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Related Experiment Video

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Author Spotlight: A Cost-Effective Genomic Workflow for Advancing Rabies Control in Resource-Limited Settings

Author Spotlight: A Cost-Effective Genomic Workflow for Advancing Rabies Control in Resource-Limited Settings

Published on: August 18, 2023

Accelerating genomic workflows using NVIDIA Parabricks.

Kyle A O'Connell¹, Zelaikha B Yosufzai¹, Ross A Campbell¹

¹Health Data and AI, Deloitte Consulting LLP, VA, 22009, Arlington, USA.

BMC Bioinformatics

|May 31, 2023

Summary

This summary is machine-generated.

Graphics Processing Units (GPUs) significantly accelerate genomic data analysis, reducing runtimes for germline variant calling by up to 65x. While GPUs offer cost savings for germline analysis, somatic caller performance varies, requiring platform-specific benchmarking.

Keywords:

Amazon Web Services Cloud computing GPU acceleration Google Cloud Platform NVIDIA Parabricks

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Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Genomic data analysis presents a significant computational challenge.
Graphics Processing Units (GPUs) offer substantial acceleration for genomic workflows.
NVIDIA Parabricks is a GPU-accelerated software suite for genomic analysis.

Purpose of the Study:

To benchmark the performance of NVIDIA Parabricks on different computing platforms (AWS, GCP, NVIDIA DGX).
To evaluate the acceleration of six variant calling pipelines (two germline, four somatic) using GPUs.

Main Methods:

Benchmarking NVIDIA Parabricks software suite.
Utilizing Amazon Web Services (AWS), Google Cloud Platform (GCP), and an NVIDIA DGX cluster.
Assessing two germline variant callers (HaplotypeCaller, DeepVariant) and four somatic callers (Mutect2, Muse, LoFreq, SomaticSniper).

Main Results:

Achieved up to 65x acceleration for germline variant callers, reducing HaplotypeCaller runtime from 36 hours to under 35 minutes.
Somatic caller performance varied across GPUs and platforms.
GPU-accelerated germline callers on cloud platforms resulted in cost savings compared to CPU runs.

Conclusions:

Germline variant callers demonstrated strong scalability with GPUs across platforms.
Somatic callers showed variable performance, indicating a need for platform-specific optimization and benchmarking.
GPU acceleration can significantly speed up genomic workflows, advancing areas like biosurveillance and personalized medicine.