scholarly journals StoatyDive: Evaluation and classification of peak profiles for sequencing data

GigaScience ◽  
2021 ◽  
Vol 10 (6) ◽  
Author(s):  
Florian Heyl ◽  
Rolf Backofen
Keyword(s):  
Quality Control ◽  
High Throughput ◽  
Binding Sites ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Typical Result ◽  
Sequence Motif ◽  
Peak Shape ◽  
Sequencing Data

Abstract Background The prediction of binding sites (peak-calling) is a common task in the data analysis of methods such as cross-linking immunoprecipitation in combination with high-throughput sequencing (CLIP-Seq). The predicted binding sites are often further analyzed to predict sequence motifs or structure patterns. When looking at a typical result of such high-throughput experiments, the obtained peak profiles differ largely on a genomic level. Thus, a tool is missing that evaluates and classifies the predicted peaks on the basis of their shapes. We hereby present StoatyDive, a tool that can be used to filter for specific peak profile shapes of sequencing data such as CLIP. Findings With StoatyDive we are able to classify peak profile shapes from CLIP-seq data of the histone stem-loop-binding protein (SLBP). We compare the results to existing tools and show that StoatyDive finds more distinct peak shape clusters for CLIP data. Furthermore, we present StoatyDive’s capabilities as a quality control tool and as a filter to pick different shapes based on biological or technical questions for other CLIP data from different RNA binding proteins with different biological functions and numbers of RNA recognition motifs. We finally show that proteins involved in splicing, such as RBM22 and U2AF1, have potentially sharper-shaped peaks than other RNA binding proteins. Conclusion StoatyDive finally fills the demand for a peak shape clustering tool for CLIP-Seq data that fine-tunes downstream analysis steps such as structure or sequence motif predictions and that acts as a quality control.

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 FLASH: ultra-fast protocol to identify RNA–protein interactions in cells

2019 ◽  
Vol 48 (3) ◽  
pp. e15-e15 ◽  
Author(s):  
Ibrahim Avsar Ilik ◽  
Tugce Aktas ◽  
Daniel Maticzka ◽  
Rolf Backofen ◽  
Asifa Akhtar
Keyword(s):  
High Throughput ◽  
Protein Interactions ◽  
Binding Sites ◽  
Binding Proteins ◽  
High Throughput Sequencing ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Affinity Purification ◽  
Sequencing Library

Abstract Determination of the in vivo binding sites of RNA-binding proteins (RBPs) is paramount to understanding their function and how they affect different aspects of gene regulation. With hundreds of RNA-binding proteins identified in human cells, a flexible, high-resolution, high-throughput, highly multiplexible and radioactivity-free method to determine their binding sites has not been described to date. Here we report FLASH (Fast Ligation of RNA after some sort of Affinity Purification for High-throughput Sequencing), which uses a special adapter design and an optimized protocol to determine protein–RNA interactions in living cells. The entire FLASH protocol, starting from cells on plates to a sequencing library, takes 1.5 days. We demonstrate the flexibility, speed and versatility of FLASH by using it to determine RNA targets of both tagged and endogenously expressed proteins under diverse conditions in vivo.

scholarly journals RBPTD: a database of cancer-related RNA-binding proteins in humans

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Kun Li ◽  
Zhi-Wei Guo ◽  
Xiang-Ming Zhai ◽  
Xue-Xi Yang ◽  
Ying-Song Wu ◽  
...  
Keyword(s):  
High Throughput ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Sequencing Data ◽  
Dna Copy Number Variation ◽  
Expression Of Genes ◽  
Number Variation

Abstract RNA-binding proteins (RBPs) play important roles in regulating the expression of genes involved in human physiological and pathological processes, especially in cancers. Many RBPs have been found to be dysregulated in cancers; however, there was no tool to incorporate high-throughput data from different dimensions to systematically identify cancer-related RBPs and to explore their causes of abnormality and their potential functions. Therefore, we developed a database named RBPTD to identify cancer-related RBPs in humans and systematically explore their functions and abnormalities by integrating different types of data, including gene expression profiles, prognosis data and DNA copy number variation (CNV), among 28 cancers. We found a total of 454 significantly differentially expressed RBPs, 1970 RBPs with significant prognostic value, and 53 dysregulated RBPs correlated with CNV abnormality. Functions of 26 cancer-related RBPs were explored by analysing high-throughput RNA sequencing data obtained by crosslinking immunoprecipitation, and the remaining RBP functions were predicted by calculating their correlation coefficient with other genes. Finally, we developed the RBPTD for users to explore functions and abnormalities of cancer-related RBPs to improve our understanding of their roles in tumorigenesis. Database URL: http: //www.rbptd.com

scholarly journals Functional characterization of splicing regulatory elements

2021 ◽  
Author(s):  
Scott I Adamson ◽  
Lijun Zhan ◽  
Brenton R Graveley
Keyword(s):  
High Throughput ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Functional Characterization ◽  
Regulatory Elements ◽  
Small Subset ◽  
General Sequence ◽  
Splicing Regulators ◽  
Splicing Regulatory Elements

Background: RNA binding protein-RNA interactions mediate a variety of processes including pre-mRNA splicing, translation, decay, polyadenylation and many others. Previous high-throughput studies have characterized general sequence features associated with increased and decreased splicing of certain exons, but these studies are limited by not knowing the mechanisms, and in particular, the mediating RNA binding proteins, underlying these associations. Results: Here we utilize ENCODE data from diverse data modalities to identify functional splicing regulatory elements and their associated RNA binding proteins. We identify features which make splicing events more sensitive to depletion of RNA binding proteins, as well as which RNA binding proteins act as splicing regulators sensitive to depletion. To analyze the sequence determinants underlying RBP-RNA interactions impacting splicing, we assay tens of thousands of sequence variants in a high-throughput splicing reporter called Vex-seq and confirm a small subset in their endogenous loci using CRISPR base editors. Finally, we leverage other large transcriptomic datasets to confirm the importance of RNA binding proteins which we designed experiments around and identify additional RBPs which may act as additional splicing regulators of the exons studied. Conclusions: This study identifies sequence and other features underlying splicing regulation mediated specific RNA binding proteins, as well as validates and identifies other potentially important regulators of splicing in other large transcriptomic datasets.

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 A Deep Boosting Based Approach for Capturing the Sequence Binding Preferences of RNA-Binding Proteins from High-Throughput CLIP-Seq Data

2016 ◽  
Author(s):  
Shuya Li ◽  
Fanghong Dong ◽  
Yuexin Wu ◽  
Sai Zhang ◽  
Chen Zhang ◽  
...  
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Binding Proteins ◽  
Rna Binding ◽  
Gene Expression Regulation ◽  
Rna Binding Proteins ◽  
False Negative ◽  
Functional Roles ◽  
Potential Applications ◽  
Validation Tests

AbstractCharacterizing 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 positive and false negative issues. 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. Comprehensive validation tests have shown that DeBooster can outperform other state-of-the-art approaches in predicting RBP targets and recover false negatives that are common in current CLIP-seq data. In addition, we have demonstrated several new potential applications of DeBooster in understanding the regulatory functions of RBPs, including the binding effects of the RNA helicase MOV10 on mRNA degradation, the influence of different binding behaviors of the ADAR proteins on RNA editing, as well as the antagonizing effect of RBP binding on miRNA repression. Moreover, DeBooster may provide an effective index to investigate the effect of pathogenic mutations in RBP binding sites, especially those related to splicing events. We expect that DeBooster will be widely applied to analyze large-scale CLIP-seq experimental data and can provide a practically useful tool for novel biological discoveries in understanding the regulatory mechanisms of RBPs.

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