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delphy

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Fast, scalable, accurate and accessible Bayesian phylogenetics

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Delphy
======

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[![codecov](https://codecov.io/gh/broadinstitute/delphy/graph/badge.svg)](https://codecov.io/gh/broadinstitute/delphy)
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[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.11922813.svg)](https://doi.org/10.5281/zenodo.11922813)

Delphy is a fast, scalable, accurate and accessible tool for Bayesian phylogenetics based on Explicit Mutation-Annotated
Trees (EMATs).  EMATs are an extension of the Mutation-Annotated Trees (MATs) introduced by
[UShER](https://github.com/yatisht/usher) where nodes have explicit times, mutations are represented as explicit timed
events along branches, and missing data is explicitly represented.  EMATs open substantial simplifications and scaling
opportunities in the calculations powering Bayesian phylogenetics, at the cost of some statistical efficiency with
respect to the traditional calculations based on Felsenstein pruning.  For genomic epidemiology datasets, where the
total number of mutations on a tree is comparable to the number of samples, this is a very favorable trade-off.

These sources comprise the "core" computational engine of Delphy.  The web application that allows users to immediately
and intuitively use Delphy (currently at [https://delphy.bio](https://delphy.bio)) is developed in
collaboration with [Fathom Information Design](https://fathom.info), and its separately licensed sources are hosted
[here](https://github.com/fathominfo/delphy-web).

**NOTE**: As of 17 Dec 2025, we have relicensed Delphy under an open-source [MIT license](LICENSE), and we have withdrawn our
patent application, instead dedicating the relevant IP to the public domain.

References
----------

* Preprint - [Delphy: scalable, near-real-time Bayesian phylogenetics for outbreaks](https://www.biorxiv.org/content/10.1101/2025.03.25.645253v1).  See [here](https://github.com/broadinstitute/delphy-2025-paper-data) for accompanying data and scripts.

* [Whitepaper with overview of key ideas and accuracy+speed benchmarks](delphywp.pdf)

Installation
------------
Pre-built Delphy binaries are available for Linux (x86_64 and ARM64) and macOS (ARM64) via [GitHub Releases](https://github.com/broadinstitute/delphy/releases), and as multi-arch Docker images from [GitHub Container Registry](https://github.com/broadinstitute/delphy/pkgs/container/delphy).  Delphy can also be compiled from source as a native command-line program or as a WebAssembly bundle for [`delphy-web`](https://github.com/fathominfo/delphy-web).  See [`INSTALL.md`](INSTALL.md) for all installation options, including building from source.  Delphy does not require special hardware, such as a GPU.

Delphy was developed and is primarily tested under Linux (Ubuntu 22.04.4 LTS and Ubuntu 24.04 LTS, x86-64).  [Delphy-web](https://github.com/fathominfo/delphy-web), deployed at [https://delphy.bio](https://delphy.bio), should work in any modern web browser; see the [Delphy-web README.md](https://github.com/fathominfo/delphy-web) for specific browsers and operating systems on which it has been tested.

Google Colab Tutorials
----------------------
Ready-to-use Google Colab tutorials for downloading and formatting sequencing data from the [NCBI Virus database](https://www.ncbi.nlm.nih.gov/labs/virus/vssi/#/), as well as user-provided data, can be accessed [here](https://colab.research.google.com/github/broadinstitute/delphy/blob/main/tutorials/delphy_workflow.ipynb) (blank notebook) and [here](https://colab.research.google.com/github/broadinstitute/delphy/blob/main/tutorials/delphy_workflow_example.ipynb) (Ebola example).  These notebooks streamline the uniform formatting of sequencing data and the associated metadata for input into Delphy, and then run Delphy using a pre-compiled binary.  The output files can be visualized on the [Delphy web interface](https://delphy.bio).

Credits and Acknowledgements
----------------------------

Delphy is developed in the [Sabeti Lab](https://www.sabetilab.org/) at the [Broad
Institute](https://www.broadinstitute.org/).

Delphy draws a lot of inspiration from:

- [UShER and matOptimize](https://github.com/yatisht/usher)
- [BEAST](https://github.com/beast-dev/beast-mcmc)
- [BEAST2](https://github.com/CompEvol/beast2)
- [MAPLE](https://github.com/NicolaDM/MAPLE)
- [IQ-TREE2](github.com/iqtree/iqtree2/)

delphy

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