  While the cost of sequencing genomes has decreased dramatically in recent
years, this expense often remains non-trivial. Under a fixed budget, then,
scientists face a natural trade-off between quantity and quality; they can
spend resources to sequence a greater number of genomes (quantity) or spend
resources to sequence genomes with increased accuracy (quality). Our goal is to
find the optimal allocation of resources between quantity and quality.
Optimizing resource allocation promises to reveal as many new variations in the
genome as possible, and thus as many new scientific insights as possible. In
this paper, we consider the common setting where scientists have already
conducted a pilot study to reveal variants in a genome and are contemplating a
follow-up study. We introduce a Bayesian nonparametric methodology to predict
the number of new variants in the follow-up study based on the pilot study.
When experimental conditions are kept constant between the pilot and follow-up,
we demonstrate on real data from the gnomAD project that our prediction is more
accurate than three recent proposals, and competitive with a more classic
proposal. Unlike existing methods, though, our method allows practitioners to
change experimental conditions between the pilot and the follow-up. We
demonstrate how this distinction allows our method to be used for (i) more
realistic predictions and (ii) optimal allocation of a fixed budget between
quality and quantity.
