Computational Approaches for Mining GRO-Seq Data to Identify and Characterize Active Enhancers.
Computational Approaches for Mining GRO-Seq Data to Identify and Characterize Active Enhancers.
复制标题
挖掘 GRO-Seq 数据以识别和表征活性增强子的计算方法。
DOI:
10.1007/978-1-4939-4035-6_10
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发表时间:
2017
期刊:
影响因子:
--
通讯作者:
Kraus WL
中科院分区:
文献类型:
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作者:
Nagari A;Murakami S;Malladi VS;Kraus WL
Transcriptional enhancers are DNA regulatory elements that are bound by transcription factors and act to positively regulate the expression of nearby or distally-located target genes. Enhancers have many features that have been discovered using genomic analyses. Recent studies have shown that active enhancers recruit RNA polymerase II (Pol II) and are transcribed, producing enhancer RNAs (eRNAs). GRO-seq, a method for identifying the location and orientation of all actively transcribing RNA polymerases across the genome, is a powerful approach for monitoring nascent enhancer transcription. Furthermore, the unique pattern of enhancer transcription can be used to identify enhancers in the absence of any information about the underlying transcription factors. Here we describe the computational approaches required to identify and analyze active enhancers using GRO-seq data, including data pre-processing, alignment, and transcript calling. In addition, we describe protocols and computational pipelines for mining GRO-seq to identify active enhancers, as well as known transcription factor binding sites that are transcribed. Furthermore, we discuss approaches for integrating GRO-seq-based enhancer data with other genomic data, including target gene expression and function. Finally, we describe molecular biology assays that can be used to confirm and explore further the function of enhancers that have been identified using genomic assays. Together, these approaches should allow the user to identify, and explore the features and biological functions of new cell type-specific enhancers.