鉴于 eccDNA 与其相应的线性 DNA 的相似性，从短测序读数中重建染色体外环状 DNA (eccDNA) 的全长序列已被证明具有挑战性。之前的测序方法无法实现全长eccDNA的高通量检测。在此，我们开发了一种称为全长eccDNA检测（FLED）的新算法，基于滚环扩增和纳米孔长读长测序技术相结合的策略来重建eccDNA序列。通过 FLED 分析了 7 个人类上皮细胞和癌细胞系样本，每个样本鉴定了超过 5000 个全长 eccDNA。鉴定出的 eccDNA 的结构通过聚合酶链式反应 (PCR) 和桑格测序进行了验证。与其他已发表的基于纳米孔的 eccDNA 检测器相比，FLED 表现出更高的灵敏度。在癌细胞系中，与eccDNA区域重叠的基因在癌症相关通路中富集，并且可以在eccDNA分子上完整基因的上游或下游预测顺式调控元件，并且这些癌症相关基因的表达失调肿瘤细胞系，表明 eccDNA 在生物过程中的调节效力。该方法利用纳米孔长读长，能够无偏重建全长 eccDNA 序列。 FLED 使用 Python3 实现，可在 GitHub (https://github.com/FuyuLi/FLED) 上免费获取。© 作者 2023。由牛津大学出版社出版。版权所有。如需权限，请发送电子邮件至：journals.permissions@oup.com。

Reconstructing the full-length sequence of extrachromosomal circular DNA (eccDNA) from short sequencing reads has proved challenging given the similarity of eccDNAs and their corresponding linear DNAs. Previous sequencing methods were unable to achieve high-throughput detection of full-length eccDNAs. Herein, a novel algorithm was developed, called Full-Length eccDNA Detection (FLED), to reconstruct the sequence of eccDNAs based on the strategy that combined rolling circle amplification and nanopore long-reads sequencing technology. Seven human epithelial and cancer cell line samples were analyzed by FLED and over 5000 full-length eccDNAs were identified per sample. The structures of identified eccDNAs were validated by both Polymerase Chain Reaction (PCR) and Sanger sequencing. Compared to other published nanopore-based eccDNA detectors, FLED exhibited higher sensitivity. In cancer cell lines, the genes overlapped with eccDNA regions were enriched in cancer-related pathways and cis-regulatory elements can be predicted in the upstream or downstream of intact genes on eccDNA molecules, and the expressions of these cancer-related genes were dysregulated in tumor cell lines, indicating the regulatory potency of eccDNAs in biological processes. The proposed method takes advantage of nanopore long reads and enables unbiased reconstruction of full-length eccDNA sequences. FLED is implemented using Python3 which is freely available on GitHub (https://github.com/FuyuLi/FLED).© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.