GeoPhy: Differentiable Phylogenetic Inference via Geometric Gradients of Tree Topologies

Phylogenetic inference, grounded in molecular evolution models, is essential for understanding the evolutionary relationships in biological data. Accounting for the uncertainty of phylogenetic tree variables, which include tree topologies and evolutionary distances on branches, is crucial for accurately inferring species relationships from molecular data and tasks requiring variable marginalization. Variational Bayesian methods are key to developing scalable, practical models; however, it remains challenging to conduct phylogenetic inference without restricting the combinatorially vast number of possible tree topologies. In this work, we introduce a novel, fully differentiable formulation of phylogenetic inference that leverages a unique representation of topological distributions in continuous geometric spaces. Through practical considerations on design spaces and control variates for gradient estimations, our approach, GeoPhy, enables variational inference without limiting the topological candidates. In experiments using real benchmark datasets, GeoPhy significantly outperformed other approximate Bayesian methods that considered whole topologies.Comment: 23 pages, 5 figure

Novel metric for hyperbolic phylogenetic tree embeddings

Advances in experimental technologies, such as DNA sequencing, have opened up new avenues for the applications of phylogenetic methods to various fields beyond their traditional application in evolutionary investigations, extending to the fields of development, differentiation, cancer genomics, and immunogenomics. Thus, the importance of phylogenetic methods is increasingly being recognized, and the development of a novel phylogenetic approach can contribute to several areas of research. Recently, the use of hyperbolic geometry has attracted attention in artificial intelligence research. Hyperbolic space can better represent a hierarchical structure compared to Euclidean space, and can therefore be useful for describing and analyzing a phylogenetic tree. In this study, we developed a novel metric that considers the characteristics of a phylogenetic tree for representation in hyperbolic space. We compared the performance of the proposed hyperbolic embeddings, general hyperbolic embeddings, and Euclidean embeddings, and confirmed that our method could be used to more precisely reconstruct evolutionary distance. We also demonstrate that our approach is useful for predicting the nearest-neighbor node in a partial phylogenetic tree with missing nodes. Furthermore, we proposed a novel approach based on our metric to integrate multiple trees for analyzing tree nodes or imputing missing distances. This study highlights the utility of adopting a geometric approach for further advancing the applications of phylogenetic methods

HLA-VBSeq v2: improved HLA calling accuracy with full-length Japanese class-I panel

HLA-VBSeq is an HLA calling tool developed to infer the most likely HLA types from high-throughput sequencing data. However, there is still room for improvement in specific genetic groups because of the diversity of HLA alleles in human populations. Here, we present HLA-VBSeq v2, a software application that makes use of a new Japanese HLA reference panel to enhance calling accuracy for Japanese HLA class-I genes. Our analysis showed significant improvements in calling accuracy in all HLA regions, with prediction accuracies achieving over 99.0, 97.8, and 99.8% in HLA-A, B and C, respectively

Serological and Progression Differences of Joint Destruction in the Wrist and the Feet in Rheumatoid Arthritis - A Cross-Sectional Cohort Study - Fig 2

<p>(A) Comparison of joint destruction of the wrist and the feet in the duration of the disease. Larsen grade of the feet was significantly higher than that of the wrist in the first subgroup (p<0.001). (B) Comparison of difference of the joint destruction between the wrist and the feet in Larsen grade. <i>P</i> < 0.001.</p