Evidence map›Paper›PMID 40661642›Full record

ArticlebioRxiv : the preprint server for biology2025

Biological causes and impacts of rugged tree landscapes in phylodynamic inference.

Jiansi Gao, Marius Brusselmans, Luiz M Carvalho, Marc A Suchard, Guy Baele, Frederick A Matsen

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Jiansi GaoComputational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA 98109.ORCID 0000-0002-5108-828X
Marius BrusselmansDepartment of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium.
Luiz M CarvalhoInstitute of Ecology and Evolution, University of Edinburgh, Edinburgh, United Kingdom.
Marc A SuchardDepartment of Biostatistics, School of Public Health, University of California, Los Angeles, CA 90095.
Guy BaeleDepartment of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium.
Frederick A MatsenComputational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA 98109.

Funding

Fast and flexible Bayesian phylogenetics via modern machine learningR01AI162611 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI MATSEN, FREDERICK ALBERT · 2021 to 2025
$3.8M
Statistical Innovation to Integrate Sequences and Phenotypes for Scalable Phylodynamic InferenceR01AI153044 · NIAID · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Marc A. Suchard · 2021 to 2026
$2.4M
High-Performance Compute Cluster for Comprehensive Cancer and Infectious Diseases ResearchS10OD028685 · OD · FRED HUTCHINSON CANCER RESEARCH CENTER · PI BRADLEY, PHILIP · 2020 to 2020
$2.0M
NIAID NIH HHS R01 AI153044NIAID NIH HHS R01 AI162611NIH HHS S10 OD028685
6 · The paper itself

Abstract

Phylodynamic analysis has been instrumental in elucidating the epidemiological and evolutionary dynamics of pathogens. The Bayesian approach to phylodynamics integrates out phylogenetic uncertainty, which is typically substantial in phylodynamic datasets due to low genetic diversity. Bayesian phylodynamic analysis does not, however, scale with modern datasets, partly due to difficulties in traversing tree space. Here, we set out to characterize tree space of phylodynamic inference and assess its impacts on analysis difficulty and key biological estimates. By running extensive Bayesian analyses of 15 classic large phylodynamic datasets and carefully analyzing the posterior samples, we find that the posterior landscape in tree space ("tree landscape") is diffuse yet rugged, leading to widespread tree sampling problems that usually stem from the sequences in a small part of the tree. We develop clade-specific diagnostics to show that a few sequences-including putative recombinants and recurrent mutants-frequently drive tree space ruggedness and sampling problems, although existing data-quality tests show limited power to detect such sequences. The sampling problems can significantly impact phylodynamic inferences or even distort major biological conclusions; the impact is usually stronger on "local" estimates (

Indexed as

epidemiologyMarkov chain Monte Carlophylodynamicstree spaceviral evolution

Identifiers

PMID40661642
PMCPMC12259096

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.