BackgroundRNA-binding proteins (RBPs) play essential roles in gene expression regulation through their interactions with RNA transcripts, including coding, canonical non-coding and long non-coding RNAs. Large amounts of crosslinking immunoprecipitation (CLIP)-seq data (including HITS-CLIP, PAR-CLIP, and iCLIP) have been recently produced to reveal transcriptome-wide binding sites of RBPs at the single-nucleotide level.DescriptionHere, we constructed a database, CLIPdb, to describe RBP-RNA interactions based on 395 publicly available CLIP-seq data sets for 111 RBPs from four organisms: human, mouse, worm and yeast. We consistently annotated the CLIP-seq data sets and RBPs, and developed a user-friendly interface for rapid navigation of the CLIP-seq data. We applied a unified computational method to identify transcriptome-wide binding sites, making the binding sites directly comparable and the data available for integration across different CLIP-seq studies. The high-resolution binding sites of the RBPs can be visualized on the whole-genome scale using a browser. In addition, users can browse and download the identified binding sites of all profiled RBPs by querying genes of interest, including both protein coding genes and non-coding RNAs.ConclusionManually curated metadata and uniformly identified binding sites of publicly available CLIP-seq data sets will be a foundation for further integrative and comparative analyses. With maintained up-to-date data sets and improved functionality, CLIPdb (http://clipdb.ncrnalab.org) will be a valuable resource for improving the understanding of post-transcriptional regulatory networks.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1273-2) contains supplementary material, which is available to authorized users.
ENCODE comprises thousands of functional genomics datasets, and the encyclopedia covers hundreds of cell types, providing a universal annotation for genome interpretation. However, for particular applications, it may be advantageous to use a customized annotation. Here, we develop such a custom annotation by leveraging advanced assays, such as eCLIP, Hi-C, and whole-genome STARR-seq on a number of data-rich ENCODE cell types. A key aspect of this annotation is comprehensive and experimentally derived networks of both transcription factors and RNA-binding proteins (TFs and RBPs). Cancer, a disease of system-wide dysregulation, is an ideal application for such a network-based annotation. Specifically, for cancer-associated cell types, we put regulators into hierarchies and measure their network change (rewiring) during oncogenesis. We also extensively survey TF-RBP crosstalk, highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of MYC, a well-known oncogenic TF. Furthermore, we show how our annotation allows us to place oncogenic transformations in the context of a broad cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend. Finally, we organize the resource into a coherent workflow to prioritize key elements and variants, in addition to regulators. We showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS. Targeted validations of the prioritized regulators, elements and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of the ENCODE resource.
Interactions with RNA-binding proteins (RBPs) are integral to RNA function and cellular regulation, and dynamically reflect specific cellular conditions. However, presently available tools for predicting RBP–RNA interactions employ RNA sequence and/or predicted RNA structures, and therefore do not capture their condition-dependent nature. Here, after profiling transcriptome-wide in vivo RNA secondary structures in seven cell types, we developed PrismNet, a deep learning tool that integrates experimental in vivo RNA structure data and RBP binding data for matched cells to accurately predict dynamic RBP binding in various cellular conditions. PrismNet results for 168 RBPs support its utility for both understanding CLIP-seq results and largely extending such interaction data to accurately analyze additional cell types. Further, PrismNet employs an “attention” strategy to computationally identify exact RBP-binding nucleotides, and we discovered enrichment among dynamic RBP-binding sites for structure-changing variants (riboSNitches), which can link genetic diseases with dysregulated RBP bindings. Our rich profiling data and deep learning-based prediction tool provide access to a previously inaccessible layer of cell-type-specific RBP–RNA interactions, with clear utility for understanding and treating human diseases.
RNA-binding proteins (RBPs) play key roles in post-transcriptional regulation. Accurate identification of RBP binding sites in multiple cell lines and tissue types from diverse species is a fundamental endeavor towards understanding the regulatory mechanisms of RBPs under both physiological and pathological conditions. Our POSTAR annotation processes make use of publicly available large-scale CLIP-seq datasets and external functional genomic annotations to generate a comprehensive map of RBP binding sites and their association with other regulatory events as well as functional variants. Here, we present POSTAR3, an updated database with improvements in data collection, annotation infrastructure, and analysis that support the annotation of post-transcriptional regulation in multiple species including: we made a comprehensive update on the CLIP-seq and Ribo-seq datasets which cover more biological conditions, technologies, and species; we added RNA secondary structure profiling for RBP binding sites; we provided miRNA-mediated degradation events validated by degradome-seq; we included RBP binding sites at circRNA junction regions; we expanded the annotation of RBP binding sites, particularly using updated genomic variants and mutations associated with diseases. POSTAR3 is freely available at http://postar.ncrnalab.org.
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