Karthikeyan Savita, Jeffery Ben, Mbuli-Robertson Duncan, Kelleher Jerome
Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom.
Bioinformatics. 2025 Aug 2;41(8). doi: 10.1093/bioinformatics/btaf393.
Ancestral recombination graphs (ARGs) represent the interwoven paths of genetic ancestry of a set of recombining sequences. The ability to capture the evolutionary history of samples makes ARGs valuable in a wide range of applications in population and statistical genetics. ARG-based approaches are increasingly becoming a part of genetic data analysis pipelines due to breakthroughs enabling ARG inference at biobank-scale. However, there is a lack of visualization tools, which are crucial for validating inferences and generating hypotheses. We present tsbrowse, an open-source, web-based Python application for the interactive visualization of the fundamental building blocks of ARGs, i.e. nodes, edges and mutations. We demonstrate the application of tsbrowse to various data sources and scenarios, and highlight its key features of browsability along the genome, user interactivity, and scalability to very large sample sizes.
Tsbrowse is installed as a Python package from PyPI (https://pypi.org/project/tsbrowse/), while a development version is maintained at https://github.com/tskit-dev/tsbrowse. Documentation is available at https://tskit.dev/tsbrowse/docs/. Source code is archived on Zenodo with DOI, https://doi.org/10.5281/zenodo.15683039.
祖先重组图（ARG）表示一组重组序列的遗传谱系的交织路径。捕获样本进化历史的能力使得ARG在群体遗传学和统计遗传学的广泛应用中具有重要价值。由于在生物样本库规模上实现ARG推断的突破，基于ARG的方法正日益成为遗传数据分析流程的一部分。然而，缺乏可视化工具，而这些工具对于验证推断和生成假设至关重要。我们展示了tsbrowse，这是一个基于网络的开源Python应用程序，用于交互式可视化ARG的基本构建块，即节点、边和突变。我们展示了tsbrowse在各种数据源和场景中的应用，并强调了其在基因组上的可浏览性、用户交互性以及对非常大样本量的可扩展性等关键特性。
tsbrowse作为一个Python包从PyPI（https://pypi.org/project/tsbrowse/）安装，而开发版本在https://github.com/tskit-dev/tsbrowse上维护。文档可在https://tskit.dev/tsbrowse/docs/获取。源代码存档于Zenodo，DOI为https://doi.org/10.5281/zenodo.15683039。
