Coppe, A. | bioRxiv Science

bioRxiv2025

GeNePi: a GPU-enhanced Next Generation Bioinformatics Pipeline for Whole Genome Sequencing Analysis

Next Generation Sequencing (NGS) has revolutionized genome biology, enabling the rapid sequencing of an entire human genome and facilitating the integration of Whole Genome Sequencing (WGS) into both research and clinical applications. The high-throughput nature of NGS and the complex data processing required has driven the need for advanced computational infrastructures to analyse these large datasets. The aim of this work is to introduce an innovative bioinformatic pipeline, named GeNePi, for the efficient and precise analysis of WGS short paired-end reads. Built on the Nextflow framework with a modular structure, GeNePi incorporates GPU-accelerated algorithms and supports multiple work-flow configurations. The pipeline automates the extraction of biologically relevant insights from raw WGS data, including: disease-related variants such as single nucleotide variants (SNVs), small insertions or deletions (INDELs), copy number variants (CNVs), and structural variants (SVs). Optimized for high-performance computing (HPC) environments, it takes advantage of job-scheduler submissions, parallelised processing, and tailored resource allocation for each analysis step. Tested on synthetic and real datasets, GeNePi accurately identifies genomic variants, with performances comparable to that of state-of-art tools. 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.

bioinformatics↗

bioRxiv2024

A highly contiguous reference genome for the Alpine ibex (Capra ibex)

Species conservation efforts can be threatened by deleterious mutation accumulation following population contractions. In addition to de novo mutations, a significant source of genetic load could be deleterious variants introduced into a population through hybridization. Hence, even successfully restored species may face deleterious mutation swamping due to hybridization with an abundant and closely related species. The outcomes of such hybridization events are poorly understood given the complex interplay of introduced adaptive and maladaptive variation. Here, we analyze this potential risk for Alpine ibex (Capra ibex), a flagship species of large mammal restoration in the Alps. Near-extinction two centuries ago resulted in exceptionally low genome-wide diversity and increased inbreeding, which facilitated the purging of severe deleterious mutations but accumulation of less severe ones. We produced a highly contiguous chromosome-level genome assembly of the Alpine ibex capturing structural divergence from its closest domestic species, the domestic goat (Capra hircus) known to hybridize with Alpine ibex Genome sequencing of eight recent ibex-goat hybrids and backcrosses from two hybrid swarms in Northern Italy revealed highly diverse recombinants and an average of 30 masked, predicted loss-of-function (LOF) mutations per hybrid compared to 10 in non-hybrid Alpine ibex. This exposes Alpine ibex to further backcrosses, exposing their vulnerable gene pool to an influx of hybridization load. Individual-based genomic simulations suggest that such LOF load would return to pre- hybridization levels with a lag of over 100 generations after gene flow subsides. Hybridization could also disrupt local adaptation in the recipient species. Our work provides a direct estimate of hybridization load and, by this, informs on the complexity of managing endangered gene pools in the face of hybridization.

evolutionary biology↗