AbstractExtrachromosomal 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.

摘要染色体外环状DNA（eccDNA）在癌基因扩增，基因转录调控和肿瘤内异质性中至关重要。虽然已经开发了各种用于eccDNA鉴定的分析管道和实验方法，但它们的检测效率尚未得到系统评估。

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.

为了解决这个问题，我们使用七个模拟数据集评估了7个分析管道在准确性，身份，重复率和计算资源消耗方面的性能。我们还通过21个真实测序数据集比较了7种实验方法的eccDNA检测效率。

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.

在这里，我们显示Circle Map和Circle\u finder（bwa mem samblaster）优于其他短读取管道。然而，Circle\u finder（bwa mem samblaster）在其结果中表现出明显的冗余。CReSIL是在深度超过10倍的长读取测序数据中检测eccDNA的最有效管道。

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..

此外，基于长读测序的Circle-Seq在检测长度超过10kb的拷贝数扩增的eccDNA方面显示出优异的效率。这些结果为研究人员选择合适的eccDNA研究方法提供了有价值的见解。。

IntroductionSequencing-based studies have greatly advanced our understanding of extrachromosomal circular DNA (eccDNA), on its roles in oncogene amplification1,2,3,4, gene expression regulation5, genome rearrangements6,7, and intratumor heterogeneity4. Diverse analysis pipelines and experimental methods have been developed to detect eccDNA (Table 1).

引言基于测序的研究极大地提高了我们对染色体外环状DNA（eccDNA）在癌基因扩增1,2,3,4，基因表达调控5，基因组重排6,7和肿瘤内异质性4中的作用的理解。已经开发了多种分析管道和实验方法来检测eccDNA（表1）。

Viraj Deshpande et al. introduced the AmpliconArchitect (AA) algorithm to predict amplicon structures and eccDNA from short-read (SR) whole-genome sequencing (WGS) (WGS-SR) data8. CReSIL utilizes coverage depths and breakpoint reads to identify eccDNA from long-read (LR) WGS (WGS-LR) data9. Kumar et al.

Viraj Deshpande等人介绍了AmpliconArchitect（AA）算法，该算法可从短读（SR）全基因组测序（WGS）（WGS-SR）数据预测扩增子结构和eccDNA 8。CReSIL利用覆盖深度和断点读取从长读取（LR）WGS（WGS-LR）数据中识别eccDNA 9。库马尔等人。

developed Circle_finder to identify eccDNA from short-read ATAC-Seq (ATAC-Seq-SR) data by analyzing split reads for eccDNA coordinates10. However, the performance of these analysis pipelines might be limited by the data generated from the corresponding experimental methods. For example, WGS and ATAC-Seq may have low eccDNA detection efficiency because vast majority of the sequencing reads were generated from linear DNA, and WGS-SR can only detect the copy number amplified eccDNA (ecDNA)4,6,11.Table 1 Summary of eccDNA analysis pipelines and supported experimental methodsFull size tableTo enhance eccDNA detection, researchers have developed methods such as Circle-Seq7,12,13 and 3SEP14,15 for eccDNA enrichment from crude DNA.

开发了Circle\u finder，通过分析eccDNA坐标的分裂读数，从短读ATAC-Seq（ATAC-Seq SR）数据中识别eccDNA 10。然而，这些分析管道的性能可能会受到相应实验方法产生的数据的限制。例如，WGS和ATAC-Seq可能具有较低的eccDNA检测效率，因为绝大多数测序读数是由线性DNA产生的，而WGS-SR只能检测拷贝数扩增的eccDNA（ecDNA）4,6,11。表1 eccDNA分析管道和支持的实验方法概述全尺寸表为了增强eccDNA检测，研究人员开发了Circle-Seq7,12,13和3SEP14,15等方法，用于从粗DNA中富集eccDNA。

Circle-Seq utilizes rolling circle amplification (RCA) for circular DNA amplification, whereas 3SEP employs Solution A for selective circular DNA recovery. Post-enrichment, eccDNA undergoes library construction for sequencing on platforms like Illumina (Circle-Seq-SR/3SEP-SR) or Oxford Nanopore Technology (ONT) (Circle-Seq-LR/3SEP-LR).

Circle-Seq利用滚环扩增（RCA）进行环状DNA扩增，而3SEP使用溶液A进行选择性环状DNA回收。富集后，eccDNA在Illumina（Circle-Seq SR/3SEP-SR）或Oxford Nanopore Technology（ONT）（Circle-Seq LR/3SEP-LR）等平台上进行文库构建以进行测序。

Concurrently, various analysis pipelines have been developed to proces.

同时，已经开发了各种分析管道来处理。

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We applied ComplexHeatmap package46 to visualize our results.Repeat elements analysisThe genomic coordinates of repeat elements on the hg38 reference genome were obtained from UCSC genome browser47. We used pysam to calculate the proportion of reads mapped to different repeat elements, including LTR, LINE, SINE and satellite.Circular DNA enrichment efficiency evaluationqPCR was used to evaluate the circular DNA enrichment efficiency.

我们应用ComplexHeatmap软件包46来可视化我们的结果。重复元件分析hg38参考基因组上重复元件的基因组坐标是从UCSC基因组浏览器47获得的。我们使用pysam来计算映射到不同重复元素（包括LTR，LINE，SINE和satellite）的读取比例。循环DNA富集效率评估qPCR用于评估循环DNA富集效率。

qPCR primers for pUC19 (F: GCAGGTCGACTCTAGAGGAT, R: GGGCCTCTTCGCTATTACGC, ordered from Sangon Biotech), and Egfr fragment (F: AAACGGAAGATCCTGCCCTG; R: GTGTACCCTGAACACGAGGG, ordered from Sangon Biotech) were used to quantify the circular DNA and linear DNA, respectively. The ∆\({Ct}\)(original) was used to normalize the qPCR results.$$\Delta {Ct}\left({{original}}\right)=\frac{\mathop{\sum }_{i=1}^{N}\left({{Ct}\left({{pUC}}19\right)}_{i}-{{Ct}\left({Egfr}\right)}_{i}\right)}{N}$$.

使用pUC19（F:GCAGGTCGACTCTAGAGGAT，R:GGGCCTCTTCGCTATTACGC，从Sangon Biotech订购）和Egfr片段（F:AAACGGAAGATCCTGCCTG；R:GTGTACCCTGAACAGGG，从Sangon Biotech订购）的qPCR引物分别定量环状DNA和线性DNA。Δ\（{Ct}\）（原始）用于标准化qPCR结果$$\Delta{Ct}\左（{{original}}\右）=\ frac{\ mathop{\ sum}{i=1}^{N}\左（{{Ct}\左（{{pUC}19 \右）}_{i}-。

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While \({{Ct}\left({{{\rm{pUC}}}}19\right)}_{i}\) and \({{Ct}\left({Egfr}\right)}_{i}\) represent the cycle threshold (Ct) value of pUC19 and Ct value of Egfr fragment of the replicate i of the original DNA pool. N represents the number of replicates.The circular DNA enrichment efficiency for each step was calculated by:$${{Circular}}\; {{enrichment}}\; {{efficiency}}\left({{{{\rm{Log}}}}}_{2}\right)=\frac{\mathop{\sum }_{j=1}^{N}-({\Delta {Ct}\left({{{\rm{step}}}}\right)}_{j}-\Delta {Ct}\left({{{\rm{original}}}}\right))}{N}$$.

而\（{Ct}\ left（{{rm{pUC}}}19 \ right）}{i}\）和\（{Ct}\ left（{Egfr}\ right）}{i}\）代表原始DNA库重复序列i的pUC19的循环阈值（Ct）值和Egfr片段的Ct值。N代表重复次数。每个步骤的环状DNA富集效率通过以下公式计算：$${{环状}}\；{{{富集}}\；{{{效率}}\左（{{{{\ rm{Log}}}}}}{2}\右）=\ frac{\ mathop{\ sum}{j=1}^{N}-（{\ Delta{Ct}\左（{{\ rm{步骤}}}\右）}_{j}-\Delta{Ct}\左（{{\rm{original}}}\右））}{N}$$。

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\({\Delta {Ct}\left({{{\rm{step}}}}\right)}_{j}\) was calculated by:$${\Delta {Ct}\left({{{\rm{step}}}}\right)}_{j}={{Ct}\left({{{\rm{pUC}}}}19\right)}_{j}-{{Ct}\left({Egfr}\right)}_{j}$$

\（{\ Delta{Ct}\ left（{{\ rm{step}}}\ right）}{j}\）的计算公式为：$${\ Delta{Ct}\ left（{{\ rm{step}}}\ right）}{j}={Ct}\ left（{\ rm{pUC}}}19 \ right）}_{j}-{{Ct}\左（{Egfr}\右）}{j}$$

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While \({{Ct}\left({{{\rm{pUC}}}}19\right)}_{j}\) and \({{Ct}\left({Egfr}\right)}_{j}\) represent the Ct value of pUC19 and Ct value of Egfr fragment of the replicate j after the specific circular DNA enrichment step. N represents the number of replicates.PCR validationWe created a numerical index for each eccDNA from each sample and used the random number generating formula in EXCEL (=randbetween(start index:end index)) to select the eccDNA.

而\（{Ct}\ left（{{rm{pUC}}}19 \ right）}{j}\）和\（{Ct}\ left（{Egfr}\ right）}{j}\）代表特定环状DNA富集步骤后重复j的pUC19的Ct值和Egfr片段的Ct值。N代表重复次数。PCR验证我们为每个样本中的每个eccDNA创建了一个数字索引，并使用EXCEL中的随机数生成公式（=randbetween（开始索引：结束索引））来选择eccDNA。

For the eccDNA that we could not design primers (potentially due to repeat sequences or low sequence complexity), we added 1 to the rolled random number and redesigned the primer for the newly indexed eccDNA. DNA sequences spanning the breakpoint were obtained by using Genome Browser (https://genome.ucsc.edu/index.html).

对于我们无法设计引物的eccDNA（可能是由于重复序列或序列复杂性低），我们在滚动随机数中添加了1，并为新索引的eccDNA重新设计了引物。通过使用基因组浏览器获得跨越断点的DNA序列(https://genome.ucsc.edu/index.html)。

Primers targeting the eccDNA breakpoint were designed by using Primer-Blast (https://www.ncbi.nlm.nih.gov/tools/primer-blast/) (Supplementary Data 2) and ordered from Sangon Biotech. The Hela cell genome was extracted by using the DNeasy® Blood & Tissue Kit (QIAGEN Cat. No. 69504). KOD FX (TOYOBO No.

利用Primer-Blast设计了靶向eccDNA断裂点的引物(https://www.ncbi.nlm.nih.gov/tools/primer-blast/)（补充数据2），并从Sangon Biotech订购。通过使用DNeasy®血液和组织试剂盒（QIAGEN目录号69504）提取Hela细胞基因组。KOD FX（TOYOBO No。

KFX-101) was used to perform the PCR. In brief, 20 ng DNA template (Genome DNA or Sample), 1.5 µL 10 µM forward primer, 1.5 µL 10 µM reverse primer, 4 µl 2 mM dNTPs, 10 µL 2X PCR Buffer for KOD FX, 1 µL KOD FX and nuclease-free water (Invitrogen 10977015) (to a 20 µL final volume) were combined. PCR was carried out by using the following thermal cycle: 94 °C for 2 minutes and then 30 cycles at 98 °C for 10 s, 60 °C for 30 s, 68 °C for 1 minute and 68 °C for 5 minutes.

KFX-101）用于进行PCR。简而言之，将20 ng DNA模板（基因组DNA或样品），1.5µl10µM正向引物，1.5µl10µM反向引物，4µl2mM dNTPs，10µlKOD FX 2X PCR缓冲液，1µlKOD FX和无核酸酶水（Invitrogen 10977015）（最终体积为20µL）合并。通过使用以下热循环进行PCR:94℃2分钟，然后在98℃10秒，60℃30秒，68℃1分钟和68℃5分钟的30个循环。

The PCR product was cut from the electrophoresis gel and sent for Sanger sequencing validation (by Sangon Biotech). We classified chimeric eccDNA as fully validated when all breakpoints were confirmed through Sanger sequencing (considered .

从电泳凝胶上切下PCR产物，并送去进行Sanger测序验证（由Sangon Biotech提供）。当通过Sanger测序确认所有断点时，我们将嵌合eccDNA分类为完全验证的（考虑）。

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$${Precision}=\frac{{TP}}{{TP}+{FP}}$$

$${精度}=\frac{{TP}}{{TP}+{FP}$$

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$${Recall}=\frac{{TP}}{{TP}+{FN}}$$

$${召回}=\frac{{TP}}{{TP}+{FN}$$

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Where TP represents the number of true positive event, FP represents the number of false positive event, and FN represents the number of false negative event.Base pair difference$${Base}\; {pair}\; {difference}=\frac{\mathop{\sum }_{i=1}^{N}\left({LEN}R{-}{LEN}1+{LEN}Q{-}{LEN}2\right)}{N}$$.

其中TP表示真阳性事件的数量，FP表示假阳性事件的数量，FN表示假阴性事件的数量。碱基对差异$${Base}\；{对}\；{差}=\frac{\mathop{\sum}\ui=1}^{N}\左({LEN}R{-}{LEN}1+（笑声）{LEN}Q{-}{LEN}2\右）}{N}$$。

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Where LEN R and LEN Q are length of reference eccDNA and query eccDNA, LEN 1 and LEN 2 are length of alignment on reference and query eccDNA. N is the number of query eccDNA that has more than 90% identity and 90% overlap with reference eccDNA.Duplication RateThe duplication rate is defined by the number of identified eccDNA (TP2) that have at least a 90% overlap of simulated eccDNA divided by the number of simulated eccDNAs (TP1) that can be identified by each pipeline.$${{Dupilcation}}{{Rate}}=\frac{{TP}2}{{TP}1}$$.

其中LEN R和LEN Q是参考eccDNA和查询eccDNA的长度，LEN 1和LEN 2是参考和查询eccDNA的比对长度。N是与参考eccDNA具有90%以上同一性和90%重叠的查询eccDNA的数量。复制率复制率由具有至少90%模拟eccDNA重叠的已识别eccDNA（TP2）的数量除以每个管道可以识别的模拟eccDNA（TP1）的数量来定义$${{{复制}}{{速率}}=\frac{{TP}2}（笑声）{{TP}1}$$。

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Detection efficiency of specific type of eccDNADetection efficiency of specific type of eccDNA (per Gb) was calculated by using the following formula:$${E}_{{ij}}=\frac{{n}_{{ij}}}{{D}_{i}}$$

$${E}_{{ij}}=\frac{{n}_{{{ij}}{{D}_{i} }$$

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Where: Eij is the detection efficiency of experimental method i in detecting eccDNA type j, nij is the number of eccDNA in type j detected by experimental method i, and Di is the size of the data (Gb) generated by experimental method i.Statistics & reproducibilityFor performance evaluation of bioinformatic pipelines.

其中：Eij是实验方法i检测j型eccDNA的检测效率，nij是实验方法i检测到的j型eccDNA的数量，Di是实验方法i产生的数据大小（Gb）。统计和重现性用于生物信息学管道的性能评估。

We used Seaborn48 to visualize statistical data. Each point showed the Mean ± SEM (Standard Error of the Mean) in the figure. For column chart, one-way ANOVA (by GraphPad Prism 9) was used to evaluate the statistical significance (degrees of freedom between methods are 6, and degrees of freedom within methods are 14).

我们使用Seaborn48可视化统计数据。每个点显示图中的平均值±SEM（平均值的标准误差）。对于柱状图，使用单向ANOVA（通过GraphPad Prism 9）来评估统计显着性（方法之间的自由度为6，方法内的自由度为14）。

For group column chart we also used one-way ANOVA (degrees of freedom between methods are 6 and degrees of freedom within methods are 14), because we focused on the comparison within each length range. Each column showed the Mean ± SEM and data points were shown as black dot on the column. For correlation dot plot (Fig. 2e), we used two-sided Pearson correlation in scipy.stats49 to measure the linear relationship between the density of coding genes and the density of eccDNA for each chromosome, and used Seaborn to present the result.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article..

对于组柱状图，我们还使用了单向方差分析（方法之间的自由度为6，方法内的自由度为14），因为我们专注于每个长度范围内的比较。每列显示平均值±SEM，数据点在列上显示为黑点。对于相关点图（图2e），我们使用scipy.stats49中的双侧Pearson相关性来测量编码基因密度与每个染色体的eccDNA密度之间的线性关系，并使用Seaborn来呈现结果。报告摘要有关研究设计的更多信息，请参阅本文链接的Nature Portfolio Reporting Summary。。

Data availability

数据可用性

The raw sequencing data (WGS-SR, WGS-LR, ATAC-Seq-SR, 3SEP-SR, 3SEP-LR, Circle-Seq-SR, and Circle-Seq-LR) generated in this study are openly available and have been deposited in the Genome Sequence Archive for Human (GSA-Human) database50 in National Genomics Data Center51, China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences under accession code HRA006020.The public data used in this study in Supplementary Fig. 1 to generate our simulation datasets are openly available from following study..

本研究中产生的原始测序数据（WGS-SR，WGS-LR，ATAC-Seq SR，3SEP-SR，3SEP-LR，Circle-Seq SR和Circle-Seq LR）是公开的，并已保存在中国科学院国家生物信息中心/北京基因组研究所国家基因组学数据中心51的人类基因组序列档案（GSA Human）数据库50中，登录号为HRA006020。补充图1中用于生成我们的模拟数据集的本研究中使用的公共数据可从以下研究中公开获得。。

Dataset 1 (sperm cells): the raw sequencing data are available in the Sequence Read Archive (SRA) database under accession code PRJNA6558197.

数据集1（精子细胞）：原始测序数据可在序列读取存档（SRA）数据库中获得，登录号为PRJNA6558197。

Dataset 2 (EJM cell line) and Dataset 3 (JJN3 cell line): the raw sequencing data are available in the Sequence Read Archive (SRA) database under accession code PRJNA8068669.

数据集2（EJM细胞系）和数据集3（JJN3细胞系）：原始测序数据可在序列读取存档（SRA）数据库中获得，登录号为PRJNA8068669。

Dataset 4 (Kelly cell line): the raw sequencing data are available in the European Nucleotide Archive (ENA) database under accession code PRJEB5051820.

数据集4（凯利细胞系）：原始测序数据可在欧洲核苷酸档案（ENA）数据库中获得，登录号为PRJEB5051820。

Dataset 5 (medulloblastoma): the raw sequencing data are available in the Gene Expression Omnibus (GEO) repository under accession code GSE20517821.

数据集5（髓母细胞瘤）：原始测序数据可在Gene Expression Omnibus（GEO）存储库中获得，登录号为GSE20517821。

Dataset 6 (muscle cells): the raw sequencing data are available in the Sequence Read Archive (SRA) database under accession code SRR631540013.

数据集6（肌肉细胞）：原始测序数据可在序列读取存档（SRA）数据库中获得，登录号为SRR631540013。

Dataset 7 (OVCAR8 cell line): the raw sequencing data are available in the Gene Expression Omnibus (GEO) repository under accession code GSE6864422.

数据集7（OVCAR8细胞系）：原始测序数据可在Gene Expression Omnibus（GEO）存储库中获得，登录号为GSE6864422。

We reanalyzed the data of EJM, JJN3, Kelly cell line, and muscle cells with corresponding pipelines in Supplementary Fig. 1. We used processed data of sperm cells, medulloblastoma and OVCAR8 cell line in their original paper. The processed template files can be found at [https://github.com/QuKunLab/eccDNABenchmarking/tree/main/ecsim/ecsim/resource/template]..

我们在补充图1中用相应的管道重新分析了EJM，JJN3，Kelly细胞系和肌肉细胞的数据。我们在他们的原始论文中使用了精子细胞，髓母细胞瘤和OVCAR8细胞系的处理数据。处理过的模板文件可以在[https://github.com/QuKunLab/eccDNABenchmarking/tree/main/ecsim/ecsim/resource/template]。。

All other data supporting the findings described in this paper are available in the article and its Supplementary Information files. Source data are provided with this paper.

本文及其补充信息文件中提供了支持本文所述发现的所有其他数据。本文提供了源数据。

Code availability

代码可用性

All original code has been deposited at Github [https://github.com/QuKunLab/eccDNABenchmarking]. We uploaded all codes and scripts used for the analyses and figure plotting in this study to a public Zenodo repository [https://zenodo.org/records/13769429]52.The simulated datasets can be generated by using the uploaded code..

所有原始代码都存放在Github[https://github.com/QuKunLab/eccDNABenchmarking]。我们将本研究中用于分析和图形绘制的所有代码和脚本上传到公共Zenodo存储库[https://zenodo.org/records/13769429]52、可以使用上传的代码生成模拟数据集。。

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Database resources of the national genomics data center, china national center for bioinformation in 2022. Nucleic Acids Research 50, D27–D38 (2022).Liu, K. QuKunLab/eccDNABenchmarking: v1.0.1, https://doi.org/10.5281/zenodo.13769429 (2024).Download referencesAcknowledgementsWe thank all members in the Qu laboratory for helpful discussions.

2022年中国国家生物信息中心国家基因组数据中心的数据库资源。核酸研究50，D27–D38（2022）。Liu，K.QuKunLab/eccDNABenchmarking:v1.0.1，https://doi.org/10.5281/zenodo.13769429（2024年）。下载参考文献致谢我们感谢Qu实验室的所有成员进行了有益的讨论。

We are grateful for the gift of NIH3T3 cells from Prof. Shu Zhu of the University of Science and Technology of China. This work was supported by the National Natural Science Foundation of China grants (T2125012 to K.Q., 32270978 to C.G., and 32100457 to J.F.), the National Key R&D Program of China (2020YFA0112200 and 2022YFA1303200 to K.Q.), the CAS Project for Young Scientists in Basic Research (YSBR-005 to K.Q.), Strategic Priority Research Program of Chinese Academy of Sciences (Grant No.

我们非常感谢中国科技大学朱曙教授赠送的NIH3T3细胞。这项工作得到了国家自然科学基金资助（T2125012授予K.Q.，32270978授予C.G.，32100457授予J.F.），中国国家重点研发计划（2020YFA0112200和2022YFA1303200授予K.Q.），中国科学院基础研究青年科学家项目（YSBR-005授予K.Q.），中国科学院战略优先研究计划（批准号：。

XDB0940301 to K.Q.), USTC Research Funds of the Double First-Class Initiative (YD9100002032 to K.Q.) and the Fundamental Research Funds for the Central Universities (YD2070002019, WK9110000141, and WK2070000158 to K.Q.). We thank the USTC Supercomputing Center and the School of Life Science Bioinformatics Center for providing computing resources for this project.

XDB0940301至K.Q.），USTC双一流计划研究基金（YD9100002032至K.Q.）和中央大学基础研究基金（YD2070002019、WK9110000141和WK207000158至K.Q.）。我们感谢中国科学技术大学超级计算中心和生命科学学院生物信息学中心为该项目提供计算资源。

Declaration of generative AI and AI-assisted technologies in the writing process. During the preparation of this work the authors used ChatGPT 3.5 and ChatGPT 4.0 in order to improve the language and readability. After using these tools, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.Author informationAuthor notesThese authors contributed equally: Xuyuan Gao, Ke Liu, Songwen Luo.Authors and AffiliationsDepartment of Oncology, The First Affiliated Hospital of USTC, School of Basic Medical Sciences, Divi.

在写作过程中声明生成人工智能和人工智能辅助技术。在这项工作的准备过程中，作者使用了ChatGPT 3.5和ChatGPT 4.0，以提高语言和可读性。使用这些工具后，作者根据需要对内容进行了审查和编辑，并对出版物的内容承担全部责任。作者信息作者注意到这些作者做出了同样的贡献：高旭元，刘科，罗松文。作者和附属机构中国科学技术大学第一附属医院肿瘤科，基础医学院，Divi。

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PubMed Google ScholarContributionsK.Q. and J. F. conceived the project. X.G., K.L., S.L., S.Z.L., and J.F. designed the framework. X.G. and S.L. performed all the wet-lab experiments with the help of M.T, S.Z.L., Y.H., and C.G.; K.L. and S.L. performed all the bioinformatics analysis with the help of N.L.

PubMed谷歌学术贡献SK。Q、 J.F.构思了这个项目。十、 G.，K.L.，S.L.，S.Z.L。和J.F.设计了框架。X.G.和S.L.在M.T，S.Z.L.，Y.H。和C.G.的帮助下进行了所有湿实验室实验。；K、 L.和S.L.在N.L.的帮助下进行了所有的生物信息学分析。

and C.J.; X.G., K.Q., K.L., C.G., and S.L. wrote the manuscript with inputs from all authors. K.Q. supervised the project.Corresponding authorsCorrespondence to.

和C.J。；十、 G.，K.Q.，K.L.，C.G。和S.L.在所有作者的投入下撰写了手稿。K、 Q.监督项目。通讯作者通讯。

Chuang Guo or Kun Qu.Ethics declarations

创国或昆曲.道德宣言

Competing interests

相互竞争的利益

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

方敬文是汉根生物科技的首席执行官。其他作者声明没有利益冲突。

Peer review

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Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. A peer review file is available.

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Reprints and permissionsAbout this articleCite this articleGao, X., Liu, K., Luo, S. et al. Comparative analysis of methodologies for detecting extrachromosomal circular DNA.

转载和许可本文引用本文Gao，X.，Liu，K.，Luo，S。等人。检测染色体外环状DNA方法的比较分析。

Nat Commun 15, 9208 (2024). https://doi.org/10.1038/s41467-024-53496-8Download citationReceived: 07 December 2023Accepted: 14 October 2024Published: 25 October 2024DOI: https://doi.org/10.1038/s41467-024-53496-8Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

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