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Tsbrowse: an interactive browser for ancestral recombination graphs

Abstract:
Summary: 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. Availability and implementation: 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.
Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Department:
Big Data Institute
Role:
Author
ORCID:
0000-0002-4798-5746
More by this author
Institution:
University of Oxford
Department:
Big Data Institute
Role:
Author
ORCID:
0000-0002-1982-6801
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Institution:
University of Oxford
Department:
Big Data Institute
Role:
Author
ORCID:
0000-0002-1660-2415
More by this author
Institution:
University of Oxford
Department:
Big Data Institute
Role:
Author
ORCID:
0000-0002-7894-5253


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Funder identifier:
https://ror.org/04h2x1077


Publisher:
Oxford University Press
Journal:
Bioinformatics More from this journal
Volume:
41
Issue:
8
Article number:
btaf393
Publication date:
2025-07-12
Acceptance date:
2025-07-08
DOI:
EISSN:
1367-4811


Language:
English
Source identifiers:
3196530
Deposit date:
2025-08-13
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