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

**Authors:** Stefano Marangoni, Federica Furia, Debora Charrance, Agata Fant, Salvatore Di Dio, Sara Trova, Giovanni Spirito, Francesco Musacchia, Alessandro Coppe, Stefano Gustincich, Manuela Vecchi, Fabio Landuzzi, Andrea Cavalli

PMC · DOI: 10.1093/bib/bbag001 · Briefings in Bioinformatics · 2026-01-25

## TL;DR

GeNePi is a fast and accurate pipeline for whole-genome sequencing that uses GPU acceleration to improve performance and scalability.

## Contribution

GeNePi introduces a GPU-accelerated, modular pipeline for comprehensive variant discovery in WGS data.

## Key findings

- GeNePi achieves performance comparable to state-of-the-art tools like GATK while using GPU acceleration.
- The pipeline supports detection of multiple variant types, including SNVs, CNVs, and structural variants.
- Benchmarking shows high accuracy on both synthetic and real datasets.

## Abstract

Next-generation sequencing (NGS) has revolutionized genome biology by enabling rapid whole-genome sequencing (WGS) and driving its adoption in research and clinical settings. However, the high-throughput nature of NGS and the complexity of downstream analyses demand robust computational solutions. We present GeNePi, a modular bioinformatic pipeline for efficient and accurate analysis of WGS short paired-end reads. GeNePi is a genomics analysis pipeline built on the Nextflow framework, integrating graphics processing unit (GPU)-accelerated algorithms from NVIDIA Clara Parabricks to enable high-performance variant discovery. The pipeline supports multiple workflow configurations and automates the detection of a broad range of genomic variants, including single-nucleotide variants and small insertions/deletions via GPU-accelerated HaplotypeCaller, copy number variants (CNVs) using CNVkit, and structural variants through a consensus approach combining Manta, Lumpy, BreakDancer, and CNVnator. Additionally, GeNePi incorporates MELT for the detection of mobile element insertions, providing a comprehensive framework for variant discovery and characterization. Benchmarking on synthetic and real datasets demonstrates high accuracy and performance comparable to state-of-the-art tools such as Genome Analysis ToolKit (GATK), establishing GeNePi as a scalable solution for comprehensive WGS analysis. These features make GeNePi a valuable instrument for large-scale analyses in both research and clinical contexts, representing a key step towards the establishment of National Centers for Computational and Technological Medicine.

## Full-text entities

- **Genes:** LGR5 (leucine rich repeat containing G protein-coupled receptor 5) [NCBI Gene 8549] {aka FEX, GPR49, GPR67, GRP49, HG38}
- **Diseases:** TP (MESH:C579935), Cancer (MESH:D009369), brain-related disorders (MESH:D001927), Hereditary Breast and Ovarian Cancer (MESH:D061325)
- **Species:** Homo sapiens (human, species) [taxon 9606]
- **Cell lines:** SCLC21H — Homo sapiens (Human), Lung small cell carcinoma, Cancer cell line (CVCL_0024), PANC1005 — Homo sapiens (Human), Pancreatic ductal adenocarcinoma, Cancer cell line (CVCL_1639)

## Full text

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## Figures

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## References

50 references — full list in the complete paper: https://tomesphere.com/paper/PMC12832024/full.md

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Source: https://tomesphere.com/paper/PMC12832024