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 Characterization of the Drosophila protein arginine methyltransferases DART1 and DART4

2004 ◽  
Vol 379 (2) ◽  
pp. 283-289 ◽  
Author(s):  
Marie-Chloé BOULANGER ◽  
Tina Branscombe MIRANDA ◽  
Steven CLARKE ◽  
Marco di FRUSCIO ◽  
Beat SUTER ◽  
...  
Keyword(s):  
Rna Binding ◽  
Developmental Stages ◽  
Rna Binding Proteins ◽  
Genetic System ◽  
Arginine Methylation ◽  
Type I ◽  
Protein Arginine Methyltransferases ◽  
Arginine Residues ◽  
Drosophila Protein

The role of arginine methylation in Drosophila melanogaster is unknown. We identified a family of nine PRMTs (protein arginine methyltransferases) by sequence homology with mammalian arginine methyltransferases, which we have named DART1 to DART9 (Drosophilaarginine methyltransferases 1–9). In keeping with the mammalian PRMT nomenclature, DART1, DART4, DART5 and DART7 are the putative homologues of PRMT1, PRMT4, PRMT5 and PRMT7. Other DART family members have a closer resemblance to PRMT1, but do not have identifiable homologues. All nine genes are expressed in Drosophila at various developmental stages. DART1 and DART4 have arginine methyltransferase activity towards substrates, including histones and RNA-binding proteins. Amino acid analysis of the methylated arginine residues confirmed that both DART1 and DART4 catalyse the formation of asymmetrical dimethylated arginine residues and they are type I arginine methyltransferases. The presence of PRMTs in D. melanogaster suggest that flies are a suitable genetic system to study arginine methylation.

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 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 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 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.

Rapid and systematic analysis of the RNA recognition specificities of RNA-binding proteins

2009 ◽  
Vol 27 (7) ◽  
pp. 667-670 ◽  
Author(s):  
Debashish Ray ◽  
Hilal Kazan ◽  
Esther T Chan ◽  
Lourdes Peña Castillo ◽  
Sidharth Chaudhry ◽  
...  
Keyword(s):  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Rna Recognition ◽  
Systematic Analysis
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