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This is the source code for the paper : Joint likelihood-free inference of the number of selected single nucleotide polymorphisms and the selection coefficient in an evolving population https://www.biorxiv.org/content/10.1101/2022.09.20.508756v1

Joint-Inference-of-selection-and-number-of-selected-target

  • abcpy: A local repository containing necessary abcpy codebase.
  • input: Contains the input haplotype dataset and mimiCREE2 Java code.
  • model_mimiCREE.py: Contains simulation model by using mimiCREE2 [2].
  • Statistics_new.py: Computes the summary statistics used for this study.
  • DataGenerator.py: Code to simulate all the simulated data for the simulation study.
  • Data: Contains all the simulated data created by DataGenerator.py for the simulation study.
  • RunSimulationExperiment.py: Runs the inference for different simulation setups.
  • Results: Contains all the posteriors inferred by RunSimulationExperiment.py
  • AnalyseSimulationPosterior.py: Analyses the posterior inferred by RunSimulationExperiment.py
  • YeastData: Contains Yeast dataset, inferred posterior and yeast.py needed to create dataset corresponding to different windows. Also some figures.
  • RunYeastExperiment.py: Runs the inference for Yeast data.

ABCpy pacakage details available at here

mimiCREE2 user manual available at here

[1] Carlo Albert, Hans R Künsch, and Andreas Scheidegger. A simulated annealing approach to approximate bayes computations. Statistics and computing, 25(6):1217–1232, 2015.

[2] Christos Vlachos and Robert Kofler. Mimicree2: Genome-wide forward simulations of evolve and resequencing studies. PLoS computational biology, 14(8):e1006413, 2018.

[3] Hui Zou. The adaptive lasso and its oracle properties. Journal of the American statistical association, 101(476):1418–1429, 2006. 15

[4] Thomas Taus, Andreas Futschik, and Christian Schlötterer. Quantifying selection with pool-seq time series data. Molecular biology and evolution, 34(11):3023–3034, 2017.

[5] Ritabrata Dutta, Marcel Schoengens, Lorenzo Pacchiardi, Avinash Ummadisingu, Nicole Widmer, Pierre Künzli, Jukka-Pekka Onnela, and Antonietta Mira. Abcpy: A high-performance computing perspective to approximate bayesian computation. Journal of Statistical Software, 100(7):1–38, 2021.