Delphy: scalable, near-real-time Bayesian phylogenetics for outbreaks Article Swipe
Patrick Varilly
,
Mark Schifferli
,
Katherine Yang
,
Timothy Burcham
,
Paul Cronan
,
Olivia Glennon
,
Olivia Jacks
,
Ellory Laning
,
Libby Marrs
,
Kyle Oba
,
Shuk Ying Yeung
,
Edyth Parker
,
Ifeanyi F. Omah
,
Jonathan E. Pekar
,
Laura Luebbert
,
Kristian G. Andersen
,
Daniel J. Park
,
S. F. Schaffner
,
Bronwyn MacInnis
,
Christian Happi
,
Jacob E. Lemieux
,
Al Ozonoff
,
Michael Mitzenmacher
,
Ben Fry
,
Pardis C. Sabeti
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.03.25.645253
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.03.25.645253
Pathogen genomic analysis is central to tracking, understanding, and containing outbreaks, but complexity and high costs of state-of-the-art (SOTA) phylogenetic tools limit global access and impact. We introduce Delphy, an exact reformulation of Bayesian phylogenetics designed to transform its speed, scalability and accessibility while retaining SOTA accuracy. Delphy's central data structure, an Explicit Mutation Annotated Tree, exploits the high sequence similarity in large-scale epidemic datasets for efficient tree exploration and convergence. By reproducing key analyses from recent major epidemics (Ebola, Zika, SARS-CoV-2, mpox, and H5N1), we demonstrate SOTA accuracy with up to 1,000x speedups. Assessing Delphy's scalability, we show that a simulated dataset of 100,000 sequences can be analyzed in under a day—the largest such computation to date. We distribute Delphy as a client-side web application, enabling users worldwide to turn raw data into interactive results within minutes, without the data ever leaving the user's machine. Delphy automatically identifies key viral lineages and mutations, as well as their emergence and prevalence through time, all with quantified uncertainties derived from a solid theoretical foundation. Delphy shows the power of Bayesian phylogenetics as a fast, accessible frontline tool for tackling future outbreaks.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.03.25.645253
- https://www.biorxiv.org/content/biorxiv/early/2025/03/26/2025.03.25.645253.full.pdf
- OA Status
- green
- Cited By
- 5
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408912933
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408912933Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2025.03.25.645253Digital Object Identifier
- Title
-
Delphy: scalable, near-real-time Bayesian phylogenetics for outbreaksWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-03-26Full publication date if available
- Authors
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Patrick Varilly, Mark Schifferli, Katherine Yang, Timothy Burcham, Paul Cronan, Olivia Glennon, Olivia Jacks, Ellory Laning, Libby Marrs, Kyle Oba, Shuk Ying Yeung, Edyth Parker, Ifeanyi F. Omah, Jonathan E. Pekar, Laura Luebbert, Kristian G. Andersen, Daniel J. Park, S. F. Schaffner, Bronwyn MacInnis, Christian Happi, Jacob E. Lemieux, Al Ozonoff, Michael Mitzenmacher, Ben Fry, Pardis C. SabetiList of authors in order
- Landing page
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https://doi.org/10.1101/2025.03.25.645253Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2025/03/26/2025.03.25.645253.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.biorxiv.org/content/biorxiv/early/2025/03/26/2025.03.25.645253.full.pdfDirect OA link when available
- Concepts
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Bayesian probability, Scalability, Computer science, Outbreak, Phylogenetics, Data mining, Econometrics, Geography, Artificial intelligence, Economics, Biology, Virology, Database, Genetics, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
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2025: 5Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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