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bioRxiv · 10.1101/2023.10.11.561816

Panera: A novel framework for surmounting uncertainty in microbial community modelling using Pan-genera metabolic models

Raman, K.·Palanikumar, I.·Sinha, H.
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Abstract

Over the last decade, microbiome research has witnessed exponential growth, largely driven by the widespread availability of metagenomic data. Despite this influx of data, 16S targeted amplicon sequencing, which offers relatively lower resolution, still dominates the landscape over whole-genome shotgun sequencing. Existing algorithms for constructing metabolic models of microbial communities primarily rely on whole-genome sequences and do not fully harness the potential of 16S datasets. In this study, we report Panera, a novel framework designed to model microbial communities under uncertainty and yet perform inferences by building pan-genus metabolic models. We tested the performance of the models from our approach by analysing their ability to capture the functionality of the entire genus and individual species within a genus. We further exercise the model to explore the comprehensive metabolic abilities of a genus, shedding light on metabolic commonalities between microbial groups. Furthermore, we showcase its application in characterising microbial community models using 16S data. Our hybrid community models, which combine both GSMM and pan-genus metabolic models, exhibit a 10% reduction in prediction error, with error rates diminishing as community size increases. Overall, the Panera framework represents a potent and effective approach for metabolic modelling, enabling robust predictions of the metabolic phenotypes of microbial communities, even when working with limited 16S data. This advancement has the potential to greatly impact the field of microbiome research, offering new insights into the metabolic dynamics of diverse microbial ecosystems.

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Raman, K., Palanikumar, I., Sinha, H.. 2023-10-13. Panera: A novel framework for surmounting uncertainty in microbial community modelling using Pan-genera metabolic models. https://doi.org/10.1101/2023.10.11.561816

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