KLinterSel: Intersection among candidates of different selective sweep detection methods
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Motivation
Studies that detect signals of selection in genomes often employ more than one method to increase the reliability of their findings. In fact, a usual recommendation for these studies is to apply different methodologies and select overlapping genomic regions. However, in many cases, this may be misleading because the regions under study are not independent. The overlap between candidate regions may not be due to the robustness of the methods, but rather to the structure of the data itself.
Results
This paper contributes to solving this issue by developing a test that compares, for a set of SNPs, the observed distribution of distances between candidates from different methods with that expected by chance for the same SNP dataset. The test is incorporated into the KLinterSel program, which also calculates clusters of sites that intersect between different methods within a given distance. As a proof of concept, we applied the program to compare the candidates of four selection detection methods applied to the study of divergent selection against the parasite Marteilia cochillia in common cockle ( Cerastoderma edule ).
Availability and implementation
Source code and documentation is hosted at GitHub ( https://github.com/noosdev0/KLinterSel ). For accessibility, KLinterSel has pre-built binaries for Windows, Linux and macOS (arm64).
Contact
acraaj@uvigo.es
Supplementary information
Links to additional methods/data available on Zenodo, or reference to online-only Supplementary methods and data available at the journal’s web site.
