ASTRAL-X: Scaling Coalescent-Based Species Tree Inference to 300,000 Taxa
- 1Bangladesh University of Engineering and Technology
Abstract
Advances in genome sequencing have enabled phylogenomic studies involving tens or even hundreds of thousands of species. However, scalability remains a major computational challenge for statistically consistent species tree inference at this scale. ASTRAL, the most widely used coalescent-based species tree estimator, remains limited by computational and memory bottlenecks that make ultra-large analyses impractical. Here we present ASTRAL-X, a complete algorithmic redesign of the ASTRAL framework that overcomes these computational limitations. By fundamentally redesigning the underlying data representations, algorithms, and computational framework, ASTRAL-X dramatically reduces running time while lowering memory requirements to nearly the size of the input--the asymptotically optimal bound--thereby enabling statistically consistent species tree inference directly from unrooted gene trees at an unprecedented scale. ASTRAL-X preserves ASTRAL's statistical guarantees and achieves accuracy comparable to state-of-the-art methods across simulated and empirical datasets while reconstructing species trees containing 200,000 and 300,000 taxa in only 5 hours and 12 hours, respectively, using modest computational resources. Notably, ASTRAL-X reconstructed the evolutionary history of 9{,}524 angiosperm species in only 16 minutes. These results enable statistically consistent coalescent-based species tree inference at the scale demanded by emerging Tree of Life initiatives. ASTRAL-X is publicly available at \url{https://github.com/aaniksahaa/ASTRAL-X}.
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BibTeX
@misc{saha2026astralx,
title = {{ASTRAL-X: Scaling Coalescent-Based Species Tree Inference to 300,000 Taxa}},
author = {A. Saha and M. S. Bayzid},
howpublished = {bioRxiv},
year = {2026},
month = jul,
doi = {10.64898/2026.07.31.742122},
url = {https://www.biorxiv.org/content/10.64898/2026.07.31.742122v2},
note = {CC BY},
}
RIS
TY - UNPB
TI - ASTRAL-X: Scaling Coalescent-Based Species Tree Inference to 300,000 Taxa
AU - A. Saha
AU - M. S. Bayzid
PY - 2026
DA - 2026/07/31
PB - bioRxiv
DO - 10.64898/2026.07.31.742122
UR - https://www.biorxiv.org/content/10.64898/2026.07.31.742122v2
AB - Advances in genome sequencing have enabled phylogenomic studies involving tens or even hundreds of thousands of species. However, scalability remains a major computational challenge for statistically consistent species tree inference at this scale. ASTRAL, the most widely used coalescent-based species tree estimator, remains limited by computational and memory bottlenecks that make ultra-large analyses impractical. Here we present ASTRAL-X, a complete algorithmic redesign of the ASTRAL framework that overcomes these computational limitations. By fundamentally redesigning the underlying data representations, algorithms, and computational framework, ASTRAL-X dramatically reduces running time while lowering memory requirements to nearly the size of the input--the asymptotically optimal bound--thereby enabling statistically consistent species tree inference directly from unrooted gene trees at an unprecedented scale. ASTRAL-X preserves ASTRAL's statistical guarantees and achieves accuracy comparable to state-of-the-art methods across simulated and empirical datasets while reconstructing species trees containing 200,000 and 300,000 taxa in only 5 hours and 12 hours, respectively, using modest computational resources. Notably, ASTRAL-X reconstructed the evolutionary history of 9{,}524 angiosperm species in only 16 minutes. These results enable statistically consistent coalescent-based species tree inference at the scale demanded by emerging Tree of Life initiatives. ASTRAL-X is publicly available at \url{https://github.com/aaniksahaa/ASTRAL-X}.
ER -