Blinka, Steven, Reimer, Michael H, Pulakanti, Kirthi, Pinello, Luca, Yuan, Guo-Cheng, Rao, Sridhar, Steven Blinka, Michael H. Reimer Jr., Kirthi Pulakanti, Luca Pinello, Guo-Cheng Yuan, Sridhar Rao Ph.D., Michael H. ReimerJr., Sridhar Rao, Michael H. Reimer
Editors
Ulf Andersson Ørom
Abstract
Recent work has shown that RNA polymerase II-mediated transcription at distal cis-regulatory elements serves as a mark of highly active enhancers. Production of noncoding RNAs at enhancers, termed eRNAs, correlates with higher expression of genes that the enhancer interacts with; hence, eRNAs provide a new tool to model gene activity in normal and disease tissues. Moreover, this unique class of noncoding RNA has diverse roles in transcriptional regulation. Transcribed enhancers can be identified by a common signature of epigenetic marks by overlaying a series of genome-wide chromatin immunoprecipitation and RNA sequencing datasets. A computational approach to filter non-enhancer elements and other classes of noncoding RNAs is essential to not cloud downstream analysis. Here we present a protocol that combines wet and dry bench methods to accurately identify transcribed enhancers genome-wide as well as an experimental procedure to validate these datasets.
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 September 2016.
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