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  • Review Article
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Bioinformatics advances in eccDNA identification and analysis

Oncogene (2024)Cite this article

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Abstract

Extrachromosomal circular DNAs (eccDNAs) are a unique class of chromosome-originating circular DNA molecules, which are closely linked to oncogene amplification. Due to recent technological advances, particularly in high-throughput sequencing technology, bioinformatics methods based on sequencing data have become primary approaches for eccDNA identification and functional analysis. Currently, eccDNA-relevant databases incorporate previously identified eccDNA and provide thorough functional annotations and predictions, thereby serving as a valuable resource for eccDNA research. In this review, we collected around 20 available eccDNA-associated bioinformatics tools, including identification tools and annotation databases, and summarized their properties and capabilities. We evaluated some of the eccDNA detection methods in simulated data to offer recommendations for future eccDNA detection. We also discussed the current limitations and prospects of bioinformatics methodologies in eccDNA research.

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Fig. 1: Historical timeline of eccDNA research.
Fig. 2: Biogenesis, classification, and functions of eccDNA in cancer.
Fig. 3: Illustrations of eccDNA experimental approaches and bioinformatics tools.
Fig. 4: Comparison of eccDNA identification methods evaluated by different publications.
Fig. 5: Comparison of eccDNA detected by eccDNA identification methods in simulated datasets.

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Data availability

The simulation pipeline and all scripts for eccDNA detection are available on GitHub (https://github.com/FuyuLi/Review_bioinformatics_in_eccDNA). The simulated datasets generated for evaluation are available from the corresponding authors and https://pan.seu.edu.cn:443/link/0300E3FCD85AA52A4301691B68BA318C.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant number: 61871121).

Author information

Authors and Affiliations

  1. State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China

    Fuyu Li, Wenxiang Lu, Ying Wang & Yunfei Bai

  2. Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210044, PR China

    Wenlong Ming

  3. Adams Center of Parkinson’s Disease Research, Yale School of Medicine, Yale University, 100 College St, New Haven, CT, 06511, USA

    Xianjun Dong

  4. Department of Neurology, Yale School of Medicine, Yale University, 100 College St, New Haven, CT, 06511, USA

    Xianjun Dong

Authors
  1. Fuyu Li
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  2. Wenlong Ming
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  3. Wenxiang Lu
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  4. Ying Wang
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  5. Xianjun Dong
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  6. Yunfei Bai
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Contributions

YB provided direction and guidance throughout the preparation of this manuscript. FL collected and interpreted the studies, and wrote the manuscript with the assistance of WM and WL. Both WM and YW made significant contributions to the final manuscript. Both XD and YB reviewed and made significant revisions to the manuscript. All authors read and approved the submission and publication.

Corresponding authors

Correspondence to Wenlong Ming, Xianjun Dong or Yunfei Bai.

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Li, F., Ming, W., Lu, W. et al. Bioinformatics advances in eccDNA identification and analysis. Oncogene (2024). https://doi.org/10.1038/s41388-024-03138-6

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  • DOI: https://doi.org/10.1038/s41388-024-03138-6

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