Comparative epigenomics: defining and utilizing epigenomic variations across species, time-course, and individuals.
Comparative epigenomics: defining and utilizing epigenomic variations across species, time-course, and individuals.
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DOI：
10.1002/wsbm.1274
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发表时间：
2014-09
期刊：
影响因子：
--
通讯作者：
Zhong S
中科院分区：
文献类型：
--
作者：
Xiao S;Cao X;Zhong S
Epigenomic profiling, by revealing genome-wide distributions of epigenetic modifications, generated a large amount of structural information about the chromosomes. Epigenomic analysis has quickly become a big data science, posing tremendous challenges on its translation into knowledge. To meet this challenge, comparative analysis of epigenomes, dubbed comparative epigenomics, has emerged as an active research area. Here, we summarize the recent developments in comparative epigenomic analyses into three major directions, namely the comparisons across species, the time-course of a biological process, and individuals. We review the main ideas, methods, and findings in each direction, and discuss the implications to understanding the regulatory functions of the genomes. Epigenomes play pivotal roles in cell identity, organismal development and disease processes, contribute to regulating cognition and behavior and reflect personal variation. By integrating environmental signals with genomic instructions, epigenomes are instrumental in bridging genotypic variation and phenotypic diversity. The rapid growth of high throughput sequencing has substantially reduced the costs of mapping epigenomes. A few new common understandings have been established through epigenomic profiling. First, each cell type possesses characteristic chromatin states. Second, cis regulatory elements possess specific chromatin signatures, characterized by combinations of epigenomic marks. Third, a large class of new genes that produce long intergenic non-coding RNAs (lincRNAs) possess similar epigenomic characteristics to coding genes, and thus can be identified and annotated in the genome. Epigenomic analysis has quickly become a big data science, posing tremendous challenges on its translation into knowledge. One dimension of the humongous growth of epigenomic data is in size, which is powered by three orthogonal engines. First, high-throughput sequencing enabled gigantic scales of data generation. Second, the enhanced data federation merged data across multiple labs and multiple institutions. Third, the raw data were transformed into processed data by analysis software, which becomes a multiplier of the (raw) big data. A second dimension of the growth of epigenomic data is in complexity and heterogeneity, which is at least partially by multi-dimensional data reflecting different aspects of the epigenetic states, including but not limited to protein-DNA interactions, histone and DNA modifications, long-range interactions, RNA-chromatin interactions, and the increasingly popular time-course experimental design. The challenge of translating epigenomic data into the knowledge of regulatory functions of the genome has been met by the recently developed “comparative epigenomics” approach. Comparative analysis is perhaps the oldest and the most essential approach to study biology. The soon that epigenomic maps were produced in a genome-wide manner, comparative analysis between cell types were started with embryonic stem cells and adult cell types. Such comparisons led to the early discovery that embryonic stem cells possess a specific combination of epigenomic marks, dubbed bivalent domains. This intuitive cell type comparison provided early insights but did not fully reveal the information buried in epigenomic data. Since then, new comparison methods have been explored and a few breakthroughs were made in the recent years.
