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Development of I-SVVS, a novel analytical method for integrating and classifying microbial community and metabolite data

May 30, 2025

A specialized tool for analyzing high-dimensional microbiome data has now arrived!

Researchers from the University of Tokyo, the RIKEN BioResource Research Center, and the RIKEN CSRS have developed a new analytical method called Integrated Stochastic Variational Variable Selection (I-SVVS). This method integrates microbiome and metabolome data from habitats close to a host, such as the rhizosphere soil and the intestine of animals, and performs clustering to resolve their relationships. I-SVVS is expected to become a valuable tool for agricultural, medical, and environmental sciences by unveiling the mechanisms of microbial and metabolic interactions.

To understand the functions and interactions of microbial communities, it is necessary to accurately analyze multi-omic microbiome data that integrates various omics technologies, such as metagenomes and metabolomes. However, these datasets are large and complex, with low density for each data point and strong correlations among variables. Therefore, the development of advanced computational methods was required. I-SVVS utilizes a Bayesian nonparametric approach to enable automatic determination of the optimal number of clusters and accurate classification based on the characteristics of the data. This method is expected to advance our understanding of microbial communities and their metabolic interactions across a wide range of host organisms.

 

Original article
Briefings in Bioinformatics doi: 10.1093/bib/bbaf132
T. Dang, Y. Fuji, K. Kumaishi, E. Usui, S. Kobori, T. Sato, M. Narukawa, Y. Toda, K. Sakurai, Y. Yamasaki, H. Tsujimoto, M. Yokota Hirai, Y. Ichihashi, H. Iwata,
"I-SVVS: Integrative stochastic variational variable selection to explore joint patterns of multi-omics microbiome data".
Contact
Yasunori Ichihashi; Team Director
Holobiont and Resilience Research Team
Masami Hirai; Team Director
Yushiro Fuji; Research Scientist
Metabolic Systems Research Team