, to build the GenomeKey WGS analysis workflow.
• Implemented GenomeKey on Amazon Web Services (AWS) for high-performance computing.
• Performed systematic benchmarking on public and clinical NGS datasets.

Main Results:

• GenomeKey, via COSMOS on AWS, demonstrated fast, scalable, and cost-effective analysis of WGS data.
• The workflow efficiently handles large-scale, heterogeneous clinical NGS datasets.
• Benchmarking provided insights into optimizing WGS analysis for clinical turnaround times.

Conclusions:

• GenomeKey represents a significant step towards making WGS analysis routine in clinical practice.
• Optimized workflow management, including strategic batching and resource configuration, is crucial for efficient clinical WGS.
• The developed system addresses the need for medically actionable reports within hours at reduced costs.