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 A cross-linking approach to map small molecule-RNA binding sites in cells

2019 ◽  
Vol 29 (12) ◽  
pp. 1532-1536 ◽  
Author(s):  
Sai Pradeep Velagapudi ◽  
Yue Li ◽  
Matthew D. Disney
Keyword(s):  
Small Molecule ◽  
Binding Sites ◽  
Rna Binding ◽  
Cross Linking ◽  
In Cells ◽  
Rna Binding Sites

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

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

Follicular lymphoma cases harbour recurrent mutations in micro-RNA binding sites of genes associated with lymphomagenesis

2016 ◽  
Vol 61 ◽  
pp. S11-S12
Author(s):  
E. Larrea ◽  
M. Fernandez-Mercado ◽  
I. Ceberio ◽  
J.A. Guerra-Assunção ◽  
J. Okosun ◽  
...  
Keyword(s):  
Follicular Lymphoma ◽  
Binding Sites ◽  
Rna Binding ◽  
Micro Rna ◽  
Recurrent Mutations ◽  
Rna Binding Sites
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