Benchmarking circRNA Detection Tools from Long-Read Sequencing Using Data-Driven and Flexible Simulation Framework

Circular RNAs (circRNAs) are unique non-coding RNAs with covalently closed loop structures formed through backsplicing events. Their stability, tissue-specific expression patterns, and potential as disease biomarkers have garnered increasing attention. However, their circular structure and diverse size range pose challenges for conventional sequencing technologies. Long-read Oxford Nanopore (ONT) sequencing offers promising capabilities for capturing entire circRNA molecules without fragmentation, yet the effectiveness of bioinformatic tools for analyzing this data remains understudied.

This study presents the first comprehensive benchmark comparison of three specialized tools for circRNA detection from ONT long-read data: CIRI-long (Zhang et al., 2021), IsoCIRC (Xin et al., 2021), and circNICK-Irs (Rahimi et al., 2021). To address the lack of standardized evaluation frameworks, we developed a novel computational pipeline, open-source and freely available, to generate realistic simulated circRNA ONT long-read datasets. Our pipeline integrates several molecular features of circRNAs extracted from established databases and real datasets into NanoSim tool (Hafezqorani et al., 2020) and outputs FASTQ reads reflecting therefore biological diversity and technical properties.

We systematically assessed tool performance across key metrics, including precision, recall, specificity, accuracy, and F1 score. Our analysis revealed distinct performance profiles: while all tools exhibited high specificity, they varied in precision and their ability to detect different circRNA subtypes, often showing limited sensitivity and precision. Notably, the overlap in detected circRNAs among tools was relatively low. Additionally, computational efficiency varied significantly across the tools. This suggests that relying on a single tool might not be ideal, and combining tools or improving algorithms could be necessary for more accurate circRNA detection from ONT data.

This benchmark provides valuable insights for researchers selecting appropriate tools for circRNA studies using ONT sequencing. Furthermore, our customizable simulation framework, offering a resource to optimize detection approaches and advance bioinformatic tool development for circRNA research is freely available at:<ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://gitlab.com/bioinfog/circall/nano-circ">https://gitlab.com/bioinfog/circall/nano-circ</ext-link>.