SCCT [1] (Selection detection by Conditional Coalescent Tree) is a efficiency computational software developed to detect recent positive selection using deep sequencing data. It's robust to various demographic events and also robust to the variations of mutation rates and recombination rates. This method is also a powerful method, which has power comparable to iHS [2] method. SCCT, however, improved the ability to pinpoint selective causal sites, facilitated with other variant annotations, which can greatly help geneticists to explore the mechanisms behind positive selection events.
| [1] | Wang, M., Huang, X., Li, R., Xu, H., Jin, L., & He, Y. (2014). Detecting Recent Positive Selection with High Accuracy and Reliability by Conditional Coalescent Tree. Molecular Biology and Evolution, msu244–. doi:10.1093/molbev/msu244. |
| [2] | Voight, B. F., Kudaravalli, S., Wen, X., & Pritchard, J. K. (2006). A map of recent positive selection in the human genome. PLoS Biology, 4(3), e72. doi:10.1371/journal.pbio.0040072. |
If you want to use this software, please read the documentation in DOC directory,
which contains a tutorial to play a toy example.
If you want to replicate the simulation results in our paper, please consult and run the the scripts in simulation_scripts directory.
Note
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