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StripePy:对结构条纹进行快速且稳健的特征描述。

StripePy: fast and robust characterization of architectural stripes.

作者信息

Raffo Andrea, Rossini Roberto, Paulsen Jonas

机构信息

Department of Biosciences, University of Oslo, Oslo 0316, Norway.

出版信息

Bioinformatics. 2025 Jun 2;41(6). doi: 10.1093/bioinformatics/btaf351.

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.

摘要

动机

Hi-C及相关数据中的结构条纹对于基因调控、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却很少。

结果

我们引入了StripePy,它利用计算几何方法来识别和分析来自诸如Hi-C和Micro-C等染色体构象捕获实验的接触图中的结构条纹。通过在各种数据集和新开发的模拟基准StripeBench上的测试表明,StripePy优于现有工具,为社区提供了宝贵的资源。

可用性和实现方式

StripePy作为一个开源的、遵循MIT许可的Python应用程序向公众发布。StripePy源代码托管在GitHub上,网址为https://github.com/paulsengroup/StripePy,并在Zenodo上存档。可以使用pip从源代码或PyPI轻松安装StripePy,也可以使用conda从Bioconda安装。StripePy的容器化版本定期发布在DockerHub上。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8011/12215313/3aa4d2c9a84e/btaf351f1.jpg

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