T. Warnow

Fast and Accurate Species Trees from Weighted Internode Distances

This paper introduces weighted ASTRID, a fast species tree estimation method that improves accuracy by incorporating branch uncertainty from gene trees, outperforming the original ASTRID and competing with weighted ASTRAL.

Scalable Species Tree Inference with External Constraints

This paper introduces two new methods for species tree inference under the multi-species coalescent model that use external constraints and are faster than existing methods.

Comparing Methods for Species Tree Estimation With Gene Duplication and Loss

This study compares three species tree estimation methods (ASTRAL-Pro, ASTRID-multi, and FastMulRFS) under various conditions, finding that ASTRAL-Pro and ASTRID-multi are more reliably accurate than FastMulRFS, with ASTRID-multi being faster.

FastMulRFS: Fast and accurate species tree estimation under generic gene duplication and loss models

FastMulRFS estimates species trees accurately and efficiently under gene duplication and loss models, outperforming existing methods in speed and accuracy.

Polynomial-Time Statistical Estimation of Species Trees under Gene Duplication and Loss

This paper shows that species trees can be statistically estimated under gene duplication and loss, and demonstrates the consistency of the ASTRAL-multi algorithm for this task.

Unblended Disjoint Tree Merging using GTM improves species tree estimation

This paper introduces GTM, a polynomial time method for merging disjoint trees in phylogenomic analysis, which improves species tree estimation accuracy and speed compared to existing methods.

Statistically consistent divide-and-conquer pipelines for phylogeny estimation using NJMerge

This paper introduces NJMerge, a divide-and-conquer method for phylogeny estimation that improves accuracy and reduces runtime compared to traditional methods.

To include or not to include: The impact of gene filtering on species tree estimation methods

This study evaluates how gene filtering affects species tree estimation methods, finding that filtering based on missing data is generally harmful, while filtering based on gene tree error can sometimes improve accuracy under specific conditions.