Rapid NGS Analysis on Google Cloud Platform: Performance Benchmark and User Tutorial

TL;DR

This paper benchmarks two NGS analysis tools on Google Cloud and provides a tutorial for healthcare professionals to implement them efficiently.

Contribution

A performance benchmark and user tutorial for cloud-based NGS analysis using Sentieon and Parabricks on GCP.

Findings

Sentieon and Parabricks perform comparably in runtime and resource utilization on GCP.

Both pipelines are viable for rapid cloud-based NGS analysis without requiring extensive local infrastructure.

The study provides cost guidance for healthcare managers considering cloud-based genomic processing.

Abstract

Next‐Generation Sequencing (NGS) is being increasingly adopted in clinical settings as a tool to increase diagnostic yield in genetically determined pathologies. However, for patients in critical conditions the time to results of data analysis is crucial for a rapid diagnosis and response. Sentieon DNASeq and Clara Parabricks Germline are two widely used pipelines for ultra‐rapid NGS analysis, but their high computational demands often exceed the resources available in many healthcare facilities. Cloud platforms, like Google Cloud Platform (GCP), offer scalable solutions to address these limitations. Yet, setting up these pipelines in a cloud environment can be complex. This work provides a benchmark of the two solutions, and offers a comprehensive tutorial aimed at easing their implementation on GCP by healthcare bioinformaticians. Additionally, it presents valuable cost guidance to healthcare managers who consider implementing cloud‐based NGS processing. Using five publicly available exome (WES) and five genome (WGS) samples, we benchmarked both pipelines on GCP in terms of runtime, cost, and resource utilization. Our results show that Sentieon and Parabricks perform comparably. Both pipelines are viable options for rapid, cloud‐based NGS analysis, enabling healthcare providers to access advanced genomic tools without the need for extensive local infrastructure.

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