Epigenetics is defined as the study of changes in phenotypes but no change in DNA sequence, whereas epigenomics is the study of the complete set of epigenetic modifications on the epigenome. Modifications including DNA methylation, mRNA methylation, histone modification, chromatin remodeling, non-coding RNA (microRNA, small RNA and long non-coding RNA) regulations etc could help us to interpret genetic and biological mechanisms underlying complex health and production traits in human and livestock, e.g., the characterization of genomic variants regulating the transcriptome.
With rapid advancements of next-generation sequencing technology, enormous amount epigenomic sequencing data appears in various modification types, such as WGBS, RRBS, MeDIP-seq, ChIP-seq, ATAC-seq, FAIRE-seq, DNase-seq, MNase-seq, miRNA-seq, lncRNA-seq, etc. To analyze epigenomic data more efficiently and accurately, advanced statistical models and bioinformatics algorithms have been applied such as linear regression, Bayesian, machine-learning models under different assumptions, to improve epigenomic-wide association study (EWAS), disease mechanism revelation and breeding programs by using epigenomic data or epigenetic catalog.
Therefore, the objective of this Research Topic is to gather findings of the epigenetics and epigenomics applied in humans and livestock for complex traits to identify the epigenomic biomarkers, interpret molecular mechanisms, improve genomic evaluation and selection, understand adaptive evolution, reveal various epigenetic maps for different species, develop novel models and methods, share new analyses tools, etc.
We welcome submissions including Original Research, Methods, Protocols, Reviews, Reports and Perspectives in the following (but not limited to) sub-themes:
• Identified candidate epigenomic biomarkers for complex traits (e.g., human and animal health) using different epigenomic sequencing data (e.g., EWAS)
• Lactate-induced histone modification or histone lysine lactylation (e.g., Lactomics)
• Applied epigenomic biomarkers to interpret molecular mechanisms from genotype to phenotype (e.g., GTEx consortium, FAANG consortium, FarmGTEx consortium)
• Use of epigenomic data in combination with genomic data to improve the genomic prediction of complex traits (e.g., Feature BLUP)
• Epigenome-wide map of various tissues
• Statistical models and algorithms for EWAS, feature BLUP and big epigenomic data
• Developed bioinformatic analysis tools in R/Python/C++/Perl/Julia languages
• Bacterial epigenome analysis
• Integrated epigenomic analysis with other omics data, like DNA methylation-mRNA, microRNA-mRNA, etc., for multi-omics data integration
Epigenetics is defined as the study of changes in phenotypes but no change in DNA sequence, whereas epigenomics is the study of the complete set of epigenetic modifications on the epigenome. Modifications including DNA methylation, mRNA methylation, histone modification, chromatin remodeling, non-coding RNA (microRNA, small RNA and long non-coding RNA) regulations etc could help us to interpret genetic and biological mechanisms underlying complex health and production traits in human and livestock, e.g., the characterization of genomic variants regulating the transcriptome.
With rapid advancements of next-generation sequencing technology, enormous amount epigenomic sequencing data appears in various modification types, such as WGBS, RRBS, MeDIP-seq, ChIP-seq, ATAC-seq, FAIRE-seq, DNase-seq, MNase-seq, miRNA-seq, lncRNA-seq, etc. To analyze epigenomic data more efficiently and accurately, advanced statistical models and bioinformatics algorithms have been applied such as linear regression, Bayesian, machine-learning models under different assumptions, to improve epigenomic-wide association study (EWAS), disease mechanism revelation and breeding programs by using epigenomic data or epigenetic catalog.
Therefore, the objective of this Research Topic is to gather findings of the epigenetics and epigenomics applied in humans and livestock for complex traits to identify the epigenomic biomarkers, interpret molecular mechanisms, improve genomic evaluation and selection, understand adaptive evolution, reveal various epigenetic maps for different species, develop novel models and methods, share new analyses tools, etc.
We welcome submissions including Original Research, Methods, Protocols, Reviews, Reports and Perspectives in the following (but not limited to) sub-themes:
• Identified candidate epigenomic biomarkers for complex traits (e.g., human and animal health) using different epigenomic sequencing data (e.g., EWAS)
• Lactate-induced histone modification or histone lysine lactylation (e.g., Lactomics)
• Applied epigenomic biomarkers to interpret molecular mechanisms from genotype to phenotype (e.g., GTEx consortium, FAANG consortium, FarmGTEx consortium)
• Use of epigenomic data in combination with genomic data to improve the genomic prediction of complex traits (e.g., Feature BLUP)
• Epigenome-wide map of various tissues
• Statistical models and algorithms for EWAS, feature BLUP and big epigenomic data
• Developed bioinformatic analysis tools in R/Python/C++/Perl/Julia languages
• Bacterial epigenome analysis
• Integrated epigenomic analysis with other omics data, like DNA methylation-mRNA, microRNA-mRNA, etc., for multi-omics data integration