Dataset Information
Benchmarking software tools for detecting and quantifying selection in evolve and resequencing studies.
Ontology highlight
ABSTRACT: Background
The combination of experimental evolution with whole-genome resequencing of pooled individuals, also called evolve and resequence (E&R) is a powerful approach to study the selection processes and to infer the architecture of adaptive variation. Given the large potential of this method, a range of software tools were developed to identify selected SNPs and to measure their selection coefficients.Results
In this benchmarking study, we compare 15 test statistics implemented in 10 software tools using three different scenarios. We demonstrate that the power of the methods differs among the scenarios, but some consistently outperform others. LRT-1, CLEAR, and the CMH test perform best despite LRT-1 and the CMH test not requiring time series data. CLEAR provides the most accurate estimates of selection coefficients.Conclusion
This benchmark study will not only facilitate the analysis of already existing data, but also affect the design of future data collections.
SUBMITTER: Vlachos C
PROVIDER: S-EPMC6694636 | biostudies-literature | 2019 Aug
REPOSITORIES: biostudies-literature
Publications
Benchmarking software tools for detecting and quantifying selection in evolve and resequencing studies.
Genome biology 20190815 1
<h4>Background</h4>The combination of experimental evolution with whole-genome resequencing of pooled individuals, also called evolve and resequence (E&R) is a powerful approach to study the selection processes and to infer the architecture of adaptive variation. Given the large potential of this method, a range of software tools were developed to identify selected SNPs and to measure their selection coefficients.<h4>Results</h4>In this benchmarking study, we compare 15 test statistics implement ...[more]