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Next-generation Sequencing

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....

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Related Experiment Video

Updated: May 23, 2025

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

Benchmarking accelerated next-generation sequencing analysis pipelines.

Pubudu Saneth Samarakoon¹, Ghislain Fournous², Lars T Hansen¹

¹Scientific Computing Services, Division for Research, Dissemination and Education, University of Oslo, Oslo, 0373, Norway.

Bioinformatics Advances

|May 21, 2025

Summary

This summary is machine-generated.

Accelerated next-generation sequencing (NGS) platforms like DRAGEN and Parabricks significantly reduce analysis time compared to CPU-based methods. Performance varies, with Parabricks-H100 showing top speedups, but scalability and resource usage differ across platforms.

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Last Updated: May 23, 2025

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Central processing unit (CPU)-based next-generation sequencing (NGS) analysis tools face limitations due to long runtimes, impacting clinical and research applications.
Accelerated NGS platforms, such as DRAGEN and Parabricks, have been developed to address these runtime issues, reducing analysis from days to hours.

Purpose of the Study:

To comprehensively evaluate the performance, computational resource usage, and speedup scalability of accelerated NGS platforms.
To address the gap in independent assessments of accelerated NGS platforms by investigating their efficiency and scalability.

Main Methods:

Comparative analysis of CPU-only NGS pipelines against accelerated platforms (DRAGEN, Parabricks) using various hardware configurations (L4, A100, H100).
Assessment of mapping and variant calling performance, speedups, and computational resource utilization.
Scalability analysis based on sequencing coverage and profiler analysis for performance insights.

Main Results:

Accelerated pipelines showed shorter runtimes than CPU-only methods, with Parabricks-H100 achieving the highest speedups.
DRAGEN excelled in mapping speed, while Parabricks (A100, H100) demonstrated superior speedups in variant calling.
Mapping scalability analysis indicated positive trends for DRAGEN and Parabricks-H100, while other configurations showed limitations. Profiler analysis revealed optimization potential for Parabricks.

Conclusions:

Accelerated NGS platforms offer significant runtime reductions for genomic analyses.
Platform selection should consider specific needs regarding coverage, time constraints, and budget, informed by performance and cost comparisons.
Further optimization of accelerated platforms like Parabricks is possible, potentially leading to enhanced efficiency and scalability.