ASTRAL-X: Scaling Coalescent-Based Species Tree Inference to 300,000 Taxa

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}.