H. Ogilvie

"Correcting'' gene trees to be more like species trees increases topological error when incomplete lineage sorting is high

This paper shows that gene tree correction methods can increase topological error when incomplete lineage sorting is high, suggesting that correction should consider the species phylogeny.

MaCroDNA: Accurate integration of single-cell DNA and RNA data for a deeper understanding of tumor heterogeneity

MaCroDNA integrates single-cell DNA and RNA data to better understand tumor heterogeneity, showing improved accuracy and speed over existing methods.

MoTERNN: Classifying the Mode of Cancer Evolution Using Recursive Neural Networks

This paper introduces MoTERNN, a recursive neural network that classifies cancer evolution modes from phylogenetic trees into four categories using simulated and real single-cell DNA sequencing data.

Phylovar: Towards scalable phylogeny-aware inference of single-nucleotide variations from single-cell DNA sequencing data

Phylovar improves scalability and accuracy of SNV detection in scDNAseq data by leveraging phylogenetic information, outperforming existing methods in speed and precision.

Supporting per-locus substitution rates improves the accuracy of species networks and avoids spurious reticulations

The paper improves species network accuracy by supporting per-locus substitution rates, reducing spurious reticulations in phylogenetic inference.

NestedBD: Bayesian Inference of Phylogenetic Trees From Single-Cell DNA Copy Number Profile Data Under a Birth-Death Model

This paper introduces NestedBD, a Bayesian method for inferring phylogenetic trees from single-cell DNA copy number data under a birth-death model, showing improved accuracy over existing methods.

Joint inference of species histories and gene flow

The paper introduces a method to infer species trees while accounting for gene flow, showing improved accuracy in species history reconstruction for mosquitoes.

Unifying Gene Duplication, Loss, and Coalescence on Phylogenetic Networks

This paper introduces a unified model for gene evolution incorporating ILS, GDL, and introgression, and develops an algorithm to estimate phylogenetic network parameters.

A Divide-and-Conquer Method for Scalable Phylogenetic Network Inference from Multi-locus Data

This paper introduces a scalable method for inferring phylogenetic networks from multi-locus data, achieving accurate results at a larger scale than existing methods.