DGMs-of-Evolution

This repository provides the code for the paper Deep Generative Models of Evolution: SNP-level Population Adaptation by Genomic Linkage Incorporation, submitted to 24th International Workshop on Data Mining in Bioinformatics (BioKDD25). The code is designed for creating artificial haplotypes, running simulations, preprocessing data, and training a VAE model for evolutionary data analysis.

Getting Started

Prerequisites

• Python, Java, R
• Slurm for batch processing
• Mimicree2 (mim2-v206.jar)
• PoolSeq R library
• Python dependencies can be installed via:

pip install -r requirements.txt

Usage

1. Create Artificial Haplotypes

python create_mimicree_files.py

2. Run Mimicree2 Simulations

sbatch ./slurm_scripts/1_batch_slurm_simulate.sh

3. Save Created Paths

sbatch ./slurm_scripts/2_slurm_single_write_paths

4. Preprocess Sync Files for Neural Network Training

sbatch ./slurm_scripts/3_batch_slurm_preprocess.sh

5. Estimate Ne with PoolSeq R Library

sbatch ./slurm_scripts/3a_slurm_single_estimateNe

6. Estimate Selection Coefficients (s)

sbatch ./slurm_scripts/3b_batch_slurm_estimate_s.sh

7. Train VAE Model

sbatch ./slurm_scripts/4_batch_slurm_model_training.sh

8. Evaluate VAE + WF Model

sbatch ./slurm_scripts/5_batch_slurm_model_evaluation.sh

9. Generate Plots

• Short Data Analysis Plot for Appendix:

python plot_scripts/main_data_analysis_exp_II.py

• Evaluation Plots:

python plot_scripts/plot_tmp_2.py
python plot_scripts/plot_linkage_correlations.py

Contact

For any questions, please contact siekiera@uni-mainz.de.