ABC estimation of the intensity and timing of selection from ancient genomes (Kerner et al., Cell Genomics 2023; https://www.sciencedirect.com/science/article/pii/S2666979X22002117).
Deep estimation based on convolutional neural networks (CNNs) (Laval et al., BioRxiv 2023; https://doi.org/10.1101/2023.07.27.550703)
Software requirements:
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Simulations: SLiM v3, R (+ dplyr package), perl
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Estimations: Python (+ Keras, TensorFlow), R (+ abc package), perl
The intensity of selection = selection strength or selection coefficient (s)
The timing of selection = time of the onset of selection (T)
Computer simulations of ABC and CNN training datasets:
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Code for simulating ancient DNA samples as described in Kerner et al., Cell Genomics 2023.
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Code was written in SLiM and uses a well-established European demography for the past 10,000 years.
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Code source for performing the simulations stored in the Simulation_files_SLiM folder.
Estimations of s and T :
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ABC estimations are conducted using R (R code for estimating selection parameters using ABC can be alo found at https://github.com/h-e-g/SLiM_aDNA_selection/).
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CNN estimations are conducted using python/keras/TensorFlow.
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Code source for performing ABC and CNN predictions are stored in the CNN folder.