TY - JOUR
T1 - Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision
AU - Vromman, Marieke
AU - Anckaert, Jasper
AU - Bortoluzzi, Stefania
AU - Buratin, Alessia
AU - Chen, Chia Ying
AU - Chu, Qinjie
AU - Chuang, Trees Juen
AU - Dehghannasiri, Roozbeh
AU - Dieterich, Christoph
AU - Dong, Xin
AU - Flicek, Paul
AU - Gaffo, Enrico
AU - Gu, Wanjun
AU - He, Chunjiang
AU - Hoffmann, Steve
AU - Izuogu, Osagie
AU - Jackson, Michael S.
AU - Jakobi, Tobias
AU - Lai, Eric C.
AU - Nuytens, Justine
AU - Salzman, Julia
AU - Santibanez-Koref, Mauro
AU - Stadler, Peter
AU - Thas, Olivier
AU - Vanden Eynde, Eveline
AU - Verniers, Kimberly
AU - Wen, Guoxia
AU - Westholm, Jakub
AU - Yang, Li
AU - Ye, Chu Yu
AU - Yigit, Nurten
AU - Yuan, Guo Hua
AU - Zhang, Jinyang
AU - Zhao, Fangqing
AU - Vandesompele, Jo
AU - Volders, Pieter Jan
N1 - Publisher Copyright: © 2023, The Author(s), under exclusive licence to Springer Nature America, Inc.
PY - 2023/8
Y1 - 2023/8
N2 - 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.
AB - 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.
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U2 - 10.1038/s41592-023-01944-6
DO - 10.1038/s41592-023-01944-6
M3 - Article
C2 - 37443337
SN - 1548-7091
VL - 20
SP - 1159
EP - 1169
JO - Nature Methods
JF - Nature Methods
IS - 8
ER -