OrthoHMM: Improved Inference of Ortholog Groups using Hidden Markov Models
Jacob Lucas Steenwyk, Thomas J. Buida, Antonis Rokas, Nicole King · bioRxiv (Cold Spring Harbor Laboratory) · 2024
Abstract Accurate orthology inference is essential for comparative genomics and phylogenomics. However, orthology inference is challenged by sequence divergence, which is pronounced among anciently diverged organisms. We present OrthoHMM, an algorithm that infers orthologous gene groups using Hidden Markov Models parameterized from substitution matrices, which enables better detection of remote homologs. Benchmarking indicates OrthoHMM outperforms currently available methods; for example, using a curated set of Bilaterian orthogroups, OrthoHMM showed a 10.3 – 138.9% improvement in precision. Rank-based benchmarking using Bilaterian orthogroups and a novel dataset of orthogroups from organisms in three major eukaryotic kingdoms revealed OrthoHMM had the best overall performance (6.7 – 97.8% overall improvement). These findings suggest that Hidden Markov Models improve orthogroup inference.