Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision

Marieke Vromman, Jasper Anckaert, Stefania Bortoluzzi, Alessia Buratin, Chia Ying Chen, Qinjie Chu, Trees Juen Chuang, Roozbeh Dehghannasiri, Christoph Dieterich, Xin Dong, Paul Flicek, Enrico Gaffo, Wanjun Gu, Chunjiang He, Steve Hoffmann, Osagie Izuogu, Michael S. Jackson, Tobias Jakobi, Eric C. Lai, Justine NuytensJulia Salzman, Mauro Santibanez-Koref, Peter Stadler, Olivier Thas, Eveline Vanden Eynde, Kimberly Verniers, Guoxia Wen, Jakub Westholm, Li Yang, Chu Yu Ye, Nurten Yigit, Guo Hua Yuan, Jinyang Zhang, Fangqing Zhao, Jo Vandesompele, Pieter Jan Volders

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

The detection of circular RNA molecules (circRNAs) is typically based on short-read RNA sequencing data processed using computational tools. Numerous such tools have been developed, but a systematic comparison with orthogonal validation is missing. Here, we set up a circRNA detection tool benchmarking study, in which 16 tools detected more than 315,000 unique circRNAs in three deeply sequenced human cell types. Next, 1,516 predicted circRNAs were validated using three orthogonal methods. Generally, tool-specific precision is high and similar (median of 98.8%, 96.3% and 95.5% for qPCR, RNase R and amplicon sequencing, respectively) whereas the sensitivity and number of predicted circRNAs (ranging from 1,372 to 58,032) are the most significant differentiators. Of note, precision values are lower when evaluating low-abundance circRNAs. We also show that the tools can be used complementarily to increase detection sensitivity. Finally, we offer recommendations for future circRNA detection and validation.

Original languageEnglish (US)
Pages (from-to)1159-1169
Number of pages11
JournalNature Methods
Volume20
Issue number8
DOIs
StatePublished - Aug 2023

ASJC Scopus subject areas

  • Biotechnology
  • Biochemistry
  • Molecular Biology
  • Cell Biology

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