{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T19:04:26Z","timestamp":1790881466930,"version":"4.1.0"},"reference-count":0,"publisher":"Cold Spring Harbor Laboratory","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Genome Res."],"accepted":{"date-parts":[[2026,9,24]]},"abstract":"<jats:p>\n                    Summary methods reconstruct species trees from collections of gene trees while accounting for gene tree discordance and provide a statistically consistent framework for phylogenomic inference under the multispecies coalescent model. While existing triplet- and quartet-based approaches such as ASTRAL and STELAR have provable statistical consistency, their running time and memory usage restrict their applicability to ultra-large datasets. We introduce STELAR-X, a statistically consistent and highly scalable triplet-based phylogenetic inference algorithm that achieves an asymptotically optimal memory complexity of\n                    <jats:italic>O(nk)<\/jats:italic>\n                    for\n                    <jats:italic>n<\/jats:italic>\n                    species and\n                    <jats:italic>k<\/jats:italic>\n                    gene trees, essentially matching the input size and allowing analyses to remain feasible as long as the input trees fit in memory, while also substantially reducing running time. STELAR-X achieves this through a compact integer tuple-based encoding of tree bipartitions, efficient precomputation of bipartition weights, and GPU parallelism. These innovations substantially reduce computational overhead in the underlying dynamic programming framework. Experiments demonstrate that STELAR-X achieves unprecedented scalability. On simulated datasets with 10,000 taxa and 1,000 gene trees, STELAR-X runs 3,576\u00d7 faster than ASTRAL-MP (the most scalable variant of ASTRAL) while using 13.9\u00d7 less CPU memory. STELAR-X analyzed a dataset of 100,000 taxa and 1,000 genes in 34.37 minutes using 58.40 GB RAM, and a 100,000-gene dataset with 1000 taxa in just 3.32 minutes using 74.10 GB RAM, scales that were previously intractable for statistically consistent summary methods. Moreover, applying STELAR-X to two large-scale avian datasets produced trees highly consistent with established bird phylogenies, demonstrating its robustness on biological data.\n                  <\/jats:p>","DOI":"10.1101\/gr.282257.126","type":"journal-article","created":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T18:20:17Z","timestamp":1790878817000},"page":"gr.282257.126","source":"Crossref","is-referenced-by-count":0,"title":["Scaling coalescent-based species tree inference to 100,000 taxa with STELAR-X"],"prefix":"10.1101","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5587-6775","authenticated-orcid":false,"given":"Anik","family":"Saha","sequence":"first","affiliation":[{"name":"Bangladesh University of Engineering and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md Shamsuzzoha","family":"Bayzid","sequence":"additional","affiliation":[{"name":"Bangladesh University of Engineering and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"246","published-online":{"date-parts":[[2026,10,1]]},"container-title":["Genome Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1101\/gr.282257.126","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T18:20:17Z","timestamp":1790878817000},"score":1,"resource":{"primary":{"URL":"http:\/\/genome.cshlp.org\/lookup\/doi\/10.1101\/gr.282257.126"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,1]]},"references-count":0,"alternative-id":["10.1101\/gr.282257.126"],"URL":"https:\/\/doi.org\/10.1101\/gr.282257.126","relation":{},"ISSN":["1088-9051"],"issn-type":[{"value":"1088-9051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10,1]]}}}