Chapter 12 - Computational Epigenetics
Abstract
Epigenetics has emerged as a rapidly growing field for studying the heritable alterations involved in the regulation of gene expression patterns that are not due to changes in DNA sequence. To which, the epigenetic mechanisms, such as DNA methylation and histone modifications, can modulate chromatin structure and gene regulation, during cellular development and differentiation in higher organisms. Recent advancements in high-throughput epigenetic profiling technologies, including bisulfite microarray, bisulfite sequencing, affinity enrichment, ChIP-on-chip, and ChIP sequencing, have generated vast amounts of epigenomic data. In turn, the developments of bioinformatics databases and software tools have thus contributed significantly to the substantial, and growing, interest in epigenetic research. This chapter reviews the key aspects and techniques of computational epigenetics. In particular, the major computational tools, databases, and strategies for epigenetics analysis of DNA methylation and histone modifications have been summarized.
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Computational Epigenetics: The Competitive Endogenous RNAs Network Analysis
2022, Handbook of Epigenetics: The New Molecular and Medical Genetics, Third EditionIn this updated chapter for computational epigenetics, we introduced the important concepts of ceRNA and its network. CeRNAs are the sponging materials within the epigenome that may affect the transcriptional and translational activities of target genes. These ceRNA components tend to interact with miRNAs as triplets (ceRNA-miRNA-ceRNA), thereby reducing the capabilities of miRNAs to act on their corresponding mRNAs. It has been known that computational epigenetics encompasses different analytical approaches that range from gene-localized, context-based to genome-wide based. Regardless of which approach has been chosen for a specific research, all the analytic approaches must reduce the possibility of obtaining false positive and false negative data. Hence, the objectives of this chapter are to review and discuss appropriate mathematical modeling methods for ceRNA network analysis, as well as to compare the context-based and genome-wide based approaches within the context of computational epigenetics.
Computational Epigenetics and Disease
2019, Computational Epigenetics and DiseasesEpigenetics represents a rapidly growing and promising field for the discovery of novel disease biomarkers and understanding the pathophysiology and mechanism of complex diseases. The central objectives of writing this book are to provide theoretical insight, summarize practical implications, and draw attention to the emerging area of computational epigenetics and disease. There are 23 chapters in this book, covering the theories, frameworks, pipelines, and methods of computational epigenetics analyses and discussing the development of new software and databases and integration of these tools in analyzing noncommunicable diseases, neurological disorders, and autoimmune diseases as well as several important types of cancers. The emerging field of computational epigenetics has been moving from a hypothesis-driven approach toward a holistic data-driven modeling approach. Hence, we hope that reader gains pertinent insight after reading this book.
Integrative Analysis of Epigenomics Data
2019, Computational Epigenetics and DiseasesState-of-the-art next-generation sequencing (NGS) technologies enable efficient generation of high-throughput “omics” data. Integration of multiple omics data may yield unprecedented comprehensive knowledge about molecular mechanisms of diseases. However, integrative analysis of these genome-wide molecular profiles is very challenging due to the high dimensionality nature of the data and the complexity of the biological systems. In this chapter, we provide an overview of methods for joint analysis of multi “omics” including epigenomics data, and how these methods can be applied to further advance our understanding of chromosomal architecture that is affecting regulation of genes in diseases.
Computational Epigenetics in Lung Cancer
2019, Computational Epigenetics and DiseasesThis chapter introduces the technology of gene expression profiles preprocessing based on the complex use of bicluster analysis and objective clustering inductive technology with the use of self-organizing SOTA and density DBSCAN clustering algorithms. Implementation of this technology allows us to increase the quality of epigenetics investigation in lung cancer based on the use of gene regulatory network. Inductive methods of complex system analysis were used as the basis to implement the objective clustering inductive technology of gene expression profiles. To estimate the clustering quality for equal power subsets (including the same quantity of pairwise similar objects) the complex multiplicative criterion was calculated as a combination of Calinski-Harabasz and WB index criteria. External clustering quality criteria were calculated as a normalized difference of internal clustering quality criteria for equal power subsets. Final decision concerning the determination of optimal parameters of clustering algorithm operation has been done based on the maximum value of Harrington desirability function that takes into account both the character of the objects and clusters distribution in various clustering and the differences between clustering, which are implemented on equal power data subsets. To estimate the effectiveness of the proposed technology, the data set of lung cancer patients were used. This data set includes the gene expression profiles of 96 patients, 10 of which were healthy, and 86 patients were divided according to the degree of disease severity into three groups (well, moderate, poor). The results of the simulation allow us to propose the hybrid model of step-by-step process of gene expression profiles, whereby grouping is based on the complex use of clustering and biclustering algorithms.
Data Analysis of ChIP-Seq Experiments: Common Practice and Recent Developments
2019, Computational Epigenetics and DiseasesIn this chapter, we reviewed many aspects of the common workflow for ChIP-Seq data analysis. First, we reminded the readers the potential impact of the design of the ChIP-Seq experiment and the quality of the data on the choice of the tools and the results. Second, we discussed many issues and the common tools in read alignment, peak calling, and the differential enrichment detection. We then proceeded to the discussion on the available analysis pipelines for ChIP-Seq data. Finally, we reviewed the common practice and the new development in allele-specific binding detection, an emergent complementary analysis of ChIP-Seq data that associates this molecular epigenetic phenotype to the variation in the genotype.
Epigenome-Wide DNA Methylation Profiles in Oral Cancer
2019, Computational Epigenetics and DiseasesAberrant DNA methylation, consequent activation of oncogenes, and silencing of tumor suppressor genes are general phenomena observed in different carcinomas including oral squamous cell carcinoma. Despite the general accessibility of the oral cavity during physical examination, many malignancies are not diagnosed until late stages of the disease. The recent advancement of massively parallel next-generation sequencing and microarray technologies enabled researchers to generate epigenome-wide DNA methylation data for several cell types of diseased and healthy individuals. The urgent need to handle these data led to development of several computational and statistical tools. Here, we have deliberated the recent advancement in computational and statistical tools for genome-wide DNA methylation data analysis and up-to-date progress of DNA methylomics in oral cancer. Identification of epigenetically regulated genes in early stages of oral cancer may be used as potential biomarker for early detection and better clinical management of oral cancer.