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RNA-binding Protein (RBP)

RNA-binding proteins (often abbreviated as RBPs) are proteins that bind to the double or single stranded RNA[1] in cells and participate in forming ribonucleoprotein complexes. RBPs contain various structural motifs, such as RNA recognition motif (RRM), dsRNA binding domain, zinc finger and others. [Source: Wikipedia]

Seten

Submitted by ChenLiang on Thu, 04/06/2017 - 19:04

RNA-binding proteins (RBPs) control the regulation of gene expression in eukaryotic genomes at post-transcriptional level by binding to their cognate RNAs. Although several variants of CLIP (crosslinking and immunoprecipitation) protocols are currently available to study the global protein-RNA interaction landscape at single nucleotide resolution in a cell, currently there are very few tools which can facilitate understanding and dissecting the functional associations of RBPs from the resulting binding maps.

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iDeep

Submitted by ChenLiang on Sun, 09/10/2017 - 17:07

RNAs play key roles in cells through the interactions with proteins known as the RNA-binding proteins (RBP) and their binding motifs enable crucial understanding of the post-transcriptional regulation of RNAs. How the RBPs correctly recognize the target RNAs and why they bind specific positions is still far from clear. Machine learning-based algorithms are widely acknowledged to be capable of speeding up this process. Although many automatic tools have been developed to predict the RNA-protein binding sites from the rapidly growing multi-resource data, e.g.

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MotifMap-RNA

Submitted by ChenLiang on Sun, 09/10/2017 - 17:15

RNA plays a critical role in gene expression and its regulation. RNA binding proteins (RBPs), in turn, are important regulators of RNA. Thanks to the availability of large scale data for RBP binding motifs and in vivo binding sites results in the form of eCLIP experiments, it is now possible to computationally predict RBP binding sites across the whole genome.
We describe MotifMap-RNA, an extension of MotifMap which predicts binding sites for RBP motifs across human and mouse genomes and allows large scale querying of predicted binding sites.

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dreamBase

Submitted by ChenLiang on Tue, 01/09/2018 - 17:49

Although thousands of pseudogenes have been annotated in the human genome, their transcriptional regulation, expression profiles and functional mechanisms are largely unknown. In this study, we developed dreamBase (http://rna.sysu.edu.cn/dreamBase) to facilitate the investigation of DNA modification, RNA regulation and protein binding of potential expressed pseudogenes from multidimensional high-throughput sequencing data.

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deepboost

Submitted by ChenLiang on Sun, 09/10/2017 - 16:53

Characterizing the binding behaviors of RNA-binding proteins (RBPs) is important for understanding their functional roles in gene expression regulation. However, current high-throughput experimental methods for identifying RBP targets, such as CLIP-seq and RNAcompete, usually suffer from the false negative issue. Here, we develop a deep boosting based machine learning approach, called DeBooster, to accurately model the binding sequence preferences and identify the corresponding binding targets of RBPs from CLIP-seq data.

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