Profiling the Binding Sites of RNA-Binding Proteins with Nucleotide Resolution Using iCLIP

2016 ◽  
pp. 175-195 ◽  
Author(s):  
FX Reymond Sutandy ◽  
Andrea Hildebrandt ◽  
Julian König
Keyword(s):  
Binding Sites ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Nucleotide Resolution

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 SSMART: Sequence-structure motif identification for RNA-binding proteins

2018 ◽  
Author(s):  
Alina Munteanu ◽  
Neelanjan Mukherjee ◽  
Uwe Ohler
Keyword(s):  
Binding Sites ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Sequence Motif ◽  
Primary Sequence ◽  
Sequence Structure ◽  
Rna Targets

AbstractMotivationRNA-binding proteins (RBPs) regulate every aspect of RNA metabolism and function. There are hundreds of RBPs encoded in the eukaryotic genomes, and each recognize its RNA targets through a specific mixture of RNA sequence and structure properties. For most RBPs, however, only a primary sequence motif has been determined, while the structure of the binding sites is uncharacterized.ResultsWe developed SSMART, an RNA motif finder that simultaneously models the primary sequence and the structural properties of the RNA targets sites. The sequence-structure motifs are represented as consensus strings over a degenerate alphabet, extending the IUPAC codes for nucleotides to account for secondary structure preferences. Evaluation on synthetic data showed that SSMART is able to recover both sequence and structure motifs implanted into 3‘UTR-like sequences, for various degrees of structured/unstructured binding sites. In addition, we successfully used SSMART on high-throughput in vivo and in vitro data, showing that we not only recover the known sequence motif, but also gain insight into the structural preferences of the RBP.AvailabilitySSMART is freely available at https://ohlerlab.mdc-berlin.de/software/SSMART_137/[email protected]

scholarly journals Genomic and cDNA selection-amplification identifies transcriptome-wide binding sites for the Drosophila protein sex-lethal

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0250592
Author(s):  
Hiren Banerjee ◽  
Ravinder Singh
Keyword(s):  
Binding Sites ◽  
Binding Proteins ◽  
Rna Binding ◽  
Developmental Stages ◽  
Rna Binding Proteins ◽  
Systematic Analysis ◽  
Downstream Targets ◽  
Drosophila Protein ◽  
Sex Lethal ◽  
Female Specific

Background Downstream targets for a large number of RNA-binding proteins remain to be identified. The Drosophila master sex-switch protein Sex-lethal (SXL) is an RNA-binding protein that controls splicing, polyadenylation, or translation of certain mRNAs to mediate female-specific sexual differentiation. Whereas some targets of SXL are known, previous studies indicate that additional targets of SXL have escaped genetic screens. Methodology/Principal findings Here, we have used an alternative molecular approach of GEnomic Selective Enrichment of Ligands by Exponential enrichment (GESELEX) using both the genomic DNA and cDNA pools from several Drosophila developmental stages to identify new potential targets of SXL. Our systematic analysis provides a comprehensive view of the Drosophila transcriptome for potential SXL-binding sites. Conclusion/Significance We have successfully identified new SXL-binding sites in the Drosophila transcriptome. We discuss the significance of our analysis and that the newly identified binding sites and sequences could serve as a useful resource for the research community. This approach should also be applicable to other RNA-binding proteins for which downstream targets are unknown.

scholarly journals Photo-cross-linking and high-resolution mass spectrometry for assignment of RNA-binding sites in RNA-binding proteins

2014 ◽  
Vol 11 (10) ◽  
pp. 1064-1070 ◽  
Author(s):  
Katharina Kramer ◽  
Timo Sachsenberg ◽  
Benedikt M Beckmann ◽  
Saadia Qamar ◽  
Kum-Loong Boon ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
High Resolution ◽  
Binding Sites ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
High Resolution Mass Spectrometry ◽  
Cross Linking ◽  
High Resolution Mass ◽  
Resolution Mass

scholarly journals GraphProt2: A novel deep learning-based method for predicting binding sites of RNA-binding proteins

2019 ◽  
Author(s):  
Michael Uhl ◽  
Van Dinh Tran ◽  
Rolf Backofen
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Deep Learning ◽  
Binding Sites ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
State Of The Art ◽  
Prediction Method ◽  
Superior Performance

AbstractCLIP-seq is the state-of-the-art technique to experimentally determine transcriptome-wide binding sites of RNA-binding proteins (RBPs). However, it relies on gene expression which can be highly variable between conditions, and thus cannot provide a complete picture of the RBP binding landscape. This necessitates the use of computational methods to predict missing binding sites. Here we present GraphProt2, a computational RBP binding site prediction method based on graph convolutional neural networks (GCN). In contrast to current CNN methods, GraphProt2 supports variable length input as well as the possibility to accurately predict nucleotide-wise binding profiles. We demonstrate its superior performance compared to GraphProt and a CNN-based method on single as well as combined CLIP-seq datasets.

scholarly journals Identifying the sequence specificities of circRNA-binding proteins based on a capsule network architecture

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Zhengfeng Wang ◽  
Xiujuan Lei
Keyword(s):  
Binding Sites ◽  
Network Architecture ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Circular Rnas ◽  
Sequence Motifs ◽  
Rna Motifs ◽  
Classification Framework ◽  
Bound Sequence

Abstract Background Circular RNAs (circRNAs) are widely expressed in cells and tissues and are involved in biological processes and human diseases. Recent studies have demonstrated that circRNAs can interact with RNA-binding proteins (RBPs), which is considered an important aspect for investigating the function of circRNAs. Results In this study, we design a slight variant of the capsule network, called circRB, to identify the sequence specificities of circRNAs binding to RBPs. In this model, the sequence features of circRNAs are extracted by convolution operations, and then, two dynamic routing algorithms in a capsule network are employed to discriminate between different binding sites by analysing the convolution features of binding sites. The experimental results show that the circRB method outperforms the existing computational methods. Afterwards, the trained models are applied to detect the sequence motifs on the seven circRNA-RBP bound sequence datasets and matched to known human RNA motifs. Some motifs on circular RNAs overlap with those on linear RNAs. Finally, we also predict binding sites on the reported full-length sequences of circRNAs interacting with RBPs, attempting to assist current studies. We hope that our model will contribute to better understanding the mechanisms of the interactions between RBPs and circRNAs. Conclusion In view of the poor studies about the sequence specificities of circRNA-binding proteins, we designed a classification framework called circRB based on the capsule network. The results show that the circRB method is an effective method, and it achieves higher prediction accuracy than other methods.

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