StripePy: fast and robust characterization of architectural stripes.

Bioinformatics (Oxford, England) Pub Date : 2025-06-13 DOI:10.1093/bioinformatics/btaf351

Andrea Raffo, Roberto Rossini, Jonas Paulsen

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引用次数: 0

Abstract

Motivation: Architectural stripes in Hi-C and related data are crucial for gene regulation, development, and DNA repair. Despite their importance, few tools exist for automatic stripe detection.

Results: We introduce StripePy, which leverages computational geometry methods to identify and analyze architectural stripes in contact maps from Chromosome Conformation Capture experiments like Hi-C and Micro-C. StripePy outperforms existing tools, as shown through tests on various datasets and a newly developed simulated benchmark, StripeBench, providing a valuable resource for the community.

Availability and implementation: StripePy is released to the public as an open source, MIT-licensed Python application. StripePy source code is hosted on GitHub at https://github.com/paulsengroup/StripePy and is archived on Zenodo. StripePy can be easily installed from source or PyPI using pip and from Bioconda using conda. Containerized versions of StripePy are regularly published on DockerHub.

Supplementary information: Supplementary data are provided as a separate file.

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StripePy：快速和健壮的建筑条纹特征。

动机：Hi-C的结构条纹及其相关数据对基因调控、发育和DNA修复至关重要。尽管它们很重要，但用于自动条纹检测的工具很少。结果：我们引入了StripePy，它利用计算几何方法来识别和分析染色体构象捕获实验（如Hi-C和Micro-C）的接触图中的建筑条纹。通过对各种数据集和新开发的模拟基准StripeBench的测试，StripePy优于现有工具，为社区提供了宝贵的资源。可用性和实现：StripePy作为麻省理工学院许可的开源Python应用程序向公众发布。StripePy源代码托管在GitHub https://github.com/paulsengroup/StripePy上，并存档在Zenodo上。StripePy可以很容易地从源代码或PyPI使用pip和从Bioconda使用conda安装。StripePy的容器化版本定期在DockerHub上发布。补充信息：补充数据以单独文件形式提供。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Bioinformatics (Oxford, England)

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