Boosting forward-time population genetic simulators through genotype compression
| dc.citation.firstpage | 192 | en_US |
| dc.citation.journalTitle | BMC Bioinformatics | en_US |
| dc.citation.volumeNumber | 14 | en_US |
| dc.contributor.author | Ruths, Troy | en_US |
| dc.contributor.author | Nakhleh, Luay | en_US |
| dc.date.accessioned | 2013-06-19T17:03:33Z | en_US |
| dc.date.available | 2013-06-19T17:03:33Z | en_US |
| dc.date.issued | 2013 | en_US |
| dc.description.abstract | Background: Forward-time population genetic simulations play a central role in deriving and testing evolutionary hypotheses. Such simulations may be data-intensive, depending on the settings to the various param- eters controlling them. In particular, for certain settings, the data footprint may quickly exceed the memory of a single compute node. Results: We develop a novel and general method for addressing the memory issue inherent in forward-time simulations by compressing and decompressing, in real-time, active and ancestral genotypes, while carefully accounting for the time overhead. We propose a general graph data structure for compressing the genotype space explored during a simulation run, along with efficient algorithms for constructing and updating compressed genotypes which support both mutation and recombination. We tested the performance of our method in very large-scale simulations. Results show that our method not only scales well, but that it also overcomes memory issues that would cripple existing tools. Conclusions: As evolutionary analyses are being increasingly performed on genomes, pathways, and networks, particularly in the era of systems biology, scaling population genetic simulators to handle large-scale simulations is crucial. We believe our method offers a significant step in that direction. Further, the techniques we provide are generic and can be integrated with existing population genetic simulators to boost their performance in terms of memory usage. | en_US |
| dc.embargo.terms | none | en_US |
| dc.identifier.citation | Ruths, Troy and Nakhleh, Luay. "Boosting forward-time population genetic simulators through genotype compression." <i>BMC Bioinformatics,</i> 14, (2013) BioMed Central: 192. http://dx.doi.org/10.1186/1471-2105-14-192. | en_US |
| dc.identifier.doi | http://dx.doi.org/10.1186/1471-2105-14-192 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/71329 | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | BioMed Central | en_US |
| dc.rights | This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | en_US |
| dc.rights.uri | https://creativecommons.org/licenses/by/2.0/ | en_US |
| dc.title | Boosting forward-time population genetic simulators through genotype compression | en_US |
| dc.type | Journal article | en_US |
| dc.type.dcmi | Text | en_US |
| dc.type.publication | publisher version | en_US |