Enhanced Identification of Transcriptional Enhancers Provides Mechanistic Insights into Diseases

Murakawa, Yuji; Yoshihara, Masahito; Kawaji, Hideya; Nishikawa, Miki; Zayed, Hatem; Suzuki, Harukazu; Hayashizaki, Yoshihide

doi:10.1016/j.tig.2015.11.004

Cited by 83 publications

(77 citation statements)

References 101 publications

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“…Therefore, a more robust method to identify eRNAs requires the capture of pioneering rounds of transcription using techniques such as global nuclear run-on sequencing (GRO-seq) [18] and precision nuclear run-on sequencing (PRO-seq) [19], [20]. Additionally, a sensitive way to detect eRNAs is cap analysis of gene expression (CAGE) sequencing [21]. This method was used by the FANTOM consortium to profile the transcriptomes of a large panel of human tissues and cell types, from which 43,011 enhancer elements were shown to be transcribed to eRNAs [22].…”

Section: How Are Ernas Detected?mentioning

confidence: 99%

Enhancer-Derived RNA: A Primer

Liu

2017

Genomics, Proteomics &Amp; Bioinformatics

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Enhancer-derived RNAs (eRNAs) are a group of RNAs transcribed by RNA polymerase II from the domain of transcription enhancers, a major type of cis-regulatory elements in the genome. The correlation between eRNA production and enhancer activity has stimulated studies on the potential role of eRNAs in transcriptional regulation. Additionally, eRNA has also served as a marker for global identification of enhancers. Here I review the brief history and fascinating properties of eRNAs.

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Section: How Are Ernas Detected?mentioning

confidence: 99%

Enhancer-Derived RNA: A Primer

Liu

2017

Genomics, Proteomics &Amp; Bioinformatics

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“…More recently, several predictive studies have been carried out to determine enhancer to gene links genome-wide, some utilizing a combination of several approaches (27, 28). Thus, eRNAs have been shown to be significantly co-expressed with the promoters that they regulate (22, 29). In parallel, chromosome conformation capture methodologies may be used to detect the proximity of an enhancer and its target gene, stemming from the physical looping of DNA (30).…”

Section: Introductionmentioning

confidence: 99%

GeneHancer: genome-wide integration of enhancers and target genes in GeneCards

et al. 2017

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A major challenge in understanding gene regulation is the unequivocal identification of enhancer elements and uncovering their connections to genes. We present GeneHancer, a novel database of human enhancers and their inferred target genes, in the framework of GeneCards. First, we integrated a total of 434 000 reported enhancers from four different genome-wide databases: the Encyclopedia of DNA Elements (ENCODE), the Ensembl regulatory build, the functional annotation of the mammalian genome (FANTOM) project and the VISTA Enhancer Browser. Employing an integration algorithm that aims to remove redundancy, GeneHancer portrays 285 000 integrated candidate enhancers (covering 12.4% of the genome), 94 000 of which are derived from more than one source, and each assigned an annotation-derived confidence score. GeneHancer subsequently links enhancers to genes, using: tissue co-expression correlation between genes and enhancer RNAs, as well as enhancer-targeted transcription factor genes; expression quantitative trait loci for variants within enhancers; and capture Hi-C, a promoter-specific genome conformation assay. The individual scores based on each of these four methods, along with gene–enhancer genomic distances, form the basis for GeneHancer’s combinatorial likelihood-based scores for enhancer–gene pairing. Finally, we define ‘elite’ enhancer–gene relations reflecting both a high-likelihood enhancer definition and a strong enhancer–gene association.GeneHancer predictions are fully integrated in the widely used GeneCards Suite, whereby candidate enhancers and their annotations are displayed on every relevant GeneCard. This assists in the mapping of non-coding variants to enhancers, and via the linked genes, forms a basis for variant–phenotype interpretation of whole-genome sequences in health and disease. Database URL: http://www.genecards.org/

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“…TATA box, INR element) are used for anchoring the transcriptional machinery and regulating the initiation of transcription (3). In contrast, enhancers are located few or many thousands base pairs (bp) upstream or downstream from the TSSs and enhance the expression of their target transcripts through interactions with transcription factors (TFs) (or complexes they form) and/or by facilitating chromatin-remodelling activities (4). …”

Section: Introductionmentioning

confidence: 99%

“…Consequently, promoters and enhancers share biochemical and transcriptional properties that have been reported in recent studies (7–9). Thus, considering their increased similarity, it may be difficult to separate effectively these regulatory classes, since some promoters have also enhancer activity (4,9–11). …”

Section: Introductionmentioning

confidence: 99%

Discriminative identification of transcriptional responses of promoters and enhancers after stimulus

Kleftogiannis

Kalnis

Arner³

et al. 2016

Nucleic Acids Res

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Promoters and enhancers regulate the initiation of gene expression and maintenance of expression levels in spatial and temporal manner. Recent findings stemming from the Cap Analysis of Gene Expression (CAGE) demonstrate that promoters and enhancers, based on their expression profiles after stimulus, belong to different transcription response subclasses. One of the most promising biological features that might explain the difference in transcriptional response between subclasses is the local chromatin environment. We introduce a novel computational framework, PEDAL, for distinguishing effectively transcriptional profiles of promoters and enhancers using solely histone modification marks, chromatin accessibility and binding sites of transcription factors and co-activators. A case study on data from MCF-7 cell-line reveals that PEDAL can identify successfully the transcription response subclasses of promoters and enhancers from two different stimulations. Moreover, we report subsets of input markers that discriminate with minimized classification error MCF-7 promoter and enhancer transcription response subclasses. Our work provides a general computational approach for identifying effectively cell-specific and stimulation-specific promoter and enhancer transcriptional profiles, and thus, contributes to improve our understanding of transcriptional activation in human.

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Enhanced Identification of Transcriptional Enhancers Provides Mechanistic Insights into Diseases

Cited by 83 publications

References 101 publications

Enhancer-Derived RNA: A Primer

Enhancer-Derived RNA: A Primer

GeneHancer: genome-wide integration of enhancers and target genes in GeneCards

Discriminative identification of transcriptional responses of promoters and enhancers after stimulus

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