Inference of population genetic parameters in metagenomics: A clean look at messy data

Metagenomic projects generate short, overlapping fragments of DNA sequence, each deriving from a different individual. We report a new method for inferring the scaled mutation rate, θ = 2N_eu, and the scaled exponential growth rate, R= N_er, from the site-frequency spectrum of these data while accounting for sequencing error via Phred quality scores. After obtaining maximum likelihood parameter estimates for θ and R, we calculate empirical Bayes quality scores reflecting the posterior probability that each apparently polymorphic site is truly polymorphic; these scores can then be used for other applications such as SNP discovery. For realistic parameter ranges, analytic and simulation results show our estimates to be essentially unbiased with tight confidence intervals. In contrast, choosing an arbitrary quality score cutoff (e.g., trimming reads) and ignoring further quality information during inference yields biased estimates with greater variance. We illustrate the use of our technique on a new project analyzing activated sludge from a lab-scale bioreactor seeded by a wastewater treatment plant.