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Related Experiment Video

Updated: Oct 28, 2025

A Workflow for the Quantitative Assessment of the Endophytic and Epiphytic Bacterial Microbiomes of the Bark of Populus trichocarpa

A Workflow for the Quantitative Assessment of the Endophytic and Epiphytic Bacterial Microbiomes of the Bark of Populus trichocarpa

Published on: June 27, 2025

Tree-aggregated predictive modeling of microbiome data.

Jacob Bien¹, Xiaohan Yan², Léo Simpson^3,4

¹Department of Data Sciences and Operations, University of Southern California, Los Angeles, CA, USA.

Scientific Reports

|July 16, 2021

Summary

This summary is machine-generated.

This study introduces trac, a novel framework for analyzing microbiome data. It adaptively groups microbial taxa, improving predictive modeling and ecological insights across diverse environments.

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Last Updated: Oct 28, 2025

A Workflow for the Quantitative Assessment of the Endophytic and Epiphytic Bacterial Microbiomes of the Bark of Populus trichocarpa

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Published on: June 27, 2025

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Published on: October 15, 2019

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Area of Science:

Microbiology
Bioinformatics
Computational Biology

Background:

High-throughput sequencing generates vast microbiome data, often sparse and complex.
Traditional analysis methods struggle with rare taxa and require manual aggregation.
Existing workflows often remove rare variants or use fixed taxonomic/phylogenetic groupings.

Purpose of the Study:

To develop a data-driven framework for microbiome data analysis.
To enable scalable and adaptive aggregation of microbial subcompositions.
To associate microbial patterns with specific response variables in complex ecosystems.

Main Methods:

Leveraging the hierarchical structure of amplicon sequencing data.
Proposing a tree-guided aggregation framework named trac (tree-aggregation of compositional data).
Developing a method for data-adaptive taxon aggregation for predictive modeling.

Main Results:

The trac framework learns optimal taxon aggregation levels automatically.
It reduces the need for user-defined aggregation in preprocessing.
Demonstrated versatility in large-scale regression across human gut, soil, and marine microbiomes.

Conclusions:

The trac framework offers a scalable and adaptive approach to microbiome data analysis.
Inferred aggregation levels provide interpretable microbial groupings.
Facilitates deeper insights into ecosystem structure and function.