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Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

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Published on: January 7, 2019

Predicting coarse-grained representations of biogeochemical cycles from metabarcoding data.

Arnaud Belcour^1,2, Loris Megy³, Sylvain Stephant⁴

¹Univ. Grenoble Alpes, Inria, 38000 Grenoble, France.

Bioinformatics (Oxford, England)

|July 15, 2025

Summary

This summary is machine-generated.

We developed Tabigecy, a bioinformatics pipeline that predicts microbial metabolic functions from taxonomic data to understand biogeochemical cycles. This tool aids in analyzing environmental samples and their impact on global processes.

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Area of Science:

Microbiology
Bioinformatics
Environmental Science

Background:

Advances in DNA sequencing enable routine taxonomic analysis of microbial communities.
Identifying metabolic functions is crucial for understanding microbial roles in biogeochemical cycles.
Reconstructing metabolic functions from metabarcoding data is a significant bioinformatics challenge.

Purpose of the Study:

To develop a bioinformatics pipeline for predicting metabolic functions from taxonomic affiliations.
To integrate predicted metabolic functions into coarse-grained representations of biogeochemical cycles.
To address the challenge of inferring microbial functions from metabarcoding data.

Main Methods:

Developed the Tabigecy pipeline utilizing taxonomic affiliations to predict metabolic functions.
Employed EsMeCaTa for consensus proteome prediction and a precomputed database of 2404 taxa.
Utilized the bigecyhmm Python package with Hidden Markov Models to identify key enzymes.
Projected metabolic functions onto coarse-grained representations of biogeochemical cycles.

Main Results:

Tabigecy successfully predicts metabolic functions involved in biogeochemical cycles.
Applied the pipeline to salt cavern datasets, validating predictions with experimental measurements.
Demonstrated the utility of the approach for investigating microbial community impacts on biogeochemical processes.

Conclusions:

The Tabigecy pipeline provides a robust method for inferring metabolic functions from taxonomic data.
This approach enhances the understanding of microbial contributions to biogeochemical cycles.
Tabigecy facilitates the integration of metabarcoding data into ecological and biogeochemical studies.