Gavin A. Huttley

This study introduces CLOAK, an MSA cleaning algorithm that improves gene tree inference by removing alignment errors while preserving informative residues.

piqtree provides a Python interface to IQ-TREE for phylogenetic inference, offering seamless integration with Python tools and workflows.

The paper introduces methods to detect neutral mutation disequilibrium (NMD) and applies them to Drosophila, mouse, and human genomes, finding NMD is common and may mislead evolutionary inferences.

diverse-seq provides alignment-free methods to select and cluster biological sequences, improving computational efficiency for phylogenetic and other workflows.

The paper introduces a new supertree method, Spectral Cluster Supertree, which efficiently merges rooted phylogenetic trees with improved accuracy and speed compared to existing methods.

This paper introduces the Inherited Rate Matrix (IRM) algorithm for non-stationary phylogenetic model selection, showing improved accuracy and speed over existing methods.

This paper introduces a Bayesian method to infer coalescence times from the site frequency spectrum, applied to simulated and historical mitochondrial data.

This paper introduces a new likelihood-based test for natural selection, called rho, which outperforms Tajima's D in detecting selection signals and adapts to demographic histories and sequencing errors.

The paper quantifies how recombination and sequence context influence human genome mutation rates and finds context effects are significant but overshadowed by base-pair interactions.

This paper uses machine learning to classify the mutagenic origins of point mutations by analyzing sequence context, showing that neighboring bases can distinguish between ENU-induced and spontaneous mutations.

This paper shows that incorporating species abundance data improves taxonomy classification accuracy for amplicon sequences.

The paper benchmarks supervised learning methods for microbiome classification and regression, introducing a QIIME 2 plugin to apply these methods to microbiome data.