VINE: Variational inference for scalable Bayesian reconstruction of species and cell-lineage phylogenies

Siepel, Adam, Hassett, Rebecca, Staklinski, Stephen J (March 2026) VINE: Variational inference for scalable Bayesian reconstruction of species and cell-lineage phylogenies. bioRxiv. ISSN 2692-8205 (Submitted)

PDF 10.64898.2025.12.24.696405.pdf - Submitted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (21MB)

Abstract

Bayesian methods are now widely used in reconstructing both species and cell-lineage phylogenies, but they remain heavily reliant on computationally intensive Markov chain Monte Carlo sampling. Phylogenetic variational inference (VI) circumvents this dependency but so far has been limited in speed and scalability. Here we introduce Variational Inference with Node Embeddings (V ine ), a computational method that combines an embedding of taxa in a high-dimensional space and a distance-based "decoder" with several algorithmic innovations to dramatically improve phylogenetic VI. V ine supports both standard DNA substitution models and CRISPR barcode-mutation models for inference of cell-lineage trees and tissue-migration histories. In extensive simulation experiments, we show that V ine is comparable in accuracy to the best available Bayesian methods with speeds orders of magnitude faster. We then apply V ine to ∼1,000 complete SARS-CoV-2 genomes and ∼900 lung-cancer cell barcodes, showing reductions in compute time from days to hours or minutes.

Item Type:	Paper
Subjects:	bioinformatics bioinformatics > quantitative biology bioinformatics > computational biology
CSHL Authors:	Siepel, Adam Hassett, Rebecca Staklinski, Stephen
Communities:	CSHL labs > Siepel lab School of Biological Sciences > Publications
SWORD Depositor:	CSHL Elements
Depositing User:	CSHL Elements
Date:	23 March 2026
Date Deposited:	06 Apr 2026 12:10
Last Modified:	06 Apr 2026 12:10
PMCID:	PMC13042005
Related URLs:	Publisher
URI:	https://repository.cshl.edu/id/eprint/42144

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