Winner 2024 - An open-source solution for finding ancient viral integrations in genomic data

Open Research objectives/practices

detectEVE is an open-source tool, released under the MIT License to promote open access, collaboration, and reuse. The full source code and documentation are publicly available on GitHub and the tool can be easily installed using conda. We further provide installation guidelines, an extensive tutorial, and a FAQ on GitHub Wiki. Additionally, we provide transparency in our development process by tracking progress and design decisions through public issue tracking on GitHub. This approach not only allows us to address problems as they arise, but also encourages user feedback which we actively incorporate into our tool's ongoing improvement. Methodology and benchmarking results are published as a pre-print, with a manuscript under review in an open-access journal.

Introduction

detectEVE is a fully automated command-line tool for the rapid detection of viral integrations in host genomes. Our user-friendly design and customizable user interface, empower researchers from diverse backgrounds to utilize the tool and cater to both novices and experts. We aim to promote data sharing and standardization in virology and genomics research.

Motivation

Detecting viral genetic material integrated within host genomes presents significant challenges due to its high heterogeneity and complex genomic structures. This complexity has led to a scarcity of specialized tools, often resulting in individual research groups developing their own pipelines. These custom solutions are rarely shared and typically lack a unified output, which limits data reusability across the scientific community. To address these barriers, we created our open-source tool that emphasizes user-friendliness and flexibility. It provides fully automated, standardized data outputs with easily trackable metadata for future research use. By employing detectEVE, researchers can enhance their individual projects while also fostering collaboration and data sharing, thereby promoting open research practices. Through its design and functionality, our tool fills critical gaps in virology and genomics, contributing to a more inclusive research environment.

Lessons learned

During benchmarking, we conducted comparative analyses against existing detection pipelines. This process revealed significant challenges, particularly with installation and deployment, as many tools lacked comprehensive documentation and/or required specialized knowledge. These barriers limit access for researchers with minimal bioinformatics expertise. We recognized that merely providing a tool does not guarantee usability. Therefore, we prioritized user-friendliness in detectEVE’s design, ensuring it accommodates a broad audience. This emphasis on comprehensive user support promotes both the adoption and data reproducibility of our tool, aligning with the principles of open research.

URLs, references and further information

• Repository: https://github.com/thackl/detectEVE
• Pre-print: https://doi.org/10.1101/2024.09.06.611620
• Documentation: https://github.com/thackl/detectEVE/wiki