Across the globe, the virulent African Swine Fever pathogen presents an agonizing dilemma for pig farmers and a grave threat to food security. When this virus strikes, it can wipe out entire herds, leading to devastating economic losses and strict trade restrictions that ripple through communities. Containing outbreaks and preventing further spread hinges on understanding the virus itself – specifically, its complete genetic blueprint, what scientists call the African Swine Fever Virus genome. But historically, getting this information has been a significant challenge.

Imagine trying to build a complex machine, but all your specialized tools are scattered in different workshops, and you have to manually carry parts from one place to another for each step. That’s been the reality for researchers trying to analyze viral genomes. Many powerful tools exist for individual tasks, like reading genetic sequences or piecing them together, but there hasn’t been one single system that brings all these steps into a smooth, automated workflow. This fragmented approach often demands advanced computing expertise, leading to inefficiencies, potential errors, and inconsistent results that slow down our response to deadly outbreaks.

Recognizing this critical gap, researchers Vicente, Aldea, and Fernández-Pinero created a new system called 'PePApipe.' This is a custom-built bioinformatics analysis pipeline, designed to streamline the entire process of analyzing the African Swine Fever Virus genome. Think of it as that specialized toolbox, but it’s smart enough to guide itself through each step, making complex genomic analysis much more accessible to laboratory professionals, even those without deep bioinformatics experience.

The key thing to understand here is what a pipeline like PePApipe actually does. It starts with raw sequencing data. This is the unprocessed genetic information that comes directly from a sequencing machine. Imagine the machine reads billions of tiny fragments of the viral genome – like individual letters or short words from a massive book – but these fragments are all mixed up and some might even have errors. PePApipe’s first job is to make sense of this jumble.

It begins by checking the quality of these raw reads. The pipeline looks for something called Phred scores, which tell us how confident we can be that each genetic letter was read correctly. Only reads with Phred scores above 28, indicating high confidence, are carried forward. After this initial check, the system cleans the data, removing any unnecessary parts or duplicate genetic fragments that could skew the analysis.

Now that we have clean, high-quality data, PePApipe assesses the amount of viral genetic material present in the sample. This is important because samples from the field might contain a lot of host DNA mixed with very little viral DNA. The pipeline sets a threshold: if less than 75% of the genetic material is identified as viral, it automatically triggers an extra filtering step. This step specifically removes the non-viral genetic information, ensuring that only the relevant viral pieces move ahead for detailed analysis.

With that foundation established, the pipeline moves to a crucial process called de novo genome assembly. Think of it this way: you have millions of tiny, overlapping genetic puzzle pieces, and you need to reconstruct the entire picture without having seen it before. That’s what de novo assembly does. It meticulously fits these fragments together to build the virus’s complete genetic sequence from scratch. To make this process more reliable, PePApipe can draw on up to ten existing reference sequences from ASFV genotype II, a common strain, helping it to assemble the new genome more accurately. Once the full genetic sequence is assembled, PePApipe generates a consensus genome sequence. This is the most accurate and complete version of the analyzed viral genome, representing the best possible reconstruction from the available data. To ensure this sequence is reliable for further study, the pipeline requires a minimum average sequencing depth of 30 times – meaning each genetic letter in the reconstructed genome was, on average, read 30 separate times. It also aims for a genome coverage breadth of at least 90-95%, which means nearly the entire length of the virus’s genetic code has been successfully reconstructed.

The final core step is variant calling. This function pinpoints any specific changes or differences in the virus’s genetic code compared to a known reference. These tiny variations, like genetic typos, are vital clues for tracking how the virus might be evolving, understanding how it causes disease, and tracing its spread across regions. To minimize false alarms, the pipeline uses a conservative threshold, requiring at least five separate reads to support a detected change before it's flagged as a true variant.

In total, PePApipe integrates thirteen software tools into one automated workflow, handling everything from initial quality control to de novo genome assembly and variant calling. This integrated approach significantly reduces the potential for manual errors and ensures that results are consistent and reproducible, which is critical for comparative studies and outbreak investigations. The data produced by PePApipe provides the essential building blocks for more advanced analyses, like mapping out evolutionary family trees of the virus or using machine learning to predict its behavior.

While PePApipe makes powerful genomic analysis more accessible, users still need basic familiarity with computer command-line tools to operate it. The pipeline is also optimized for samples that have a decent amount of viral genetic material to begin with. The researchers have clear plans for the future. A primary next step is to integrate a dedicated tool for viral genome annotation directly into the pipeline. This is a complex but crucial stage that involves identifying and labeling all the genes within the ASFV genome, which can contain over 180 distinct genetic instructions. Beyond ASFV, PePApipe is designed with flexibility in mind. Its parameters can be easily adjusted, opening the door for its use in studying other viral species. This adaptability positions PePApipe as a valuable and extensible resource, helping scientists globally improve disease surveillance and accelerate our understanding of infectious threats. From scattered genetic fragments, this new tool builds a complete map, offering a clearer path forward in the fight against devastating viral diseases. ◆

Sourced from: "'PePApipe': A complete bioinformatics analysis pipeline for African Swine Fever Virus genome." by Lopez-Chavarrias, Vicente, Aldea, Irene, and Fernández-Pinero, Jovita