More for less: predicting and maximizing genomic variant discovery via Bayesian nonparametrics
Name
1912.05516.pdf
Description
Accepted version
Size
8.37 MB
Format
Adobe PDF
Checksum (MD5)
1c4b0156a953294e4b8de18d39ad4e2b
Author(s)
Masoero, Lorenzo
Camerlenghi, Federico
Favaro, Stefano
Broderick, Tamara
Date Issued
2022
Journal
Biometrika
Publisher
Oxford University Press (OUP)
Citation
Masoero, Lorenzo, Camerlenghi, Federico, Favaro, Stefano and Broderick, Tamara. 2022. "More for less: predicting and maximizing genomic variant discovery via Bayesian nonparametrics." Biometrika, 109 (1).
Version
Author's final manuscript
Abstract
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.
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1093/BIOMET/ASAB012
