epigen

Code and scripts related to the paper. O# O4747: Machine learning tools for epigenetics.

Environmental toxins and pathogens can influence epigenetic modifications on chromosomes, thereby leaving trace evidence of exposures. However, the avalanche of epigenomic data is difficult to parse for biological interpretation given non-linear complex patterns and relationships. This attractive challenge in epigenomic data lends itself to machine learning for discerning infectivity and susceptibility. In this study, we explore over 3,000 epigenomes of uninfected individuals and provide a comprehensive characterization of the relationships among epigenetic modifiers, their modifiers, and specific immune cell types across all chromosomes using hierarchical clustering. Workflow of our approach.

General info:

data folder contains files necessary to execute the code, some large files needed to be downloaded as written in the scripts below.

This repository contains python scripts and bash scripts to obtain the paper results.

All of these scripts related to the hg38-aligned dataset; it is easy to get files and results for the hg19-aligned dataset by changing 38 to 19 in the code and 1632 (#samples in the hg 38) to 1479 (#samples in the hg 19).

The data doesn't contain the chromosome Y.

The order of execution:

1. Download bigWig files from the ENCODE, run run_download_data_files_script that calls download_data_files.py.
2. For each chromosome calculate values for each 200bp region, run run_get_values_for_chromosome_script that calls get_values_for_chromosome.py.
3. For each chromosome obtain the dataframe that contains columns where each value (p-values here) is less than 0.05 in at least one sample (row): create_pvdf.py that calls run_create_pvdf_script.
4. For each chromosome calculate the pairwise correlation matrix between samples which requires GPU (otherwise will take hours vs minutes per chromosome): correlation_matrix_calculation.py.
5. For each chromosome obtain a list of known genes and their bp position in the chromosome: get_ret_df_files.py.
6. Produce paper circular plot 2: get_newick_string.py and produce_circular_plot.py.
7. Produce paper umap plots 3, 4, and 5: produce_umap_plots.py.
8. Produce paper entropy plots 6 and 7: produce_entropy_plots.py.
9. Produce paper co-occurence plot 8: produce_cooccurence_plot.py.
10. Produce paper gene ontology resuls needed for plots 9 and 10: produce_go_results.py.
11. Produce paper datasets comparison plots 11 and 12: produce_comparison_plots_datasets19vs38.py.

Circular plot (step #6 above).

UMAP plot (step #7 above).

© 2024. Triad National Security, LLC. All rights reserved. This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S. Department of Energy/National Nuclear Security Administration. All rights in the program are reserved by Triad National Security, LLC, and the U.S. Department of Energy/National Nuclear Security Administration. The Government is granted for itself and others acting on its behalf a nonexclusive, paid-up, irrevocable worldwide license in this material to reproduce, prepare. derivative works, distribute copies to the public, perform publicly and display publicly, and to permit others to do so.

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