Details

Autor(en) / Beteiligte

• Xiao, Shu
• Xie, Dan
• Cao, Xiaoyi
• Yu, Pengfei
• Xing, Xiaoyun
• Chen, Chieh-Chun
• Musselman, Meagan
• Xie, Mingchao
• West, Franklin D.
• Lewin, Harris A.
• Wang, Ting
• Zhong, Sheng

Titel

Comparative Epigenomic Annotation of Regulatory DNA

Ist Teil von

• Cell, 2012-06, Vol.149 (6), p.1381-1392

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2012

Quelle

MEDLINE

Beschreibungen/Notizen

• Despite the explosive growth of genomic data, functional annotation of regulatory sequences remains difficult. Here, we introduce “comparative epigenomics”—interspecies comparison of DNA and histone modifications—as an approach for annotation of the regulatory genome. We measured in human, mouse, and pig pluripotent stem cells the genomic distributions of cytosine methylation, H2A.Z, H3K4me1/2/3, H3K9me3, H3K27me3, H3K27ac, H3K36me3, transcribed RNAs, and P300, TAF1, OCT4, and NANOG binding. We observed that epigenomic conservation was strong in both rapidly evolving and slowly evolving DNA sequences, but not in neutrally evolving sequences. In contrast, evolutionary changes of the epigenome and the transcriptome exhibited a linear correlation. We suggest that the conserved colocalization of different epigenomic marks can be used to discover regulatory sequences. Indeed, seven pairs of epigenomic marks identified exhibited regulatory functions during differentiation of embryonic stem cells into mesendoderm cells. Thus, comparative epigenomics reveals regulatory features of the genome that cannot be discerned from sequence comparisons alone. [Display omitted] ► Epigenetic patterns of histone and DNA modification are conserved across species ► Epigenomic conservation occurs in both fast- and slow-evolving DNA sequences ► Changes in the epigenome, transcriptome, and protein-DNA binding patterns are correlated ► The conserved colocalization of different epigenetic marks defines regulatory DNA Interspecies, comparative analysis of DNA methylation and histone modification patterns can be used to identify regulatory DNA sequences and may explain how changes in transcription and protein-DNA interactions arise during evolution.