Histone Explorer: AI-Enhanced Identification and Characterization of Histone Modification

Abstract

Histone modifications are one of the key epigenetic mechanisms to control the structure of chromatids and gene expression and play a pivotal role in cellular differentiation, development, and pathogenesis. In spite of the fact that Chromatin Immunoprecipitation Sequencing (ChIP-seq) is an experimental methodology that allows identifying histone marks through the use of high-resolution sequencing, it is expensive, labor-intensive and has low scalability, preventing its general use in a wide range of biological studies. To overcome these difficulties, this research suggests to consider Histone Explorer as an AI-enhanced computational system of the correct recognition and description of sites of histone modifications directly on the basis of genomic sequence information. The suggested framework will consist of a bioinformatics preprocessing pipeline that is reproducible, and sophisticated artificial intelligence models. Peaks of histone modification genomic intervals were obtained at the public repositories of epigenomic repositories such as ENCODE and Roadmap Epigenomics and mapped to a reference genome, hg38, to produce a curated FASTA sequence dataset. An extensive feature extraction scheme was used in order to obtain local and global sequence features, such as nucleotide frequency patterns, k-mer representations, positional features, pairwise interaction features, and higher-order statistical moments. It was trained and tested on the following models: CNN, BiLSTM, GRU, and attention- based transformer models among various models trained using machine learning, deep learning, and transformer-based architectures. There were 100,000 positive and negative samples of Histone sequences in the dataset. The performance of the models was strictly tested with independent test sets and using the k-fold cross-validation protocols. The experimental findings show that the transformer-based Histone Explorer model performs better than both classical and deep learning baselines with an accuracy of 91.5, precision of 90.2, recall of 92.8 and F1-score of 91.5 and AUC- ROC of 95.0. Such findings demonstrate that there is high generalization properties and strength over a wide spectrum of genomic regions. All in all, Histone Explorer offers a scalable, precise, and biologically meaningful AI-based framework of histone modifications identification. The proposed system can provide a useful computational resource in epigenetics studies, biomarker discovery, and precision-medicine studies because it lowers reliance on expensive experimental studies and allows analyzing epigenomics on a high-throughput.