Summary While the cost of sequencing genomes has decreased dramatically in recent years, this expense often remains nontrivial. Under a fixed budget, scientists face a natural trade-off between quantity and quality: spending resources to sequence a greater number of genomes or spending resources to sequence genomes with increased accuracy. 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. 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. We validate our method on cancer and human genomics data.

中文翻译：

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事半功倍：通过贝叶斯非参数预测和最大化基因组变异发现

总结 尽管近年来基因组测序的成本已大幅下降，但这笔费用通常仍然不小。在固定预算下，科学家面临数量和质量之间的自然权衡：花费资源对更多的基因组进行测序，或者花费资源对基因组进行更准确的测序。我们的目标是在数量和质量之间找到资源的最优配置。优化资源分配有望揭示基因组中尽可能多的新变异。我们引入了贝叶斯非参数方法来预测基于试点研究的后续研究中新变体的数量。当实验条件在试点和后续行动之间保持不变时，我们发现我们的预测与现有的最佳方法具有竞争力。然而，与目前的方法不同的是，我们的新方法允许从业者在试点和后续行动之间改变实验条件。我们展示了这种区别如何使我们的方法能够用于更现实的预测以及在质量和数量之间实现固定预算的最佳分配。我们在癌症和人类基因组学数据上验证了我们的方法。