GeNePi: a graphics processing unit enhanced next-generation bioinformatics pipeline for whole-genome sequencing...

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Next-generation sequencing (NGS) has advanced genome biology, enabling rapid whole-genome sequencing (WGS).
• High-throughput NGS generates complex data, necessitating efficient computational analysis pipelines.
• Existing tools may lack the scalability or comprehensive variant detection required for large-scale WGS studies.

Purpose of the Study:

• To develop and present GeNePi, a modular bioinformatic pipeline for efficient and accurate WGS short paired-end read analysis.
• To integrate GPU-accelerated algorithms for high-performance genomic variant discovery.
• To provide a comprehensive framework for detecting diverse genomic variants in WGS data.

Main Methods:

• GeNePi is built on the Nextflow framework, utilizing NVIDIA Clara Parabricks for GPU-accelerated algorithms.
• The pipeline automates the detection of single-nucleotide variants, small insertions/deletions, copy number variants (CNVs), and structural variants.
• It incorporates tools like HaplotypeCaller, CNVkit, Manta, Lumpy, BreakDancer, CNVnator, and MELT for comprehensive variant characterization.

Main Results:

• Benchmarking on synthetic and real datasets demonstrated high accuracy and performance.
• GeNePi's performance is comparable to state-of-the-art tools such as the Genome Analysis ToolKit (GATK).
• The pipeline offers a scalable solution for comprehensive WGS analysis, supporting multiple workflow configurations.

Conclusions:

• GeNePi provides an efficient, accurate, and scalable solution for WGS data analysis.
• Its comprehensive variant detection capabilities make it valuable for large-scale research and clinical applications.
• GeNePi represents a significant advancement towards establishing robust computational infrastructure for genomic medicine.