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CLIP-Seq

High-throughput sequencing of RNA isolated by crosslinking immunoprecipitation (HITS-CLIP, also known as CLIP-Seq) is a genome-wide means of mapping protein–RNA binding sites or RNA modification sites in vivo. [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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POSTAR

Submitted by ChenLiang on Sun, 09/10/2017 - 20:12

We present POSTAR (http://POSTAR.ncrnalab.org), a resource of POST-trAnscriptional Regulation coordinated by RNA-binding proteins (RBPs). Precise characterization of post-transcriptional regulatory maps has accelerated dramatically in the past few years. Based on new studies and resources, POSTAR supplies the largest collection of experimentally probed (~23 million) and computationally predicted (approximately 117 million) RBP binding sites in the human and mouse transcriptomes.

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miRBShunter

Submitted by ChenLiang on Sun, 09/10/2017 - 20:25

Experimental evidence indicates that about 60% of miRNA-binding activity does not follow the canonical rule about the seed matching between miRNA and target mRNAs, but rather a non-canonical miRNA targeting activity outside the seed or with a seed-like motifs. Here, we propose a new unbiased method to identify canonical and non-canonical miRNA-binding sites from peaks identified by Ago2 Cross-Linked ImmunoPrecipitation associated to high-throughput sequencing (CLIP-seq).

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chimiRic

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

Recent technologies like AGO CLIP sequencing and CLASH enable direct transcriptome-wide identification of AGO binding and miRNA target sites, but the most widely used miRNA target prediction algorithms do not exploit these data. Here we use discriminative learning on AGO CLIP and CLASH interactions to train a novel miRNA target prediction model. Our method combines two SVM classifiers, one to predict miRNA-mRNA duplexes and a second to learn a binding model of AGO's local UTR sequence preferences and positional bias in 3'UTR isoforms.

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miRTar2GO

Submitted by ChenLiang on Mon, 01/09/2017 - 11:14

MicroRNAs (miRNAs) are ∼19-22 nucleotides (nt) long regulatory RNAs that regulate gene expression by recognizing and binding to complementary sequences on mRNAs. The key step in revealing the function of a miRNA, is the identification of miRNA target genes. Recent biochemical advances including PAR-CLIP and HITS-CLIP allow for improved miRNA target predictions and are widely used to validate miRNA targets.

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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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CCmiR

Submitted by ChenLiang on Tue, 01/09/2018 - 17:39

The identification of microRNA (miRNA) target sites is important. In the past decade, dozens of computational methods have been developed to predict miRNA target sites. Despite their existence, rarely does a method consider the well-known competition and cooperation among miRNAs when attempts to discover target sites. To fill this gap, we developed a new approach called CCmiR, which takes the cooperation and competition of multiple miRNAs into account in a statistical model to predict their target sites.

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