Bayesian localization of CNV candidates in WGS data within minutes
Abstract Background Full Bayesian inference for detecting copy number variants (CNV) from whole-genome sequencing (WGS) data is still largely infeasible due to computational demands. A recently introduced approach to perform Forward–Backward Gibbs sampling using dynamic Haar wavelet compression has...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2019-09-01
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| Series: | Algorithms for Molecular Biology |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13015-019-0154-7 |
| _version_ | 1818533753969967104 |
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| author | John Wiedenhoeft Alex Cagan Rimma Kozhemyakina Rimma Gulevich Alexander Schliep |
| author_facet | John Wiedenhoeft Alex Cagan Rimma Kozhemyakina Rimma Gulevich Alexander Schliep |
| author_sort | John Wiedenhoeft |
| collection | DOAJ |
| description | Abstract Background Full Bayesian inference for detecting copy number variants (CNV) from whole-genome sequencing (WGS) data is still largely infeasible due to computational demands. A recently introduced approach to perform Forward–Backward Gibbs sampling using dynamic Haar wavelet compression has alleviated issues of convergence and, to some extent, speed. Yet, the problem remains challenging in practice. Results In this paper, we propose an improved algorithmic framework for this approach. We provide new space-efficient data structures to query sufficient statistics in logarithmic time, based on a linear-time, in-place transform of the data, which also improves on the compression ratio. We also propose a new approach to efficiently store and update marginal state counts obtained from the Gibbs sampler. Conclusions Using this approach, we discover several CNV candidates in two rat populations divergently selected for tame and aggressive behavior, consistent with earlier results concerning the domestication syndrome as well as experimental observations. Computationally, we observe a 29.5-fold decrease in memory, an average 5.8-fold speedup, as well as a 191-fold decrease in minor page faults. We also observe that metrics varied greatly in the old implementation, but not the new one. We conjecture that this is due to the better compression scheme. The fully Bayesian segmentation of the entire WGS data set required 3.5 min and 1.24 GB of memory, and can hence be performed on a commodity laptop. |
| first_indexed | 2024-12-11T18:02:55Z |
| format | Article |
| id | doaj.art-4893cd7a92b74bd1b6c203833c7d5319 |
| institution | Directory Open Access Journal |
| issn | 1748-7188 |
| language | English |
| last_indexed | 2024-12-11T18:02:55Z |
| publishDate | 2019-09-01 |
| publisher | BMC |
| record_format | Article |
| series | Algorithms for Molecular Biology |
| spelling | doaj.art-4893cd7a92b74bd1b6c203833c7d53192022-12-22T00:55:50ZengBMCAlgorithms for Molecular Biology1748-71882019-09-0114111610.1186/s13015-019-0154-7Bayesian localization of CNV candidates in WGS data within minutesJohn Wiedenhoeft0Alex Cagan1Rimma Kozhemyakina2Rimma Gulevich3Alexander Schliep4Department of Computer Science and Engineering, University of Gothenburg | ChalmersMax Planck Institute for Evolutionary AnthropologyInstitute of Cytology and Genetics of the Siberian Branch of the Russian Academy of SciencesInstitute of Cytology and Genetics of the Siberian Branch of the Russian Academy of SciencesDepartment of Computer Science and Engineering, University of Gothenburg | ChalmersAbstract Background Full Bayesian inference for detecting copy number variants (CNV) from whole-genome sequencing (WGS) data is still largely infeasible due to computational demands. A recently introduced approach to perform Forward–Backward Gibbs sampling using dynamic Haar wavelet compression has alleviated issues of convergence and, to some extent, speed. Yet, the problem remains challenging in practice. Results In this paper, we propose an improved algorithmic framework for this approach. We provide new space-efficient data structures to query sufficient statistics in logarithmic time, based on a linear-time, in-place transform of the data, which also improves on the compression ratio. We also propose a new approach to efficiently store and update marginal state counts obtained from the Gibbs sampler. Conclusions Using this approach, we discover several CNV candidates in two rat populations divergently selected for tame and aggressive behavior, consistent with earlier results concerning the domestication syndrome as well as experimental observations. Computationally, we observe a 29.5-fold decrease in memory, an average 5.8-fold speedup, as well as a 191-fold decrease in minor page faults. We also observe that metrics varied greatly in the old implementation, but not the new one. We conjecture that this is due to the better compression scheme. The fully Bayesian segmentation of the entire WGS data set required 3.5 min and 1.24 GB of memory, and can hence be performed on a commodity laptop.http://link.springer.com/article/10.1186/s13015-019-0154-7HMMWaveletCNVBayesian inference |
| spellingShingle | John Wiedenhoeft Alex Cagan Rimma Kozhemyakina Rimma Gulevich Alexander Schliep Bayesian localization of CNV candidates in WGS data within minutes Algorithms for Molecular Biology HMM Wavelet CNV Bayesian inference |
| title | Bayesian localization of CNV candidates in WGS data within minutes |
| title_full | Bayesian localization of CNV candidates in WGS data within minutes |
| title_fullStr | Bayesian localization of CNV candidates in WGS data within minutes |
| title_full_unstemmed | Bayesian localization of CNV candidates in WGS data within minutes |
| title_short | Bayesian localization of CNV candidates in WGS data within minutes |
| title_sort | bayesian localization of cnv candidates in wgs data within minutes |
| topic | HMM Wavelet CNV Bayesian inference |
| url | http://link.springer.com/article/10.1186/s13015-019-0154-7 |
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