Comparative analysis of methodologies for detecting extrachromosomal circular DNA

Xuyuan Gao, Ke Liu, Songwen Luo, Meifang Tang, Nianping Liu, Chen Jiang, Jingwen Fang, Shouzhen Li, Yanbing Hou, Chuang Guo () and Kun Qu ()
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Xuyuan Gao: University of Science and Technology of China
Ke Liu: University of Science and Technology of China
Songwen Luo: University of Science and Technology of China
Meifang Tang: University of Science and Technology of China
Nianping Liu: University of Science and Technology of China
Chen Jiang: University of Science and Technology of China
Jingwen Fang: University of Science and Technology of China
Shouzhen Li: University of Science and Technology of China
Yanbing Hou: University of Science and Technology of China
Chuang Guo: University of Science and Technology of China
Kun Qu: University of Science and Technology of China

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract Extrachromosomal circular DNA (eccDNA) is crucial in oncogene amplification, gene transcription regulation, and intratumor heterogeneity. While various analysis pipelines and experimental methods have been developed for eccDNA identification, their detection efficiencies have not been systematically assessed. To address this, we evaluate the performance of 7 analysis pipelines using seven simulated datasets, in terms of accuracy, identity, duplication rate, and computational resource consumption. We also compare the eccDNA detection efficiency of 7 experimental methods through twenty-one real sequencing datasets. Here, we show that Circle-Map and Circle_finder (bwa-mem-samblaster) outperform the other short-read pipelines. However, Circle_finder (bwa-mem-samblaster) exhibits notable redundancy in its outcomes. CReSIL is the most effective pipeline for eccDNA detection in long-read sequencing data at depths higher than 10X. Moreover, long-read sequencing-based Circle-Seq shows superior efficiency in detecting copy number-amplified eccDNA over 10 kb in length. These results offer valuable insights for researchers in choosing the suitable methods for eccDNA research.

Date: 2024
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DOI: 10.1038/s41467-024-53496-8

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