QR-STAR: A polynomial-time statistically consistent method for rooting species trees under the coalescent

We address the problem of rooting an unrooted species tree given a set of unrooted gene trees, under the assumption that gene trees evolve within the model species tree under the multispecies coalescent (MSC) model. Quintet Rooting (QR) is a polynomial-time algorithm that was recently proposed for this problem, which is based on the theory developed by Allman, Degnan, and Rhodes that proves the identifiability of rooted 5-taxon trees from unrooted gene trees under the MSC. However, although QR had good accuracy in simulations, its statistical consistency was left as an open problem. We present QR-STAR, a variant of QR with an additional step and a different cost function, and prove that it is statistically consistent under the MSC. Moreover, we derive sample complexity bounds for QR-STAR and show that a particular variant of it based on ‘‘short quintets’’ has polynomial sample complexity. Finally, our simulation study under a variety of model conditions shows that QR-STAR matches or imp..., , # Data from: QR-STAR: A polynomial-time statistically consistent method for rooting species trees under the coalescent

This repository includes the datasets and scripts used in the following papers:

* Tabatabaee, Y., Roch, S., and Warnow, T. (2023). Statistically consistent rooting of species trees under the multispecies coalescent model. In International Conference on Research in Computational Molecular Biology (RECOMB 2023) (pp. 41-57). [https://doi.org/10.1101/2022.10.26.513897](https://doi.org/10.1101/2022.10.26.513897)
* Tabatabaee, Y., Roch, S., and Warnow, T. (2023) QR-STAR: A Polynomial-Time Statistically Consistent Method for Rooting Species Trees Under the Coalescent. Journal of Computational Biology, 30(11), pp.1146-1181. [https://www.liebertpub.com/doi/10.1089/cmb.2023.0185](https://www.liebertpub.com/doi/10.1089/cmb.2023.0185)

For experiments in this study, we studied a collection of simulated datasets with incomplete lineage sorting (ILS). We used the 100- and 200-taxon...,