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ATACAmp: a tool for detecting ecDNA/HSRs from bulk and single-cell ATAC-seq data
BACKGROUND: High oncogene expression in cancer cells is a major cause of rapid tumor progression and drug resistance. Recent cancer genome research has shown that oncogenes as well as regulatory elements can be amplified in the form of extrachromosomal DNA (ecDNA) or subsequently integrated into chr...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10638764/ https://www.ncbi.nlm.nih.gov/pubmed/37950200 http://dx.doi.org/10.1186/s12864-023-09792-6 |
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author | Cheng, Hansen Ma, Wenhao Wang, Kun Chu, Han Bao, Guangchao Liao, Yu Yuan, Yawen Gou, Yixiong Dong, Liting Yang, Jian Cai, Haoyang |
author_facet | Cheng, Hansen Ma, Wenhao Wang, Kun Chu, Han Bao, Guangchao Liao, Yu Yuan, Yawen Gou, Yixiong Dong, Liting Yang, Jian Cai, Haoyang |
author_sort | Cheng, Hansen |
collection | PubMed |
description | BACKGROUND: High oncogene expression in cancer cells is a major cause of rapid tumor progression and drug resistance. Recent cancer genome research has shown that oncogenes as well as regulatory elements can be amplified in the form of extrachromosomal DNA (ecDNA) or subsequently integrated into chromosomes as homogeneously staining regions (HSRs). These genome-level variants lead to the overexpression of the corresponding oncogenes, resulting in poor prognosis. Most existing detection methods identify ecDNA using whole genome sequencing (WGS) data. However, these techniques usually detect many false positive regions owing to chromosomal DNA interference. RESULTS: In the present study, an algorithm called “ATACAmp” that can identify ecDNA/HSRs in tumor genomes using ATAC-seq data has been described. High chromatin accessibility, one of the characteristics of ecDNA, makes ATAC-seq naturally enriched in ecDNA and reduces chromosomal DNA interference. The algorithm was validated using ATAC-seq data from cell lines that have been experimentally determined to contain ecDNA regions. ATACAmp accurately identified the majority of validated ecDNA regions. AmpliconArchitect, the widely used ecDNA detecting tool, was used to detect ecDNA regions based on the WGS data of the same cell lines. Additionally, the Circle-finder software, another tool that utilizes ATAC-seq data, was assessed. The results showed that ATACAmp exhibited higher accuracy than AmpliconArchitect and Circle-finder. Moreover, ATACAmp supported the analysis of single-cell ATAC-seq data, which linked ecDNA to specific cells. CONCLUSIONS: ATACAmp, written in Python, is freely available on GitHub under the MIT license: https://github.com/chsmiss/ATAC-amp. Using ATAC-seq data, ATACAmp offers a novel analytical approach that is distinct from the conventional use of WGS data. Thus, this method has the potential to reduce the cost and technical complexity associated ecDNA analysis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-023-09792-6. |
format | Online Article Text |
id | pubmed-10638764 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106387642023-11-11 ATACAmp: a tool for detecting ecDNA/HSRs from bulk and single-cell ATAC-seq data Cheng, Hansen Ma, Wenhao Wang, Kun Chu, Han Bao, Guangchao Liao, Yu Yuan, Yawen Gou, Yixiong Dong, Liting Yang, Jian Cai, Haoyang BMC Genomics Software BACKGROUND: High oncogene expression in cancer cells is a major cause of rapid tumor progression and drug resistance. Recent cancer genome research has shown that oncogenes as well as regulatory elements can be amplified in the form of extrachromosomal DNA (ecDNA) or subsequently integrated into chromosomes as homogeneously staining regions (HSRs). These genome-level variants lead to the overexpression of the corresponding oncogenes, resulting in poor prognosis. Most existing detection methods identify ecDNA using whole genome sequencing (WGS) data. However, these techniques usually detect many false positive regions owing to chromosomal DNA interference. RESULTS: In the present study, an algorithm called “ATACAmp” that can identify ecDNA/HSRs in tumor genomes using ATAC-seq data has been described. High chromatin accessibility, one of the characteristics of ecDNA, makes ATAC-seq naturally enriched in ecDNA and reduces chromosomal DNA interference. The algorithm was validated using ATAC-seq data from cell lines that have been experimentally determined to contain ecDNA regions. ATACAmp accurately identified the majority of validated ecDNA regions. AmpliconArchitect, the widely used ecDNA detecting tool, was used to detect ecDNA regions based on the WGS data of the same cell lines. Additionally, the Circle-finder software, another tool that utilizes ATAC-seq data, was assessed. The results showed that ATACAmp exhibited higher accuracy than AmpliconArchitect and Circle-finder. Moreover, ATACAmp supported the analysis of single-cell ATAC-seq data, which linked ecDNA to specific cells. CONCLUSIONS: ATACAmp, written in Python, is freely available on GitHub under the MIT license: https://github.com/chsmiss/ATAC-amp. Using ATAC-seq data, ATACAmp offers a novel analytical approach that is distinct from the conventional use of WGS data. Thus, this method has the potential to reduce the cost and technical complexity associated ecDNA analysis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-023-09792-6. BioMed Central 2023-11-10 /pmc/articles/PMC10638764/ /pubmed/37950200 http://dx.doi.org/10.1186/s12864-023-09792-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Cheng, Hansen Ma, Wenhao Wang, Kun Chu, Han Bao, Guangchao Liao, Yu Yuan, Yawen Gou, Yixiong Dong, Liting Yang, Jian Cai, Haoyang ATACAmp: a tool for detecting ecDNA/HSRs from bulk and single-cell ATAC-seq data |
title | ATACAmp: a tool for detecting ecDNA/HSRs from bulk and single-cell ATAC-seq data |
title_full | ATACAmp: a tool for detecting ecDNA/HSRs from bulk and single-cell ATAC-seq data |
title_fullStr | ATACAmp: a tool for detecting ecDNA/HSRs from bulk and single-cell ATAC-seq data |
title_full_unstemmed | ATACAmp: a tool for detecting ecDNA/HSRs from bulk and single-cell ATAC-seq data |
title_short | ATACAmp: a tool for detecting ecDNA/HSRs from bulk and single-cell ATAC-seq data |
title_sort | atacamp: a tool for detecting ecdna/hsrs from bulk and single-cell atac-seq data |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10638764/ https://www.ncbi.nlm.nih.gov/pubmed/37950200 http://dx.doi.org/10.1186/s12864-023-09792-6 |
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