MT-Tracker: A Phylogeny-Aware Algorithm for Quantifying Microbiome Transitions Across Scales and Habitats

Wenjie Zhu, Yangyang Sun, Weiwen Luo, Guosen Hou, Hao Gao and Xiaoquan Su ()
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Wenjie Zhu: College of Computer Science and Technology, Qingdao University, Qingdao 266071, China
Yangyang Sun: College of Computer Science and Technology, Qingdao University, Qingdao 266071, China
Weiwen Luo: College of Computer Science and Technology, Qingdao University, Qingdao 266071, China
Guosen Hou: College of Computer Science and Technology, Qingdao University, Qingdao 266071, China
Hao Gao: College of Computer Science and Technology, Qingdao University, Qingdao 266071, China
Xiaoquan Su: College of Computer Science and Technology, Qingdao University, Qingdao 266071, China

Mathematics, 2025, vol. 13, issue 12, 1-17

Abstract: The structural diversity of microbial communities plays a pivotal role in microbiological research and applications. However, the study of microbial transitions has remained challenging due to a lack of effective methods, limiting our understanding of microbial dynamics and their underlying mechanisms. To address this gap, we introduce MT-tracker (microbiome transition tracker), a novel algorithm designed to capture the transitional trajectories of microbial communities. Grounded in diversity and phylogenetic principles, MT-tracker reconstructs the virtual common ancestors of microbiomes at the community level. By calculating distances between microbiomes and their ancestors, MT-tracker deduces their transitional directions and probabilities, achieving a substantial speed advantage over conventional approaches. The accuracy and robustness of MT-tracker were first validated by a phylosymbiosis analysis using samples from 28 mammals and 24 nonmammal animals, describing the co-evolutionary pattern between hosts and their associated microbiomes. We then expanded the usage of MT-tracker to 456,702 microbiomes sampled world-wide, uncovering the global transitional directions among 21 ecosystems for the first time. This effort provides new insights into the macro-scale dynamic patterns of microbial communities. Additionally, MT-tracker revealed intricate longitudinal transition trends in human microbiomes over a sampling period exceeding 400 days, capturing temporal dynamics often overlooked by normal diversity analyses. In summary, MT-tracker offers robust support for the qualitative and quantitative analysis of microbial community diversity, offering significant potential for studying and utilizing the macrobiome variation.

Keywords: microbiome transition; microbiome dynamics; MT-tracker algorithm; microbial diversity (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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