scholarly journals RBPSponge: genome-wide identification of lncRNAs that sponge RBPs

2019 ◽  
Author(s):  
Saber HafezQorani ◽  
Aissa Houdjedj ◽  
Mehmet Arici ◽  
Abdesselam Said ◽  
Hilal Kazan
Keyword(s):  
Binding Sites ◽  
Regulatory Network ◽  
Target Genes ◽  
Rna Binding ◽  
Rna Binding Protein ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Web Tool ◽  
Genome Wide ◽  
The Web

AbstractSummaryLong noncoding RNAs (lncRNAs) can act as molecular sponges or decoys for an RNA-binding protein (RBP) through their RBP binding sites, thereby modulating the expression of all target genes of the corresponding RBP of interest. Here, we present a web tool named RBPSponge to explore lncRNAs based on their potential to act as a sponge for an RBP of interest. RBPSponge identifies the occurrences of RBP binding sites and CLIP peaks on lncRNAs, and enables users to run statistical analyses to investigate the regulatory network between lncRNAs, RBPs and targets of RBPs.AvailabilityThe web server is available athttps://[email protected]

scholarly journals RBPSponge: genome-wide identification of lncRNAs that sponge RBPs

2019 ◽  
Vol 35 (22) ◽  
pp. 4760-4763 ◽  
Author(s):  
Saber HafezQorani ◽  
Aissa Houdjedj ◽  
Mehmet Arici ◽  
Abdesselam Said ◽  
Hilal Kazan
Keyword(s):  
Binding Sites ◽  
Regulatory Network ◽  
Target Genes ◽  
Rna Binding ◽  
Rna Binding Protein ◽  
Supplementary Information ◽  
Web Tool ◽  
Genome Wide ◽  
Non Coding Rnas ◽  
The Web

Abstract Summary Long non-coding RNAs (lncRNAs) can act as molecular sponge or decoys for an RNA-binding protein (RBP) through their RBP-binding sites, thereby modulating the expression of all target genes of the corresponding RBP of interest. Here, we present a web tool named RBPSponge to explore lncRNAs based on their potential to act as a sponge for an RBP of interest. RBPSponge identifies the occurrences of RBP-binding sites and CLIP peaks on lncRNAs, and enables users to run statistical analyses to investigate the regulatory network between lncRNAs, RBPs and targets of RBPs. Availability and implementation The web server is available at https://www.RBPSponge.com. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Deep learning identifies genome-wide DNA binding sites of long noncoding RNAs

RNA Biology ◽  
2018 ◽  
Vol 15 (12) ◽  
pp. 1468-1476 ◽  
Author(s):  
Fan Wang ◽  
Pranik Chainani ◽  
Tommy White ◽  
Jin Yang ◽  
Yu Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dna Binding ◽  
Binding Sites ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Dna Binding Sites ◽  
Genome Wide

scholarly journals Genomic Analyses of the RNA-binding Protein Hu Antigen R (HuR) Identify a Complex Network of Target Genes and Novel Characteristics of Its Binding Sites

2011 ◽  
Vol 286 (43) ◽  
pp. 37063-37066 ◽  
Author(s):  
Philip J. Uren ◽  
Suzanne C. Burns ◽  
Jianhua Ruan ◽  
Kusum K. Singh ◽  
Andrew D. Smith ◽  
...  
Keyword(s):  
Complex Network ◽  
Binding Sites ◽  
Target Genes ◽  
Rna Binding ◽  
Binding Protein ◽  
Rna Binding Protein ◽  
Genomic Analyses ◽  
Hu Antigen R

scholarly journals Identification of key tissue-specific, biological processes by integrating enhancer information in maize gene regulatory networks

2020 ◽  
Author(s):  
Maud Fagny ◽  
Marieke Lydia Kuijjer ◽  
Maike Stam ◽  
Johann Joets ◽  
Olivier Turc ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Binding Sites ◽  
Regulatory Network ◽  
Tissue Specificity ◽  
Target Genes ◽  
Specific Gene ◽  
Tissue Specific ◽  
Genome Wide ◽  
Gene Regulatory

AbstractEnhancers are important regulators of gene expression during numerous crucial processes including tissue differentiation across development. In plants, their recent molecular characterization revealed their capacity to activate the expression of several target genes through the binding of transcription factors. Nevertheless, identifying these target genes at a genome-wide level remains a challenge, in particular in species with large genomes, where enhancers and target genes can be hundreds of kilobases away. Therefore, the contribution of enhancers to regulatory network is still poorly understood in plants. In this study, we investigate the enhancer-driven regulatory network of two maize tissues at different stages: leaves at seedling stage and husks (bracts) at flowering. Using a systems biology approach, we integrate genomic, epigenomic and transcriptomic data to model the regulatory relationship between transcription factors and their potential target genes. We identify regulatory modules specific to husk and V2-IST, and show that they are involved in distinct functions related to the biology of each tissue. We evidence enhancers exhibiting binding sites for two distinct transcription factor families (DOF and AP2/ERF) that drive the tissue-specificity of gene expression in seedling immature leaf and husk. Analysis of the corresponding enhancer sequences reveals that two different transposable element families (TIR transposon Mutator and MITE Pif/Harbinger) have shaped the regulatory network in each tissue, and that MITEs have provided new transcription factor binding sites that are involved in husk tissue-specificity.SignificanceEnhancers play a major role in regulating tissue-specific gene expression in higher eukaryotes, including angiosperms. While molecular characterization of enhancers has improved over the past years, identifying their target genes at the genome-wide scale remains challenging. Here, we integrate genomic, epigenomic and transcriptomic data to decipher the tissue-specific gene regulatory network controlled by enhancers at two different stages of maize leaf development. Using a systems biology approach, we identify transcription factor families regulating gene tissue-specific expression in husk and seedling leaves, and characterize the enhancers likely to be involved. We show that a large part of maize enhancers is derived from transposable elements, which can provide novel transcription factor binding sites crucial to the regulation of tissue-specific biological functions.

scholarly journals SEQing: web-based visualization of iCLIP and RNA-seq data in an interactive python framework

2019 ◽  
Author(s):  
Martin Lewinski ◽  
Yannik Bramkamp ◽  
Tino Köster ◽  
Dorothee Staiger
Keyword(s):  
Binding Sites ◽  
Binding Proteins ◽  
Target Genes ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Rna Seq ◽  
Web Based ◽  
Genome Wide ◽  
Functional Relevance ◽  
Nucleotide Resolution

AbstractBackgroundRNA-binding proteins interact with their target RNAs at specific sites. These binding sites can be determined genome-wide through individual nucleotide resolution crosslinking immunoprecipitation (iCLIP). Subsequently, the binding sites have to be visualized. So far, no visualization tool exists that is easily accessible but also supports restricted access so that data can be shared among collaborators.ResultsHere we present SEQing, a customizable interactive dashboard to visualize crosslink sites on target genes of RNA-binding proteins that have been obtained by iCLIP. Moreover, SEQing supports RNA-seq data that can be displayed in a diffrerent window tab. This allows, e.g. crossreferencing the iCLIP data with genes differentially expressed in mutants of the RBP and thus obtain some insights into a potential functional relevance of the binding sites. Additionally, detailed information on the target genes can be incorporated in another tab.ConclusionSEQing is written in Python3 and runs on Linux. The web-based access makes iCLIP data easily accessible, even with mobile devices. SEQing is customizable in many ways and has also the option to be secured by a password. The source code is available at https://github.com/malewins/SEQing.

scholarly journals Roles of long noncoding RNAs in Hepatocellular Carcinoma

2016 ◽  
Vol 11 (1) ◽  
pp. 91-97 ◽  
Author(s):  
Diyu Huang ◽  
Jie Fang ◽  
Gaojian Luo
Keyword(s):  
Hepatocellular Carcinoma ◽  
Mrna Stability ◽  
Target Genes ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Malignant Tumors ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Aberrant Expression ◽  
Comprehensive Review

AbstractLong noncoding RNAs (lncRNAs) are nonprotein coding transcripts longer than 200 nucleotides. Aberrant expression of lncRNAs has been found to be associated with hepatocellular carcinoma, one of the most malignant tumors. In this paper, we give a systematic and comprehensive review of existing literature about the involvement of lncRNAs in hepatocellular carcinoma. To date, evidence suggests that a number of lncRNAs, including HEIH, H19, HOTAIR, MALAT1, and PVT1, may regulate the transcription of target genes by recruiting histone-modifying complexes. Under certain circumstances, lncRNAs form RNA-dsDNA triplexes. Certain lncRNAs, such as HULC, HOTAIR, H19, HOTTIP and PTENP1, exhibit their biological roles by associating with microRNAs (miRNAs). In addition, by complementary base pairing with mRNAs or forming complexes with RNA binding proteins (RBPs), lncRNA-ATB, MALAT1 and PCNA-AS1 may mediate mRNA stability and splicing. In conclusion, interactions with DNA, RNA and proteins appears to be involved in lncRNAs’ participation in tumorigenesis and developmental processes related to hepatocellular carcinoma.

scholarly journals Genome-Wide Identification of Barley Long Noncoding RNAs and Analysis of Their Regulatory Interactions during Shoot and Grain Development

2021 ◽  
Vol 22 (10) ◽  
pp. 5087
Author(s):  
Sebastian Gasparis ◽  
Mateusz Przyborowski ◽  
Anna Nadolska-Orczyk
Keyword(s):  
Target Genes ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Shoot Apices ◽  
Protein Coding ◽  
Cis Acting ◽  
Rna Molecules ◽  
Functional Studies ◽  
Potential Target ◽  
Genome Wide

Long noncoding RNAs (lncRNAs) are a class of RNA molecules with gene regulatory functions in plant development and the stress response. Although the number of lncRNAs identified in plants is rapidly increasing, very little is known about their role in barley development. In this study, we performed global identification of barley lncRNAs based on 53 RNAseq libraries derived from nine different barley tissues and organs. In total, 17,250 lncRNAs derived from 10,883 loci were identified, including 8954 novel lncRNAs. Differential expression of lncRNAs was observed in the developing shoot apices and grains, the two organs that have a direct influence on the final yield. The regulatory interaction of differentially expressed lncRNAs with the potential target genes was evaluated. We identified 176 cis-acting lncRNAs in shoot apices and 424 in grains, while the number of trans-acting lncRNAs in these organs was 1736 and 540, respectively. The potential target protein-coding genes were identified, and their biological function was annotated using MapMan ontology. This is the first insight into the roles of lncRNAs in barley development on the genome-wide scale, and our results provide a solid background for future functional studies.

scholarly journals Genetic Basis of Alternative Polyadenylation is an Emerging Molecular Phenotype for Human Traits and Diseases

2019 ◽  
Author(s):  
Lei Li ◽  
Yipeng Gao ◽  
Fanglue Peng ◽  
Eric J. Wagner ◽  
Wei Li
Keyword(s):  
Genetic Variants ◽  
Binding Sites ◽  
Genetic Basis ◽  
Target Genes ◽  
Rna Binding ◽  
Rna Binding Protein ◽  
Alternative Polyadenylation ◽  
Molecular Phenotype ◽  
Protein Binding Sites ◽  
Coding Variants

SUMMARYGenome-wide association studies have identified thousands of non-coding variants that are statistically associated with human traits and diseases. However, functional interpretation of these variants remains a major challenge. Here, we describe the first atlas of human 3’-UTR alternative polyadenylation (APA) Quantitative Trait Loci (3’QTLs), i.e. ∼0.4 million genetic variants associated with APA of target genes across 46 Genotype-Tissue Expression (GTEx) tissues from 467 individuals. APA occurs in approximately 70% of human genes and substantively impacts cellular proliferation, differentiation and tumorigenesis. Mechanistically, 3’QTLs could alter polyA motifs and RNA-binding protein binding sites, leading to thousands of APA changes. Importantly, 3’QTLs can be used to interpret ∼16.1% of trait-associated variants and are largely distinct from other QTLs such as eQTLs. The genetic basis of APA (3’QTLs) thus represent a novel molecular phenotype to explain a large fraction of non-coding variants and to provide new insights into complex traits and disease etiologies.HighlightsThe first atlas of human 3’QTLs: ∼0.4 million genetic variants associated with alternative polyadenylation of target genes across 46 tissues from 467 individuals3’QTLs could alter polyA motifs and RNA-binding protein binding sites3’QTLs can be used to interpret ∼16.1% of trait-associated variantsMany disease-associated 3’QTLs contribute to phenotype independent of gene expression

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