I guess, its not possible to detect eRNAs by microarray or traditional RNA-Seq data.
Dear researchers,
This question may have been asked before, sorry for that. I have read several related papers, in their method part, the detection criteria remains ambiguous.I am curious about the widely-used way to detect enhancer RNAs (eRNAs) by using a RNAseq data.
Kind regards.
2 answers
I presume that you mean enhancer-RNAs. There have been some landmark studies looking at enhancers and enhancer RNAs.
To infer a non-coding RNA as an enhancer RNA, it's transcription has to coincide with an enhancer region. Enhancer regions can be inferred by the presence of H3K27-acetylation marks, i.e., through ChIP-chip or ChIP-seq studies. The expression and identification of the eRNA would have to be inferred through through a cDNA microarray or RNA-seq. Mehylation of H3K4me1 at enhancer regions is also important in regulating transcription at the site.
The interesting studies:
- Functional roles of enhancer RNAs for oestrogen-dependent transcriptional activation
- DNA Methylation of Transcriptional Enhancers and Cancer Predisposition
- An oncogenic super-enhancer formed through somatic mutation of a noncoding intergenic element (not exactly eRNAs but intriguing study about an enhancer region nevertheless)
Edit: November 2nd 2017
Here is a list of methods that can be used:
- Reverse transcription-PCR (RT-PCR)
- RNA fluorescence in situ hybridization (RNA-FISH)
- RNA polymerase II chromatin immunoprecipitation coupled with high-throughput sequencing (RNAPII ChIP–seq)
- Global run-on sequencing (GRO-seq)
- 5′GRO-seq or GRO-cap
- BruUV-seq
- RNA-seq (total)
- RNA-seq (poly(A))
- Cap analysis of gene expression (CAGE) followed by deep sequencing
- Chromatin- bound RNA-seq
- RNA-Seq in isolated 'transcription factories'
- Native elongating transcript sequencing (NET-seq)
- RNA capture sequencing (CaptureSeq)
- Chromatin isolation by RNA purification (ChIRP-seq)
[source: http://www.nature.com/nrg/journal/v17/n4/fig_tab/nrg.2016.4_T1.html]
Gracias por el comentario. As with everything in life, there is diversity, and eRNAs can be both polyadenylated and non-polyadenylated.
Thanks, the review you attached states that "Sometimes, by pure chance, the DNA around enhancers may harbor splicing acceptor and donor sites, even perhaps polyadenylation cassettes, in which case the corresponding eRNA becomes spliced, polyadenylated, and stable. These processed and stable eRNAs, now fitting the current defining criteria for lncRNAs".
Its a minute percentage that are polyadenylated.
See my edited answer above. We are both correct in what we are saying.
Why are you so sure that it is not possible to detect these by cDNA microarray? If the eRNAs are known, then a specially-designed cDNA microarray will surely be capable of detecting these (but obviously not de novo identification of them). It will also detect the polyadenylated linc-RNAs that behave as enhancer RNAs.
Sure. cDNA microarray could be used. Thankfully cDNA microarray is not mentioned in the nature reference paper. May be its a mistake that I assumed the OP wants to identify eRNAs from traditional RNA/Seq data.
Just check this pipeline for enhancer RNA quantification from RNA-seq,
http://fun-science.club/PET/
You can also check the paper: Wu, Y, Yang, Y, Gu, H, et al. Multi‐omics analysis reveals the functional transcription and potential translation of enhancers. Int. J. Cancer. 2020; 1– 15. https://doi.org/10.1002/ijc.33132
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Its difficult to find eRNAs through RNA-Seq data. The eRNAs are transcribed at very low levels and are usually do not contains polyA tails. You need to use something like CAGE-Seq to find eRNAs.
I have improved my answer below by adding a table of all possible methods, which include what both geek_y and I have been saying.
Just check this pipeline for enhancer RNA quantification from RNA-seq,
http://fun-science.club/PET/
You can also check the paper: Wu, Y, Yang, Y, Gu, H, et al. Multi‐omics analysis reveals the functional transcription and potential translation of enhancers. Int. J. Cancer. 2020; 1– 15. https://doi.org/10.1002/ijc.33132