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 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 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 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.

scholarly journals The YTHDF proteins ECT2 and ECT3 bind largely overlapping target sets and influence target mRNA abundance, not alternative polyadenylation

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Laura Arribas-Hernández ◽  
Sarah Rennie ◽  
Michael Schon ◽  
Carlotta Porcelli ◽  
Balaji Enugutti ◽  
...  
Keyword(s):  
Indirect Effects ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Expression Profiles ◽  
Alternative Polyadenylation ◽  
Target Mrna ◽  
Target Mrnas ◽  
Yth Domain ◽  
Target Sets

Gene regulation via N6-methyladenosine (m6A) in mRNA involves RNA-binding proteins that recognize m6A via a YT521-B homology (YTH) domain. The plant YTH domain proteins ECT2 and ECT3 act genetically redundantly in stimulating cell proliferation during organogenesis, but several fundamental questions regarding their mode of action remain unclear. Here, we use HyperTRIBE (targets of RNA-binding proteins identified by editing) to show that most ECT2 and ECT3 targets overlap, with only few examples of preferential targeting by either of the two proteins. HyperTRIBE in different mutant backgrounds also provides direct views of redundant and specific target interactions of the two proteins. We also show that contrary to conclusions of previous reports, ECT2 does not accumulate in the nucleus. Accordingly, inactivation of ECT2, ECT3 and their surrogate ECT4 does not change patterns of polyadenylation site choice in ECT2/3 target mRNAs, but does lead to lower steady state accumulation of target mRNAs. In addition, mRNA and microRNA expression profiles show indications of stress response activation in ect2/ect3/ect4 mutants, likely via indirect effects. Thus, previous suggestions of control of alternative polyadenylation by ECT2 are not supported by evidence, and ECT2 and ECT3 act largely redundantly to regulate target mRNA, including its abundance, in the cytoplasm.

scholarly journals High-throughput mutagenesis identifies mutations and RNA binding proteins controlling CD19 splicing and CART-19 therapy resistance

2021 ◽  
Author(s):  
Mariela Cortés-López ◽  
Laura Schulz ◽  
Mihaela Enculescu ◽  
Claudia Paret ◽  
Bea Spiekermann ◽  
...  
Keyword(s):  
High Throughput ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Single Point ◽  
Point Mutations ◽  
Therapy Resistance ◽  
Regulatory Elements ◽  
Splice Isoforms ◽  
Exon 2

During CART-19 immunotherapy for B-cell acute lymphoblastic leukaemia (B-ALL), many patients relapse due to loss of the cognate CD19 epitope. Since epitope loss can be caused by aberrant CD19 exon 2 processing, we herein investigate the regulatory code that controls CD19 splicing. We combine high-throughput mutagenesis with mathematical modelling to quantitatively disentangle the effects of all mutations in the region comprising CD19 exons 1-3. Thereupon, we identify ~200 single point mutations that alter CD19 splicing and thus could predispose B-ALL patients to CART-19 resistance. Furthermore, we report almost 100 previously unknown splice isoforms that emerge from cryptic splice sites and likely encode non-functional CD19 proteins. We further identify cis-regulatory elements and trans-acting RNA-binding proteins that control CD19 splicing (e.g., PTBP1 and SF3B4) and validate that loss of these factors leads to enhanced CD19 mis-splicing. Our dataset represents a comprehensive resource for potential prognostic factors predicting success of CART-19 therapy.

Integrative analyses of transcriptome data reveal the mechanisms of post-transcriptional regulation

2021 ◽  
Author(s):  
Jinkai Wang
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Rapid Development ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
Public Resources ◽  
Integrative Analyses ◽  
Post Transcriptional Regulation

Abstract Post-transcriptional processing of RNAs plays important roles in a variety of physiological and pathological processes. These processes can be precisely controlled by a series of RNA binding proteins and cotranscriptionally regulated by transcription factors as well as histone modifications. With the rapid development of high-throughput sequencing techniques, multiomics data have been broadly used to study the mechanisms underlying the important biological processes. However, how to use these high-throughput sequencing data to elucidate the fundamental regulatory roles of post-transcriptional processes is still of great challenge. This review summarizes the regulatory mechanisms of post-transcriptional processes and the general principles and approaches to dissect these mechanisms by integrating multiomics data as well as public resources.

scholarly journals Transcriptome-wide high-throughput mapping of protein–RNA occupancy profiles using POP-seq

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mansi Srivastava ◽  
Rajneesh Srivastava ◽  
Sarath Chandra Janga
Keyword(s):  
High Throughput ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Interaction Network ◽  
K562 Cells ◽  
Genomic Variation ◽  
Interaction Sites ◽  
Protein Occupancy ◽  
Rna Interaction

AbstractInteraction between proteins and RNA is critical for post-transcriptional regulatory processes. Existing high throughput methods based on crosslinking of the protein–RNA complexes and poly-A pull down are reported to contribute to biases and are not readily amenable for identifying interaction sites on non poly-A RNAs. We present Protein Occupancy Profile-Sequencing (POP-seq), a phase separation based method in three versions, one of which does not require crosslinking, thus providing unbiased protein occupancy profiles on whole cell transcriptome without the requirement of poly-A pulldown. Our study demonstrates that ~ 68% of the total POP-seq peaks exhibited an overlap with publicly available protein–RNA interaction profiles of 97 RNA binding proteins (RBPs) in K562 cells. We show that POP-seq variants consistently capture protein–RNA interaction sites across a broad range of genes including on transcripts encoding for transcription factors (TFs), RNA-Binding Proteins (RBPs) and long non-coding RNAs (lncRNAs). POP-seq identified peaks exhibited a significant enrichment (p value < 2.2e−16) for GWAS SNPs, phenotypic, clinically relevant germline as well as somatic variants reported in cancer genomes, suggesting the prevalence of uncharacterized genomic variation in protein occupied sites on RNA. We demonstrate that the abundance of POP-seq peaks increases with an increase in expression of lncRNAs, suggesting that highly expressed lncRNA are likely to act as sponges for RBPs, contributing to the rewiring of protein–RNA interaction network in cancer cells. Overall, our data supports POP-seq as a robust and cost-effective method that could be applied to primary tissues for mapping global protein occupancies.

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