Evidence map›Paper›PMID 40650922›Full record

ArticleBioinformatics (Oxford, England)2025

Tsbrowse: an interactive browser for ancestral recombination graphs.

Savita Karthikeyan, Ben Jeffery, Duncan Mbuli-Robertson, Jerome Kelleher

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. A Pandemic-Scale Ancestral Recombination Graph for SARS-CoV-2.bioRxiv : the preprint server for biology · 2025
    Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Savita KarthikeyanBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom.ORCID 0000-0002-4798-5746
Ben JefferyBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom.ORCID 0000-0002-1982-6801
Duncan Mbuli-RobertsonBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom.ORCID 0000-0002-1660-2415
Jerome KelleherBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom.ORCID 0000-0002-7894-5253

Funding

Scaling up computational genomics with tree sequencesR01HG012473 · NHGRI · UNIVERSITY OF OREGON · PI PETER Lochhead RALPH · 2023 to 2026
$2.3M
Scaling up computational genomics with tree sequencesR56HG011395 · NHGRI · UNIVERSITY OF OREGON · PI RALPH, PETER LOCHHEAD · 2021 to 2021
$557k
NHGRI NIH HHS R01 HG012473NHGRI NIH HHS R56 HG011395Novo Nordisk Research Centre Oxford and the Biotechnology and Biological Sciences Research CouncilRobertson FoundationWellcome Programme in Genomic Medicine and Statistics. J.K. acknowledges EPSRC EP/X024881/1Wellcome Programme in Genomic Medicine and Statistics. J.K. acknowledges EPSRC HG011395Wellcome Programme in Genomic Medicine and Statistics. J.K. acknowledges EPSRC HG012473Wellcome Trust
6 · The paper itself

Abstract

summaryAncestral 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.

Indexed as

Recombination, GeneticSoftwareComputer GraphicsEvolution, MolecularGenomicsHumans

Identifiers

PMID40650922
PMCPMC12342996

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.