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bioRxiv · 10.1101/2025.01.30.635649

Predicting coarse-grained representations of biogeochemical cycles from metabarcoding data

Belcour, A.·Megy, L.·Stephant, S.·Michel, C.·Rad, S.·Bombach, P.·Dopffel, N.·de Jong, H.·Ropers, D.
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Abstract

MotivationTaxonomic analysis of environmental microbial communities is now routinely performed thanks to advances in DNA sequencing. Determining the role of these communities in global biogeochemical cycles requires the identification of their metabolic functions, such as hydrogen oxidation, sulfur reduction, and carbon fixation. These functions can be directly inferred from metagenomics data, but in many environmental applications metabarcoding is still the method of choice. The reconstruction of metabolic functions from metabarcoding data and their integration into coarse-grained representations of geobiochemical cycles remains a difficult bioinformatics problem today. ResultsWe developed a pipeline, called Tabigecy, which exploits taxonomic affiliations to predict metabolic functions constituting biogeochemical cycles. In a first step, Tabigecy uses the tool EsMeCaTa to predict consensus proteomes from input affiliations. To optimise this process, we generated a precomputed database containing information about 2,404 taxa from UniProt. The consensus proteomes are searched using bigecyhmm, a newly developed Python package relying on Hidden Markov Models to identify key enzymes involved in metabolic function of biogeochemical cycles. The metabolic functions are then projected on coarse-grained representation of the cycles. We applied Tabigecy to two salt cavern datasets and validated its predictions with microbial activity and hydrochemistry measurements performed on the samples. The results highlight the utility of the approach to investigate the impact of microbial communities on geobiochemical processes.

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Belcour, A., Megy, L., Stephant, S., Michel, C., Rad, S., Bombach, P., Dopffel, N., de Jong, H., Ropers, D.. 2025-02-01. Predicting coarse-grained representations of biogeochemical cycles from metabarcoding data. https://doi.org/10.1101/2025.01.30.635649

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