About this Research Topic
Our Research Topic Volume I: Computational Epigenetics in Human Diseases, Cell Differentiation, and Cell Reprogramming , was incredibly successful with over 40 accepted articles. In this rapidly developing field, we are excited to launch a second volume for further submissions.
Intergenerational inheritance of epigenetic determinants such as DNA methylation, histone modifications, or non-coding RNAs can affect gene regulation in offspring. Epigenetic regulatory events contribute in different cellular transitions such as stem cell differentiation and lineage commitment by regulating cell type-specific gene expression. Aberrations in epigenetic modifications frequently occur in a wide range of common diseases, including aging-related diseases, neuropsychiatric disorders, autoimmunity, and cancer.
In the past few decades, with the advent of high-throughput sequencing and single-cell sequencing technologies, it is now possible to identify epigenetic modifications at the whole genome scale with base-pair and single-cell resolution. This research topic will focus on the challenges of epigenetic big data analysis and integrated analysis of epigenetic and genetic data such as WGS, WES, WGBS, ChIP-seq and RNA-seq.
Areas of focus include but are not limited to:
1. Machine learning, statistical methods, and other algorithms for integrating multi-level omics data and regulation mechanisms
2. Identification of epigenetic biomarkers for disease diagnosis and prognosis
3. Single-cell epigenome analysis for human diseases and cell fate determination
4. Epigenetic and chromatin reprogramming during embryogenesis, cell reprogramming, or other algorithms of computational epigenomics
5. Databases or web servers for collecting and annotating epigenetic data
Intergenerational inheritance of epigenetic determinants such as DNA methylation, histone modifications, or non-coding RNAs can affect gene regulation in offspring. Epigenetic regulatory events contribute in different cellular transitions such as stem cell differentiation and lineage commitment by regulating cell type-specific gene expression. Aberrations in epigenetic modifications frequently occur in a wide range of common diseases, including aging-related diseases, neuropsychiatric disorders, autoimmunity, and cancer.
In the past few decades, with the advent of high-throughput sequencing and single-cell sequencing technologies, it is now possible to identify epigenetic modifications at the whole genome scale with base-pair and single-cell resolution. This research topic will focus on the challenges of epigenetic big data analysis and integrated analysis of epigenetic and genetic data such as WGS, WES, WGBS, ChIP-seq and RNA-seq.
Areas of focus include but are not limited to:
1. Machine learning, statistical methods, and other algorithms for integrating multi-level omics data and regulation mechanisms
2. Identification of epigenetic biomarkers for disease diagnosis and prognosis
3. Single-cell epigenome analysis for human diseases and cell fate determination
4. Epigenetic and chromatin reprogramming during embryogenesis, cell reprogramming, or other algorithms of computational epigenomics
5. Databases or web servers for collecting and annotating epigenetic data
Keywords: Epigenetics, DNA Methylation, ncRNA, Machine Learning, Omics, Integrative analysis, Cancer biomarker
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
