An Explainable Machine Learning Approach to study the positional significance of histone post-translational modifications in gene regulation

Epigenetic mechanisms regulate gene-expression by altering the structure of the chromatin without modifying the underlying DNA sequence. Histone post-translational modifications (PTMs) are critical epigenetic signals that influence transcriptional activity, promoting or repressing gene-expression. Understanding the impact of individual PTMs and the combinatorial effects is essential to deciphering gene regulatory mechanisms. In this study, we analyzed the ChIP-seq data for 26 PTMs in yeast, examining the PTM intensities gene-wise from positions -3 to 8 in each gene. Using XGBoost classifiers, we predicted gene transcription rates and identified key histone modifications and nucleosomal positions that are critical in gene expression using explainability measures (such as SHAP). Our study provides a comprehensive insight into the histone modifications, their positions and their combinations that are most critical in gene regulation in yeast. The proposed explainable Machine Learning models can be easily extended to other model organisms to provide meaningful insights into gene regulation by epigenetic mechanisms.