More for less: predicting and maximizing genomic variant discovery via Bayesian nonparametrics
<jats:title>Summary</jats:title> <jats:p>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 resource...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Oxford University Press (OUP)
2022
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| Online Access: | https://hdl.handle.net/1721.1/142893 |
| _version_ | 1826214209591967744 |
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| author | Masoero, Lorenzo Camerlenghi, Federico Favaro, Stefano Broderick, Tamara |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Masoero, Lorenzo Camerlenghi, Federico Favaro, Stefano Broderick, Tamara |
| author_sort | Masoero, Lorenzo |
| collection | MIT |
| description | <jats:title>Summary</jats:title>
<jats:p>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.</jats:p> |
| first_indexed | 2024-09-23T16:01:50Z |
| format | Article |
| id | mit-1721.1/142893 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T16:01:50Z |
| publishDate | 2022 |
| publisher | Oxford University Press (OUP) |
| record_format | dspace |
| spelling | mit-1721.1/1428932023-12-08T18:13:17Z More for less: predicting and maximizing genomic variant discovery via Bayesian nonparametrics Masoero, Lorenzo Camerlenghi, Federico Favaro, Stefano Broderick, Tamara Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science <jats:title>Summary</jats:title> <jats:p>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.</jats:p> 2022-06-07T12:26:26Z 2022-06-07T12:26:26Z 2022 2022-06-07T11:57:16Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/142893 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). en 10.1093/BIOMET/ASAB012 Biometrika Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International https://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Oxford University Press (OUP) arXiv |
| spellingShingle | Masoero, Lorenzo Camerlenghi, Federico Favaro, Stefano Broderick, Tamara More for less: predicting and maximizing genomic variant discovery via Bayesian nonparametrics |
| title | More for less: predicting and maximizing genomic variant discovery via Bayesian nonparametrics |
| title_full | More for less: predicting and maximizing genomic variant discovery via Bayesian nonparametrics |
| title_fullStr | More for less: predicting and maximizing genomic variant discovery via Bayesian nonparametrics |
| title_full_unstemmed | More for less: predicting and maximizing genomic variant discovery via Bayesian nonparametrics |
| title_short | More for less: predicting and maximizing genomic variant discovery via Bayesian nonparametrics |
| title_sort | more for less predicting and maximizing genomic variant discovery via bayesian nonparametrics |
| url | https://hdl.handle.net/1721.1/142893 |
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