UNIVERSITY PARK, Pa. — In the quest to understand viruses, the tools researchers use may hold more significance than previously thought. A team at Penn State has found that the bioinformatic tools used to analyze plant virus genomes can yield inconsistent results, a revelation that could impact how scientists conduct their research on viral replication.
Every time a virus replicates within a host, there is a chance that the new copy will be an imperfect version of the original. Understanding the genome structure of these defective copies could provide insights into the virus’s biology. However, the study, published in the Journal of General Virology, reveals that the five tools available for identifying these defective genomes from next-generation sequencing datasets may not always agree.
Inconsistencies in Virus Genome Analysis Tools
Anthony Taylor, a doctoral student in plant biology and the lead author of the study, emphasizes the importance of using multiple programs for a comprehensive analysis. “We suggest utilizing more than one program when analyzing a dataset to get multiple ‘points of view’ and a thorough analysis of the data,” Taylor stated. He also noted that the method of sequencing could influence the outputs of these programs.
Despite decades of awareness about defective viruses, their roles in infection remain largely unexplored. Some researchers theorize that understanding these defective versions could enhance predictions of a virus’s evolution, pathogenesis, and transmission. Marco Archetti, an associate professor in the Eberly College of Science and co-author of the study, highlighted the potential therapeutic applications of these defective genomes. “It may be possible to create new defective versions to use as treatments,” Archetti explained.
Comparative Analysis and Findings
Taylor’s study involved a comparative analysis of five bioinformatic tools designed to discover defective genomes. By applying these tools to eight datasets, including two control datasets, the team aimed to evaluate the consistency of the results. The control datasets included one computer-generated dataset and another for SARS-CoV-2, where common junction points were already known.
The analysis revealed minimal overlap in the results from the five programs. Most identified different junction points, indicating a lack of consistency. Cristina Rosa, a professor of plant virology and co-author, stressed the importance of this finding. “There’s this incredible amount of data that we are generating with next-generation sequencing technology, which is very useful for asking biological questions,” Rosa said. “The reality is they’re good, but it might be best to run the same dataset through multiple programs to look at the overlap when conducting this type of research.”
Implications for Future Research
The study’s findings suggest that generating full-sequence datasets specifically for analyzing defective viral genomes, rather than relying on datasets from various labs, could improve the tools’ effectiveness. This approach could enhance their applicability to answer biologically relevant questions, such as those concerning virus replication.
Looking forward, the research underscores the need for a more standardized approach in bioinformatics tools for virus genome analysis. As scientists continue to explore the roles of defective viruses, the development of more reliable tools could pave the way for breakthroughs in understanding viral behaviors and developing innovative therapies.
The move represents a significant step in refining the methodologies used in virology research, potentially leading to more accurate predictions and effective treatments for viral infections.
