Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Comparative Study
. 2024 Oct 25;15(1):9208.
doi: 10.1038/s41467-024-53496-8.

Comparative analysis of methodologies for detecting extrachromosomal circular DNA

Xuyuan Gao #  1 , Ke Liu #  1 , Songwen Luo #  1 , Meifang Tang  1   2 , Nianping Liu  1 , Chen Jiang  1   2 , Jingwen Fang  1   3 , Shouzhen Li  1 , Yanbing Hou  1 , Chuang Guo  4   5   6 , Kun Qu  7   8   9
Affiliations
Comparative Study

Comparative analysis of methodologies for detecting extrachromosomal circular DNA

Xuyuan Gao et al. Nat Commun. .

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.

PubMed Disclaimer

Conflict of interest statement

Jingwen Fang is the chief executive officer of HanGen Biotech. The other authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Assessment of analysis pipelines in eccDNA identification.
a Schematic overview of the benchmarking workflow used to compare the performance of bioinformatic pipelines. The cell line, healthy tissue and tumor illustration were created in BioRender. Gao, X. (2024) BioRender.com/h74t202. ‘Std’ represents standard deviation. b Performance comparison of analysis pipelines at a simulated sequencing depth of 50X (bms, bwa-mem-samblaster; mIs, microDNA.InOne.sh). Data are presented as mean values +/- SEM. c Impact of simulated sequencing depth on eccDNA identification accuracy. Data are presented as mean values +/- SEM. d Impact of simulated sequencing depth on eccDNA identification duplication rates. Centre line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range. e Impact of chimeric DNA proportion on eccDNA identification recall. Data are presented as mean values +/- SEM. The ‘n’ in the figure represents the number of datasets successfully analyzed by corresponding analysis pipeline and is used as the sample size to evaluate the performance of the respective analysis pipeline in the analysis. For panels (b, c and d) n = 7, except for ecc_finder (asm-sr) with n = 6 and ECCsplorer with n = 6 when depth ≤ 10, n = 5 for depths between 15X and 25X, n = 4 for depths between 30X and 40X, and n = 3 for depths higher than 40X. For panel (e) n = 4 for only 4 datasets contain chimeric eccDNA. Source data are provided as a Source Data file.
Fig. 2
Fig. 2. Impact of eccDNA enrichment operations on eccDNA identification.
a Schematic overview of the experimental methods comparison. b eccDNA detection efficiency comparison. Data are presented as mean values +/- SEM. c Circular DNA enrichment efficiency. Data are presented as mean values +/- SEM. LDD, Linear DNA Digestion; Solution A, using Solution A for circular DNA purification; RCA, Rolling Cycle Amplification. d Detection efficiency for eccDNA with different length ranges. Data are presented as mean values +/- SEM. e Correlation between eccDNA density and coding gene density. Dots represent individual experiments and the shaded area represents 95% confidence interval. For all experiments, n = 3. Statistical analyses were performed using one-way ANOVA with Tukey correction for panel (b, c and d,) and two-sided Pearson correlation was performed for panel (e). The ‘p’ represents p-value. Source data are provided as a Source Data file.
Fig. 3
Fig. 3. Detection efficiency of ecDNA by 7 experimental methods.
a ecDNA detection efficiency of 7 experimental methods. b Comparison of the proportion of ecDNA in the total detected eccDNA. c Comparison of the detection efficiency of ecDNA with different length ranges by 7 experimental methods. d Comparison of the detection efficiency of nonecDNA with different length ranges by 7 experimental methods. Dots represent individual experiments; For all experiments, n = 3. Statistical analyses were performed using one-way ANOVA with Tukey correction; The ‘p’ represents p-value. Data are presented as mean values +/- SEM. Source data are provided as a Source Data file.
Fig. 4
Fig. 4. eccDNA profile heterogeneity across experimental methods.
a Proportion of highly-correlated eccDNA between each pair experimental replicates in vertical replicates; b Number of highly-correlated eccDNA between each pair of experimental replicates; c Number of detected oncogenes shared across different methods. d Number of oncogenes shared by different replicates. e Proportion of read mapped to repeat elements (n = 3). For panel e, statistical analyses were performed using one-way ANOVA with Tukey correction; The ‘p’ represents p-value. Source data are provided as a Source Data file.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Yi, E., Chamorro González, R., Henssen, A. G. & Verhaak, R. G. W. Extrachromosomal DNA amplifications in cancer. Nat. Rev. Genet.23, 760–771 (2022). - PMC - PubMed
    1. Verhaak, R. G. W., Bafna, V. & Mischel, P. S. Extrachromosomal oncogene amplification in tumour pathogenesis and evolution. Nat. Rev. Cancer19, 283–288 (2019). - PMC - PubMed
    1. Cox, D., Yuncken, C. & Spriggs, A. I. Minute chromatin bodies in malignant tumours of childhood. Lancet1, 55–58 (1965). - PubMed
    1. Turner, K. M. et al. Extrachromosomal oncogene amplification drives tumour evolution and genetic heterogeneity. Nature543, 122–125 (2017). - PMC - PubMed
    1. Paulsen, T., Shibata, Y., Kumar, P., Dillon, L. & Dutta, A. Small extrachromosomal circular DNAs, microDNA, produce short regulatory RNAs that suppress gene expression independent of canonical promoters. Nucleic Acids Res.47, 4586–4596 (2019). - PMC - PubMed

Publication types

MeSH terms

LinkOut - more resources