Recent advances in cross-species RNA-seq analysis have enabled the identification of conserved and species-specific features, contributing to both evolutionary biology and biomedical research. However, alignments based solely on RNA expression are limited in their ability to capture regulatory logic. With the emergence of multi-omics data, particularly chromatin accessibility measured by ATAC-seq, it has become possible to investigate upstream regulatory states and transcription factor activity. Integrating RNA and ATAC enables more direct characterization of transcriptional regulatory modules, which are often more conserved than gene expression patterns across species. Despite these advantages, multi-omics integration faces several challenges, including technical heterogeneity and the limited availability of comparable datasets. To address these issues, various methods have been proposed to enable integration across modalities that are not measured in the same cells (diagonal integration). However, these approaches do not adequately model species-specific differences or evolutionary changes in regulatory relationships. In this study, we propose a novel framework, Multi-omics Unified embedding across Species (MUSE), which constructs a cross-species graph by introducing inter-species edges based on orthologous relationships and protein sequence similarity, while preserving the regulatory graph structure within each species. Based on this integrated graph, MUSE aligns omics measurements across species, enabling biologically meaningful cross-species comparisons at both the omics and regulatory module levels.

Co-authors: Shintaro Yuki, Zhenan Liu, Chikara Mizukoshi, Shuto Hayashi, Teppei Shimamura, Hiroshi Yadohisa