Utilizing High-Throughput Sequencing and Deep Learning to Uncover Disease Epigenetic Mechanisms
A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Bioinformatics".
Deadline for manuscript submissions: 15 September 2025 | Viewed by 74
Special Issue Editors
Interests: high-throughput sequencing; deep learning; epigenetic mechanisms; genome 3D structure; Method development
Interests: focused on developing machine learning/deep learning tools for identifying DNA, RNA, and protein modification sites, with current interest in developing computational pipelines to generate embeddings and identify cell types from single-cell Hi-C data
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Special Issue Information
Dear Colleagues,
The completion of the Human Genome Project marked a pivotal milestone in biomedical research, providing a comprehensive catalog of genes essential for human life. However, it has become increasingly evident that gene sequences alone do not fully explain the intricate regulatory mechanisms underlying development and disease. Epigenetic modifications, which influence gene expression without altering DNA sequences, play a crucial role in governing cellular identity, differentiation, and response to environmental stimuli. Among these, DNA methylation, histone modifications, chromatin remodeling, non-coding RNA-mediated regulation, and 3D chromatin interactions have emerged as key players in disease pathogenesis.
The advent of high-throughput sequencing technologies has revolutionized the study of epigenetics, enabling the genome-wide profiling of DNA methylation landscapes, chromatin accessibility, histone modifications, and 3D chromatin structure at an unprecedented resolution. Concurrently, deep learning and artificial intelligence have become indispensable tools for extracting complex patterns from these large-scale datasets, facilitating novel insights into disease-associated epigenetic mechanisms. By integrating sequencing-based multi-omics approaches with advanced computational models, researchers can now uncover intricate regulatory networks, identify disease biomarkers, and predict epigenetic alterations that contribute to pathological conditions.
This Special Issue will highlight cutting-edge research on the interplay between high-throughput sequencing and deep learning in deciphering disease-related epigenetic mechanisms. Topics of interest include, but are not limited to, the following:
- DNA Methylation and Disease: Advances in whole-genome bisulfite sequencing, targeted methylation sequencing, and their applications in cancer, neurodegenerative diseases, and immune disorders.
- Histone Modifications and Chromatin Accessibility: The integration of ChIP-seq, ATAC-seq, and computational modeling to elucidate chromatin dynamics in health and disease.
- Non-coding RNAs in Epigenetic Regulation: The role of long non-coding RNAs, microRNAs, and circular RNAs in modulating chromatin states and gene expression.
- Three-dimensional Chromatin Interactions and Disease: High-resolution chromatin conformation capture techniques (e.g., Hi-C, ChIA-PET, Micro-C) combined with deep learning to investigate enhancer–promoter interactions, topologically associating domains (TADs), and their dysregulation in diseases such as cancer and neurodevelopmental disorders.
- Deep Learning for Epigenomics: The development and application of AI-driven frameworks for predicting epigenetic modifications, chromatin interactions, and enhancer–promoter regulation.
- Multi-Omics Data Integration: Approaches combining epigenomics, transcriptomics, proteomics, and single-cell sequencing to dissect disease mechanisms.
- Clinical and Translational Epigenetics: The identification of epigenetic biomarkers for early diagnosis, prognosis, and personalized medicine.
By bringing together advances in high-throughput sequencing and deep learning, this Special Issue aims to provide a comprehensive perspective on how these powerful tools are transforming our understanding of epigenetic mechanisms in disease. We invite contributions from researchers exploring novel methodologies, computational innovations, and translational applications in this rapidly evolving field.
Dr. Fuying Dao
Dr. Hao Lv
Guest Editors
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Keywords
- epigenomics
- deep Learning
- three-dimensional chromatin interactions
- high-throughput sequencing
- disease biomarkers
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