Identifying transcriptional cis-regulatory modules in animal genomes

被引:50
作者
Suryamohan, Kushal [1 ,2 ]
Halfon, Marc S. [1 ,2 ,3 ,4 ,5 ,6 ]
机构
[1] SUNY Buffalo, Dept Biochem, Buffalo, NY 14214 USA
[2] NY State Ctr Excellence Bioinformat & Life Sci, Buffalo, NY USA
[3] SUNY Buffalo, Dept Biol Sci, Buffalo, NY 14260 USA
[4] SUNY Buffalo, Dept Biomed Informat, Buffalo, NY 14260 USA
[5] Roswell Pk Canc Inst, Dept Mol & Cellular Biol, Buffalo, NY 14263 USA
[6] Roswell Pk Canc Inst, Program Canc Genet, Buffalo, NY 14263 USA
关键词
DNA-BINDING SPECIFICITY; CONSERVED NONCODING SEQUENCES; ENHANCER ACTIVITY MAPS; DE-NOVO DISCOVERY; IN-VIVO; MAMMALIAN ENHANCERS; CHIP-SEQ; DEVELOPMENTAL ENHANCERS; TARGET GENES; COMPUTATIONAL IDENTIFICATION;
D O I
10.1002/wdev.168
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Gene expression is regulated through the activity of transcription factors (TFs) and chromatin-modifying proteins acting on specific DNA sequences, referred to as cis-regulatory elements. These include promoters, located at the transcription initiation sites of genes, and a variety of distal cis-regulatory modules (CRMs), the most common of which are transcriptional enhancers. Because regulated gene expression is fundamental to cell differentiation and acquisition of new cell fates, identifying, characterizing, and understanding the mechanisms of action of CRMs is critical for understanding development. CRM discovery has historically been challenging, as CRMs can be located far from the genes they regulate, have few readily identifiable sequence characteristics, and for many years were not amenable to high-throughput discovery methods. However, the recent availability of complete genome sequences and the development of next-generation sequencing methods have led to an explosion of both computational and empirical methods for CRM discovery in model and nonmodel organisms alike. Experimentally, CRMs can be identified through chromatin immunoprecipitation directed against TFs or histone post-translational modifications, identification of nucleosome-depleted open' chromatin regions, or sequencing-based high-throughput functional screening. Computational methods include comparative genomics, clustering of known or predicted TF-binding sites, and supervised machine-learning approaches trained on known CRMs. All of these methods have proven effective for CRM discovery, but each has its own considerations and limitations, and each is subject to a greater or lesser number of false-positive identifications. Experimental confirmation of predictions is essential, although shortcomings in current methods suggest that additional means of validation need to be developed. WIREs Dev Biol 2015, 4:59-84. doi: 10.1002/wdev.168 For further resources related to this article, please visit the . Conflict of interest: The authors have declared no conflicts of interest for this article. (C) 2014Wiley Periodicals, Inc.
引用
收藏
页码:59 / 84
页数:26
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