Coalescent simulation with msprime
Coalescent simulation is a fundamental tool in modern population genetics. The msprime library provides unprecedented scalability in terms of both the simulations that can be performed and the efficiency with which the results can be processed. We show how coalescent models for population structure...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Book section |
| Language: | English |
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Springer
2020
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| _version_ | 1797109560555601920 |
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| author | Kelleher, J Lohse, K |
| author2 | Dutheil, JY |
| author_facet | Dutheil, JY Kelleher, J Lohse, K |
| author_sort | Kelleher, J |
| collection | OXFORD |
| description | Coalescent simulation is a fundamental tool in modern population genetics. The msprime library provides unprecedented scalability in terms of both the simulations that can be performed and the efficiency with which the results can be processed. We show how coalescent models for population structure and demography can be constructed using a simple Python API, as well as how we can process the results of such simulations to efficiently calculate statistics of interest. We illustrate msprime’s flexibility by implementing a simple (but functional) approximate Bayesian computation inference method in just a few tens of lines of code. |
| first_indexed | 2024-03-07T07:43:26Z |
| format | Book section |
| id | oxford-uuid:1af26b56-cfa1-4230-8f18-2e3e72ee2e4c |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:43:26Z |
| publishDate | 2020 |
| publisher | Springer |
| record_format | dspace |
| spelling | oxford-uuid:1af26b56-cfa1-4230-8f18-2e3e72ee2e4c2023-05-19T09:34:44ZCoalescent simulation with msprimeBook sectionhttp://purl.org/coar/resource_type/c_3248uuid:1af26b56-cfa1-4230-8f18-2e3e72ee2e4cEnglishSymplectic ElementsSpringer2020Kelleher, JLohse, KDutheil, JYCoalescent simulation is a fundamental tool in modern population genetics. The msprime library provides unprecedented scalability in terms of both the simulations that can be performed and the efficiency with which the results can be processed. We show how coalescent models for population structure and demography can be constructed using a simple Python API, as well as how we can process the results of such simulations to efficiently calculate statistics of interest. We illustrate msprime’s flexibility by implementing a simple (but functional) approximate Bayesian computation inference method in just a few tens of lines of code. |
| spellingShingle | Kelleher, J Lohse, K Coalescent simulation with msprime |
| title | Coalescent simulation with msprime |
| title_full | Coalescent simulation with msprime |
| title_fullStr | Coalescent simulation with msprime |
| title_full_unstemmed | Coalescent simulation with msprime |
| title_short | Coalescent simulation with msprime |
| title_sort | coalescent simulation with msprime |
| work_keys_str_mv | AT kelleherj coalescentsimulationwithmsprime AT lohsek coalescentsimulationwithmsprime |