Abstract
Microbes are found in high abundances in the environment and in human-associated microbiomes, often exceeding one million per milliliter. Viruses of microbes are estimated to turn over 10 to 40 percent of microbes daily and, consequently, are important in shaping microbial communities. Given the relative specificity of viral infection and lysis, it is essential to identify the functional linkages between viruses and their microbial hosts. Multiple time-series analysis methods, including correlation-based approaches, have been proposed to infer infection networks in situ. In this work, we evaluate the effectiveness of correlation-based inference using an in silico approach. In doing so, we compare actual networks to predicted networks as a means to assess the self-consistency of correlation-based inference. Contrary to common use, we find that correlation is a poor indicator of interactions that arise from antagonistic virus-host infections that culminate in lysis. In closing, we discuss alternative inference methods, particularly model-based methods, as a means to predict interactions in complex virus-microbe communities.
Competing interests: The authors have declared that no competing interests exist.
Funding: This work was supported by the Simons Foundation (SCOPE award ID 329108, J.S.W.).