scholarly journals Identification of In Vivo, Conserved, TAF15 RNA Binding Sites Reveals the Impact of TAF15 on the Neuronal Transcriptome

Cell Reports ◽  
2013 ◽  
Vol 3 (2) ◽  
pp. 301-308 ◽  
Author(s):  
Fadia Ibrahim ◽  
Manolis Maragkakis ◽  
Panagiotis Alexiou ◽  
Margaret A. Maronski ◽  
Marc A. Dichter ◽  
...  
Keyword(s):  
Binding Sites ◽  
Rna Binding ◽  
The Impact ◽  
Rna Binding Sites

scholarly journals Mapping of In Vivo RNA-Binding Sites by Ultraviolet (UV)-Cross-Linking Immunoprecipitation (CLIP)

2018 ◽  
Vol 2018 (12) ◽  
pp. pdb.top097931 ◽  
Author(s):  
Jennifer C. Darnell ◽  
Aldo Mele ◽  
Ka Ying Sharon Hung ◽  
Robert B. Darnell
Keyword(s):  
Binding Sites ◽  
Rna Binding ◽  
Cross Linking ◽  
Rna Binding Sites

scholarly journals Multi-resBind: a residual network-based multi-label classifier for in vivo RNA binding prediction and preference visualization

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Shitao Zhao ◽  
Michiaki Hamada
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Binding Sites ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Rna Binding ◽  
Binding Prediction ◽  
Binding Preference ◽  
The Impact

Abstract Background Protein-RNA interactions play key roles in many processes regulating gene expression. To understand the underlying binding preference, ultraviolet cross-linking and immunoprecipitation (CLIP)-based methods have been used to identify the binding sites for hundreds of RNA-binding proteins (RBPs) in vivo. Using these large-scale experimental data to infer RNA binding preference and predict missing binding sites has become a great challenge. Some existing deep-learning models have demonstrated high prediction accuracy for individual RBPs. However, it remains difficult to avoid significant bias due to the experimental protocol. The DeepRiPe method was recently developed to solve this problem via introducing multi-task or multi-label learning into this field. However, this method has not reached an ideal level of prediction power due to the weak neural network architecture. Results Compared to the DeepRiPe approach, our Multi-resBind method demonstrated substantial improvements using the same large-scale PAR-CLIP dataset with respect to an increase in the area under the receiver operating characteristic curve and average precision. We conducted extensive experiments to evaluate the impact of various types of input data on the final prediction accuracy. The same approach was used to evaluate the effect of loss functions. Finally, a modified integrated gradient was employed to generate attribution maps. The patterns disentangled from relative contributions according to context offer biological insights into the underlying mechanism of protein-RNA interactions. Conclusions Here, we propose Multi-resBind as a new multi-label deep-learning approach to infer protein-RNA binding preferences and predict novel interactions. The results clearly demonstrate that Multi-resBind is a promising tool to predict unknown binding sites in vivo and gain biology insights into why the neural network makes a given prediction.

scholarly journals ‘Read–through marking’ reveals differential nucleotide composition of read-through and truncated cDNAs in iCLIP

2018 ◽  
Vol 3 ◽  
pp. 77 ◽  
Author(s):  
Ina Huppertz ◽  
Nejc Haberman ◽  
Jernej Ule
Keyword(s):  
Reverse Transcription ◽  
Binding Sites ◽  
Rna Binding ◽  
Nucleotide Composition ◽  
Peptide Complex ◽  
Rna Fragments ◽  
The Impact ◽  
Rna Binding Sites

We established a modified iCLIP protocol, called ‘read-through marking’, which facilitates the detection of cDNAs that have not been truncated upon encountering the RNA–peptide complex during reverse transcription (read-through cDNAs). A large proportion of these cDNAs would be undesirable in an iCLIP library, as it could affect the resolution of the method. To this end, we added an oligonucleotide to the 5’-end of RNA fragments—a 5’-marker—to mark the read-through cDNAs. By applying this modified iCLIP protocol to PTBP1 and eIF4A3, we found that the start sites of read-through cDNAs are enriched in adenosines, while the remaining cDNAs have a markedly different sequence content at their starts, preferentially containing thymidines. This finding in turn indicates that most of the reads in our iCLIP libraries are a product of truncation with valuable information regarding the proteins’ RNA-binding sites. Thus, cDNA start sites confidently identify a protein’s RNA-crosslink sites and we can account for the impact of read-through cDNAs by commonly adding a 5’-marker.

CSBPI_Site：Multi-Information Sources of Features to RNA Binding Sites Prediction

2021 ◽  
Vol 15 ◽  
Author(s):  
Lichao Zhang ◽  
Zihong Huang ◽  
Liang Kong
Keyword(s):  
Computational Model ◽  
Binding Sites ◽  
Noncoding Rna ◽  
Computational Models ◽  
Information Sources ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Gradient Boosting ◽  
Position Information ◽  
Rna Binding Sites

Background: RNA-binding proteins establish posttranscriptional gene regulation by coordinating the maturation, editing, transport, stability, and translation of cellular RNAs. The immunoprecipitation experiments could identify interaction between RNA and proteins, but they are limited due to the experimental environment and material. Therefore, it is essential to construct computational models to identify the function sites. Objective: Although some computational methods have been proposed to predict RNA binding sites, the accuracy could be further improved. Moreover, it is necessary to construct a dataset with more samples to design a reliable model. Here we present a computational model based on multi-information sources to identify RNA binding sites. Method: We construct an accurate computational model named CSBPI_Site, based on xtreme gradient boosting. The specifically designed 15-dimensional feature vector captures four types of information (chemical shift, chemical bond, chemical properties and position information). Results: The satisfied accuracy of 0.86 and AUC of 0.89 were obtained by leave-one-out cross validation. Meanwhile, the accuracies were slightly different (range from 0.83 to 0.85) among three classifiers algorithm, which showed the novel features are stable and fit to multiple classifiers. These results showed that the proposed method is effective and robust for noncoding RNA binding sites identification. Conclusion: Our method based on multi-information sources is effective to represent the binding sites information among ncRNAs. The satisfied prediction results of Diels-Alder riboz-yme based on CSBPI_Site indicates that our model is valuable to identify the function site.

scholarly journals Predicting RNA-binding sites of proteins using support vector machines and evolutionary information

2008 ◽  
Vol 9 (Suppl 12) ◽  
pp. S6 ◽  
Author(s):  
Cheng-Wei Cheng ◽  
Emily Su ◽  
Jenn-Kang Hwang ◽  
Ting-Yi Sung ◽  
Wen-Lian Hsu
Keyword(s):  
Support Vector Machines ◽  
Binding Sites ◽  
Rna Binding ◽  
Evolutionary Information ◽  
Support Vector ◽  
Vector Machines ◽  
Rna Binding Sites

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 Crimean-Congo hemorrhagic fever virus nucleocapsid protein harbors distinct RNA-binding sites in the stalk and head domains

2019 ◽  
Vol 294 (13) ◽  
pp. 5023-5037 ◽  
Author(s):  
Subbiah Jeeva ◽  
Sheema Mir ◽  
Adrain Velasquez ◽  
Jacquelyn Ragan ◽  
Aljona Leka ◽  
...  
Keyword(s):  
Binding Sites ◽  
Nucleocapsid Protein ◽  
Rna Binding ◽  
Hemorrhagic Fever ◽  
Fever Virus ◽  
Crimean Congo Hemorrhagic Fever ◽  
Hemorrhagic Fever Virus ◽  
Rna Binding Sites
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