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B0594
Title: More for less: Predicting and maximizing genetic variant discovery via bayesian nonparametrics Authors:  Tamara Broderick - MIT (United States)
Stefano Favaro - University of Torino and Collegio Carlo Alberto (Italy)
Federico Camerlenghi - University of Milano-Bicocca and Collegio Carlo Alberto (Italy)
Lorenzo Masoero - MIT (United States) [presenting]
Abstract: 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: spending resources to sequence a greater number of genomes (quantity) or spending 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. We introduce a Bayesian nonparametric methodology to predict the number of new variants in a follow-up study based on a pilot study. We validate our method on cancer and human genomics data. When experimental conditions are kept constant between the pilot and follow-up, we find that our prediction is competitive with the best existing methods. Unlike current methods, though, our new 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 more realistic predictions and for optimal allocation of a fixed budget between quality and quantity.