Method

Accurate and robust inference of microbial growth dynamics from metagenomic sequencing reveals personalized growth rates

    • 1Department of Computer Science, Columbia University, New York, New York 10027, USA;
    • 2Department of Systems Biology, Columbia University Irving Medical Center, New York, New York 10032, SA;
    • 3Department of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, New York 10032, SA;
    • 4CIFAR Azrieli Global Scholars Program, CIFAR, Toronto, Ontario M5G 1M1, Canada;
    • 5Data Science Institute, Columbia University, New York, New York 10027, USA
    • 6 These authors contributed equally to this work.
Published January 5, 2022. Vol 32 Issue 3, pp. 558-568. https://doi.org/10.1101/gr.275533.121
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Abstract

Patterns of sequencing coverage along a bacterial genome—summarized by a peak-to-trough ratio (PTR)—have been shown to accurately reflect microbial growth rates, revealing a new facet of microbial dynamics and host–microbe interactions. Here, we introduce Compute PTR (CoPTR): a tool for computing PTRs from complete reference genomes and assemblies. Using simulations and data from growth experiments in simple and complex communities, we show that CoPTR is more accurate than the current state of the art while also providing more PTR estimates overall. We further develop a theory formalizing a biological interpretation for PTRs. Using a reference database of 2935 species, we applied CoPTR to a case-control study of 1304 metagenomic samples from 106 individuals with inflammatory bowel disease. We show that growth rates are personalized, are only loosely correlated with relative abundances, and are associated with disease status. We conclude by showing how PTRs can be combined with relative abundances and metabolomics to investigate their effect on the microbiome.

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