Evidence brief: ‘PePApipe’: A complete bioinformatics analysis pipeline for African Swine Fever Virus genome

Abstract

African Swine Fever Virus (ASFV) is of high concern in porcine livestock across the world due to both the high mortality rates and the trade restrictions imposed on affected regions. The viral genome is large and complex, and genomic analysis is essential for tracing its origin and evolution. Although several bioinformatics tools exist for genome assembly and analysis, no single platform integrates all necessary steps in an accessible and systematic way. In this study the authors developed ‘PePApipe’, a custom-built, user-friendly pipeline that enables rapid, complete, and efficient ASFV genome analysis. It is specifically designed for laboratory professionals with limited bioinformatics experience, requiring only basic command-line knowledge. Starting from raw sequencing data, PePApipe integrates thirteen software tools into one automated workflow, covering quality control and pre-processing of raw reads, de novo genome assembly and variant calling. Programmed in Python, it can be executed locally through bash scripts, or using a Slurm protocol for batch processing of multiple samples. The main outputs are the ASFV consensus genome sequence and a file listing its putative variants compared to the selected reference genome. PePApipe classifies generated files into structured folders and produces intermediate files that can be used as inputs for further or parallel analyses; users can also enable or disable specific steps in each particular case. This pipeline is adaptable and complementary to downstream steps such as viral genome annotation or genome visualization. By consolidating all stages of viral genome analysis into a single automated workflow, PePApipe reduces the likelihood of user error, and enhances reproducibility and efficiency. This user-friendly pipeline facilitates the transition from sequencing to assembly and downstream analysis of viral genomes, ensuring a fast and reliable response to molecular analysis demands. Finally, the pipeline can be easily adapted to the study of other viral species, expanding its application in infectious diseases surveillance.

Introduction

Despite the availability of generic bioinformatics tools for genome analysis across a variety of organisms, the intrinsic biological and structural differences between genomes often necessitate the development of specialized tools tailored to the organism of interest—this is particularly true in the case of viruses. One such organism is the African Swine Fever Virus (ASFV), a large, complex, double-stranded DNA virus belonging to the family Asfarviridae [1]. ASFV is of critical concern to the global swine industry due to its ability to cause a haemorrhagic disease with near 100% mortality in domestic pigs, as well as the substantial trade restrictions imposed on affected regions following confirmed outbreaks.

The absence of an effective vaccine, coupled with the virus’s complex biology and high environmental stability, makes ASFV a formidable challenge for veterinary and agricultural systems worldwide [2,3]. Indeed, ASFV has garnered increasing scientific interest in recent years due to its severe threat to global pig production and food security derived from the worldwide epidemiological scenario. The virus has expanded beyond its original endemic regions in sub-Saharan Africa, initially entering Eastern Europe in 2007 and further spreading throughout Europe, Asia, and reaching the Americas in 2021, which has further intensified the need to understand its pathogenesis, transmission mechanisms, and evolutionary dynamics [4,5]. Moreover, knowing the origin of a new ASFV incursion and tracing the virus spread are critically demanded right after an outbreak occurs in a new region. Furthermore, the socioeconomic impact of ASF outbreaks, particularly in countries with large swine industries such as China or Germany, has underscored the urgent need for more targeted control strategies and improved diagnostic tools [6,7].