Publications

Probabilities of unranked and ranked anomaly zones under birth-death models

Anastasiia Kim, Noah Rosenberg, and James Degnan
Molecular Biology and Evolution, 2020
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In this paper, we study how the parameters of a species tree simulated under a constant rate birth-death process can affect the probability that the species tree lies in the anomaly zone. We derive the lower bound of the probability of the species tree being in an unranked anomaly zone with n leaves for large speciation rate $\lambda$, and we show that this lower bound approaches 1 as n $\rightarrow \infty$ and $\lambda \rightarrow \infty$.

PRANC: ML species tree estimation from the ranked gene trees under coalescence

Anastasiia Kim and James Degnan
Bioinformatics, 2020
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PRANC is a computational framework to work with the ranked gene trees. PRANC performs a heuristic search from the initial trees to find a ML species tree.

Heuristics for unrooted, unranked, and ranked anomaly zones under birth-death models

Anastasiia Kim and James Degnan
Molecular Phylogenetics and Evolution (under review), 2020

In this article, we introduce several heuristic approaches to infer whether species trees are in anomaly zones when it is difficult or impossible to compute the entire distribution of gene tree topologies.