CLIPZ: a database and analysis environment for experimentally determined binding sites of RNA-binding proteins

PAR-CLIP Assay in Ferroptosis

Ferroptosis is a regulatory cell death process that is accompanied by large amounts of iron ion accumulation and lipid peroxidation. Photoactivated ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP) is a method used to identify the binding sites of RNA-binding proteins (RBPs) on target RNAs with high resolution at the nucleotide level. By inserting photosensitive ribonucleoside analogs into new RNA transcripts of living cells, characteristic mutations can be generated during reverse transcription and be used to accurately locate the crosslinking position of RNAs and RBPs. The use of PAR-CLIP to detect interactions and determine precise crosslinking sites between RNAs and RBPs, or to search for RNAs upstream or downstream of ferroptosis pathways genes through known proteins, can help to clarify and verify the occurrence and regulation mechanisms of the various signaling pathways of ferroptosis. Furthermore, it may reveal new targets for ferroptosis detection and improve the treatment efficiency of ferroptosis-related diseases such as cancer and neurodegenerative diseases. Here, we introduce a specific PAR-CLIP protocol for monitoring the ferroptosis process.

Computational tools to study RNA-protein complexes

RNA is the key player in many cellular processes such as signal transduction, replication, transport, cell division, transcription, and translation. These diverse functions are accomplished through interactions of RNA with proteins. However, protein–RNA interactions are still poorly derstood in contrast to protein–protein and protein–DNA interactions. This knowledge gap can be attributed to the limited availability of protein-RNA structures along with the experimental difficulties in studying these complexes. Recent progress in computational resources has expanded the number of tools available for studying protein-RNA interactions at various molecular levels. These include tools for predicting interacting residues from primary sequences, modelling of protein-RNA complexes, predicting hotspots in these complexes and insights into derstanding in the dynamics of their interactions. Each of these tools has its strengths and limitations, which makes it significant to select an optimal approach for the question of interest. Here we present a mini review of computational tools to study different aspects of protein-RNA interactions, with focus on overall application, development of the field and the future perspectives.

PAR-CLIP: A Method for Transcriptome-Wide Identification of RNA Binding Protein Interaction Sites

During post-transcriptional gene regulation (PTGR), RNA binding proteins (RBPs) interact with all classes of RNA to control RNA maturation, stability, transport, and translation. Here, we describe Photoactivatable-Ribonucleoside-Enhanced Crosslinking and Immunoprecipitation (PAR-CLIP), a transcriptome-scale method for identifying RBP binding sites on target RNAs with nucleotide-level resolution. This method is readily applicable to any protein directly contacting RNA, including RBPs that are predicted to bind in a sequence- or structure-dependent manner at discrete RNA recognition elements (RREs), and those that are thought to bind transiently, such as RNA polymerases or helicases.

Techniques for Characterizing Cytomegalovirus-Encoded miRNAs

microRNAs (miRNAs) are small noncoding RNAs that regulate gene expression at the posttranscriptional level by binding to sites within the 3' untranslated regions of messenger RNA (mRNA) transcripts. The discovery of this completely new mechanism of gene regulation necessitated the development of a variety of techniques to further characterize miRNAs, their expression, and function. In this chapter, we will discuss techniques currently used in the miRNA field to detect, express and inhibit miRNAs, as well as methods used to identify and validate their targets, specifically with respect to the miRNAs encoded by human cytomegalovirus.

Clip for studying protein-RNA interactions that regulate virus replication

Viral and cellular RNA-binding proteins regulate numerous key steps in the replication of diverse virus genera. Viruses efficiently co-opt the host cell machinery for purposes such as transcription, splicing and subcellular localization of viral genomes. Though viral RNAs often need to resemble cellular RNAs to effectively utilize the cellular machinery, they still retain unique sequence and structural features for recognition by viral proteins for processes such as RNA polymerization, RNA export and selective packaging into virus particles. While beneficial for virus replication, distinct features of viral nucleic acids can also be recognized as foreign by several host defense proteins. Development of the crosslinking immunoprecipitation coupled with sequencing (CLIP) approach has allowed the study of viral and cellular RNA binding proteins that regulate critical aspects of viral replication in unprecedented detail. By combining immunoprecipitation of covalently crosslinked protein-RNA complexes with high-throughput sequencing, CLIP provides a global account of RNA sequences bound by RNA-binding proteins of interest in physiological settings and at near-nucleotide resolution. Here, we describe the step-by-step application of the CLIP methodology within the context of two cellular splicing regulatory proteins, hnRNP A1 and hnRNP H1 that regulate HIV-1 splicing. In principle, this versatile protocol can be applied to many other viral and cellular RNA-binding proteins.

Loss of TSC complex enhances gluconeogenesis via upregulation of Dlk1-Dio3 locus miRNAs

Loss of the tumor suppressor tuberous sclerosis complex 1 (Tsc1) in the liver promotes gluconeogenesis and glucose intolerance. We asked whether this could be attributed to aberrant expression of small RNAs. We performed small-RNA sequencing on liver of Tsc1-knockout mice, and found that miRNAs of the delta-like homolog 1 (Dlk1)-deiodinase iodothyronine type III (Dio3) locus are up-regulated in an mTORC1-dependent manner. Sustained mTORC1 signaling during development prevented CpG methylation and silencing of the Dlk1-Dio3 locus, thereby increasing miRNA transcription. Deletion of miRNAs encoded by the Dlk1-Dio3 locus reduced gluconeogenesis, glucose intolerance, and fasting blood glucose levels. Thus, miRNAs contribute to the metabolic effects observed upon loss of TSC1 and hyperactivation of mTORC1 in the liver. Furthermore, we show that miRNA is a downstream effector of hyperactive mTORC1 signaling.

Recent Advances in Machine Learning Based Prediction of RNA-protein Interactions

The interactions between RNAs and proteins play critical roles in many biological processes. Therefore, characterizing these interactions becomes critical for mechanistic, biomedical, and clinical studies. Many experimental methods can be used to determine RNA-protein interactions in multiple aspects. However, due to the facts that RNA-protein interactions are tissuespecific and condition-specific, as well as these interactions are weak and frequently compete with each other, those experimental techniques can not be made full use of to discover the complete spectrum of RNA-protein interactions. To moderate these issues, continuous efforts have been devoted to developing high quality computational techniques to study the interactions between RNAs and proteins. Many important progresses have been achieved with the application of novel techniques and strategies, such as machine learning techniques. Especially, with the development and application of CLIP techniques, more and more experimental data on RNA-protein interaction under specific biological conditions are available. These CLIP data altogether provide a rich source for developing advanced machine learning predictors. In this review, recent progresses on computational predictors for RNA-protein interaction were summarized in the following aspects: dataset, prediction strategies, and input features. Possible future developments were also discussed at the end of the review.

Computational analysis of ribonomics datasets identifies long non-coding RNA targets of γ-herpesviral miRNAs

Ribonomics experiments involving crosslinking and immuno-precipitation (CLIP) of Ago proteins have expanded the understanding of the miRNA targetome of several organisms. These techniques, collectively referred to as CLIP-seq, have been applied to identifying the mRNA targets of miRNAs expressed by Kaposi’s Sarcoma-associated herpes virus (KSHV) and Epstein–Barr virus (EBV). However, these studies focused on identifying only those RNA targets of KSHV and EBV miRNAs that are known to encode proteins. Recent studies have demonstrated that long non-coding RNAs (lncRNAs) are also targeted by miRNAs. In this study, we performed a systematic re-analysis of published datasets from KSHV- and EBV-driven cancers. We used CLIP-seq data from lymphoma cells or EBV-transformed B cells, and a crosslinking, ligation and sequencing of hybrids dataset from KSHV-infected endothelial cells, to identify novel lncRNA targets of viral miRNAs. Here, we catalog the lncRNA targetome of KSHV and EBV miRNAs, and provide a detailed in silico analysis of lncRNA–miRNA binding interactions. Viral miRNAs target several hundred lncRNAs, including a subset previously shown to be aberrantly expressed in human malignancies. In addition, we identified thousands of lncRNAs to be putative targets of human miRNAs, suggesting that miRNA–lncRNA interactions broadly contribute to the regulation of gene expression.

DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA-gene interactions

DIANA-TarBase v8 (http://www.microrna.gr/tarbase) is a reference database devoted to the indexing of experimentally supported microRNA (miRNA) targets. Its eighth version is the first database indexing >1 million entries, corresponding to ∼670 000 unique miRNA-target pairs. The interactions are supported by >33 experimental methodologies, applied to ∼600 cell types/tissues under ∼451 experimental conditions. It integrates information on cell-type specific miRNA–gene regulation, while hundreds of thousands of miRNA-binding locations are reported. TarBase is coming of age, with more than a decade of continuous support in the non-coding RNA field. A new module has been implemented that enables the browsing of interactions through different filtering combinations. It permits easy retrieval of positive and negative miRNA targets per species, methodology, cell type and tissue. An incorporated ranking system is utilized for the display of interactions based on the robustness of their supporting methodologies. Statistics, pie-charts and interactive bar-plots depicting the database content are available through a dedicated result page. An intuitive interface is introduced, providing a user-friendly application with flexible options to different queries.

Recent computational developments on CLIP-seq data analysis and microRNA targeting implications

Cross-Linking Immunoprecipitation associated to high-throughput sequencing (CLIP-seq) is a technique used to identify RNA directly bound to RNA-binding proteins across the entire transcriptome in cell or tissue samples. Recent technological and computational advances permit the analysis of many CLIP-seq samples simultaneously, allowing us to reveal the comprehensive network of RNA–protein interaction and to integrate it to other genome-wide analyses. Therefore, the design and quality management of the CLIP-seq analyses are of critical importance to extract clean and biological meaningful information from CLIP-seq experiments. The application of CLIP-seq technique to Argonaute 2 (Ago2) protein, the main component of the microRNA (miRNA)-induced silencing complex, reveals the direct binding sites of miRNAs, thus providing insightful information about the role played by miRNA(s). In this review, we summarize and discuss the most recent computational methods for CLIP-seq analysis, and discuss their impact on Ago2/miRNA-binding site identification and prediction with a regard toward human pathologies.

Argonaute 4 as an Effector Protein in RNA-Directed DNA Methylation in Human Cells

DNA methylation of specific genome locations contributes to the distinct functions of multicellular organisms. DNA methylation can be governed by RNA-dependent DNA methylation (RdDM). RdDM is carried out by endogenous small-RNA-guided epigenomic editing complexes that add a methyl group to a precise DNA location. In plants, the Argonaute 4 (AGO4) protein is one of the main catalytic components involved in RdDM. Although small interfering RNA or short hairpin RNA has been shown to be able to guide DNA methylation in human cells, AGO protein-regulated RdDM in humans has not yet been evaluated. This study aimed to identify a key regulatory AGO protein involved in human RdDM by bioinformatics and to explore its function in RdDM by a combination of AGO4 knockdown, Alu small interfering RNA transfection, AGO4-expressing plasmid transfection, chromatin immunoprecipitation, cell-penetrating peptide-tagged AGO4 combined Alu single-guide RNA transfection, and methylation analyses. We found that first, human AGO4 showed stronger genome-wide association with DNA methylation than AGO1–AGO3. Second, endogenous AGO4 depletion demethylated DNA of known AGO4 bound loci. Finally, exogenous AGO4 de novo methylated the bound DNA sequences. Therefore, we discovered that AGO4 plays a role in human RdDM.

Practical considerations on performing and analyzing CLIP-seq experiments to identify transcriptomic-wide RNA-protein interactions

RNA-binding proteins are important players in post-transcriptional regulation, such as modulating mRNA splicing, translation, and degradation under diverse biological settings. Identifying and characterizing the RNA substrates is a critical step in deciphering the function and molecular mechanisms of the target RNA-binding proteins. High-throughput sequencing of the RNA fragments isolated by crosslinking immunoprecipitation (CLIP-seq) is one of the standard techniques to identify the in vivo transcriptome-wide binding sites of the target RNA-binding protein. This method is widely used in functional and mechanistic characterizations of RNA-binding proteins. In this review, we provide several practical considerations on performing and analyzing CLIP-seq experiments. Particularly, we focus on how to perform CLIP-seq experiments on endogenous RNA-binding proteins. In addition, we provide a practical summary on how to choose and use computational pipelines from an increasing number of computational methods and packages that are available for analyzing the sequencing datasets from the CLIP-seq experiments. We hope these practical considerations will facilitate experimental biologists in performing and analyzing CLIP-seq experiment to obtain biologically relevant mechanistic insights.

MINA-1 and WAGO-4 are part of regulatory network coordinating germ cell death and RNAi in C. elegans

Post-transcriptional control of mRNAs by RNA-binding proteins (RBPs) has a prominent role in the regulation of gene expression. RBPs interact with mRNAs to control their biogenesis, splicing, transport, localization, translation, and stability. Defects in such regulation can lead to a wide range of human diseases from neurological disorders to cancer. Many RBPs are conserved between Caenorhabditis elegans and humans, and several are known to regulate apoptosis in the adult C. elegans germ line. How these RBPs control apoptosis is, however, largely unknown. Here, we identify mina-1(C41G7.3) in a RNA interference-based screen as a novel regulator of apoptosis, which is exclusively expressed in the adult germ line. The absence of MINA-1 causes a dramatic increase in germ cell apoptosis, a reduction in brood size, and an impaired P granules organization and structure. In vivo crosslinking immunoprecipitation experiments revealed that MINA-1 binds a set of mRNAs coding for RBPs associated with germ cell development. Additionally, a system-wide analysis of a mina-1 deletion mutant compared with wild type, including quantitative proteome and transcriptome data, hints to a post-transcriptional regulatory RBP network driven by MINA-1 during germ cell development in C. elegans. In particular, we found that the germline-specific Argonaute WAGO-4 protein levels are increased in mina-1 mutant background. Phenotypic analysis of double mutant mina-1;wago-4 revealed that contemporary loss of MINA-1 and WAGO-4 strongly rescues the phenotypes observed in mina-1 mutant background. To strengthen this functional interaction, we found that upregulation of WAGO-4 in mina-1 mutant animals causes hypersensitivity to exogenous RNAi. Our comprehensive experimental approach allowed us to describe a phenocritical interaction between two RBPs controlling germ cell apoptosis and exogenous RNAi. These findings broaden our understanding of how RBPs can orchestrate different cellular events such as differentiation and death in C. elegans.

The unfolded protein response and endoplasmic reticulum protein targeting machineries converge on the stress sensor IRE1

The protein folding capacity of the endoplasmic reticulum (ER) is tightly regulated by a network of signaling pathways, known as the unfolded protein response (UPR). UPR sensors monitor the ER folding status to adjust ER folding capacity according to need. To understand how the UPR sensor IRE1 maintains ER homeostasis, we identified zero-length crosslinks of RNA to IRE1 with single nucleotide precision in vivo. We found that IRE1 specifically crosslinks to a subset of ER-targeted mRNAs, SRP RNA, ribosomal and transfer RNAs. Crosslink sites cluster in a discrete region of the ribosome surface spanning from the A-site to the polypeptide exit tunnel. Moreover, IRE1 binds to purified 80S ribosomes with high affinity, indicating association with ER-bound ribosomes. Our results suggest that the ER protein translocation and targeting machineries work together with the UPR to tune the ER’s protein folding load.

RNA⁻Protein Interactions Prevent Long RNA Duplex Formation: Implications for the Design of RNA-Based Therapeutics

Cells frequently simultaneously express RNAs and cognate antisense transcripts without necessarily leading to the formation of RNA duplexes. Here, we present a novel transcriptome-wide experimental approach to ascertain the presence of accessible double-stranded RNA structures based on sequencing of RNA fragments longer than 18 nucleotides that were not degraded by single-strand cutting nucleases. We applied this approach to four different cell lines with respect to three different treatments (native cell lysate, removal of proteins, and removal of ribosomal RNA and proteins). We found that long accessible RNA duplexes were largely absent in native cell lysates, while the number of RNA duplexes was dramatically higher when proteins were removed. The majority of RNA duplexes involved ribosomal transcripts. The duplex formation between different non-ribosomal transcripts appears to be largely of a stochastic nature. These results suggest that cells are—via RNA-binding proteins—mostly devoid of long RNA duplexes, leading to low “noise” in the molecular patterns that are utilized by the innate immune system. These findings have implications for the design of RNA interference (RNAi)-based therapeutics by imposing structural constraints on designed RNA complexes that are intended to have specific properties with respect to Dicer cleavage and target gene downregulation.

Mapping of In Vivo RNA-Binding Sites by Ultraviolet (UV)-Cross-Linking Immunoprecipitation (CLIP)

RNA "CLIP" (cross-linking immunoprecipitation), the method by which RNA-protein complexes are covalently cross-linked and purified and the RNA sequenced, has attracted attention as a powerful means of developing genome-wide maps of direct, functional RNA-protein interaction sites. These maps have been used to identify points of regulation, and they hold promise for understanding the dynamics of RNA regulation in normal cell function and its dysregulation in disease.

Revealing lncRNA Structures and Interactions by Sequencing-Based Approaches

Long noncoding RNAs (lncRNAs) have emerged as significant players in almost every level of gene function and regulation. Thus, characterizing the structures and interactions of lncRNAs is essential for understanding their mechanistic roles in cells. Through a combination of (bio)chemical approaches and automated capillary and high-throughput sequencing (HTS), the complexity and diversity of RNA structures and interactions has been revealed in the transcriptomes of multiple species. These methods have uncovered important biological insights into the mechanistic and functional roles of lncRNA in gene expression and RNA metabolism, as well as in development and disease. In this review, we summarize the latest sequencing strategies to reveal RNA structure, RNA-RNA, RNA-DNA, and RNA-protein interactions, and highlight the recent applications of these approaches to map functional lncRNAs. We discuss the advantages and limitations of these strategies, and provide recommendations to further advance methodologies capable of mapping RNA structure and interactions in order to discover new biology of lncRNAs and decipher their molecular mechanisms and implication in diseases.

NGS-FC: A Next-Generation Sequencing Data Format Converter

With the widespread implementation of next-generation sequencing (NGS) technologies, millions of sequences have been produced. A lot of databases were created to store and organize the high-throughput sequencing data. Numerous analysis software programs and tools have been developed over the past years. Most of them use specific formats for data representation and storage. Data interoperability becomes a crucial challenge and many tools have been developed to convert NGS data from one format to another. However, most of them were developed for specific and limited formats. Here, we present NGS-FC (Next-Generation Sequencing Format Converter), which provides a framework to support the conversion between several formats. It supports 14 formats now and provides interfaces to enable users to improve the existing converters and add new ones. Moreover, NGS-FC achieved the overall competitive performance in comparison with some existing converters in terms of RAM usage and running time. The software is written in Java and can be executed standalone. The source code and documentation are freely available at http://sysbio.suda.edu.cn/NGS-FC.

Mapping transcriptome-wide protein-RNA interactions to elucidate RNA regulatory programs

BACKGROUND

Our understanding of post-transcriptional gene regulation has increased exponentially with the development of robust methods to define protein-RNA interactions across the transcriptome. In this review, we highlight the evolution and successful applications of crosslinking and immunoprecipitation (CLIP) methods to interrogate protein-RNA interactions in a transcriptome-wide manner.

RESULTS

Here, we survey the vast array of in vitro and in vivo approaches used to identify protein-RNA interactions, including but not limited to electrophoretic mobility shift assays, systematic evolution of ligands by exponential enrichment (SELEX), and RIP-seq. We particularly emphasize the advancement of CLIP technologies, and detail protocol improvements and computational tools used to analyze the output data. Importantly, we discuss how profiling protein-RNA interactions can delineate biological functions including splicing regulation, alternative polyadenylation, cytoplasmic decay substrates, and miRNA targets.

CONCLUSIONS

In summary, this review summarizes the benefits of characterizing RNA-protein networks to further understand the regulation of gene expression and disease pathogenesis. Our review comments on how future CLIP technologies can be adapted to address outstanding questions related to many aspects of RNA metabolism and further advance our understanding of RNA biology.

CLIP-related methodologies and their application to retrovirology

Virtually every step of HIV-1 replication and numerous cellular antiviral defense mechanisms are regulated by the binding of a viral or cellular RNA-binding protein (RBP) to distinct sequence or structural elements on HIV-1 RNAs. Until recently, these protein-RNA interactions were studied largely by in vitro binding assays complemented with genetics approaches. However, these methods are highly limited in the identification of the relevant targets of RBPs in physiologically relevant settings. Development of crosslinking-immunoprecipitation sequencing (CLIP) methodology has revolutionized the analysis of protein-nucleic acid complexes. CLIP combines immunoprecipitation of covalently crosslinked protein-RNA complexes with high-throughput sequencing, providing a global account of RNA sequences bound by a RBP of interest in cells (or virions) at near-nucleotide resolution. Numerous variants of the CLIP protocol have recently been developed, some with major improvements over the original. Herein, we briefly review these methodologies and give examples of how CLIP has been successfully applied to retrovirology research.

A Deep Learning Framework for Robust and Accurate Prediction of ncRNA-Protein Interactions Using Evolutionary Information

The interactions between non-coding RNAs (ncRNAs) and proteins play an important role in many biological processes, and their biological functions are primarily achieved by binding with a variety of proteins. High-throughput biological techniques are used to identify protein molecules bound with specific ncRNA, but they are usually expensive and time consuming. Deep learning provides a powerful solution to computationally predict RNA-protein interactions. In this work, we propose the RPI-SAN model by using the deep-learning stacked auto-encoder network to mine the hidden high-level features from RNA and protein sequences and feed them into a random forest (RF) model to predict ncRNA binding proteins. Stacked assembling is further used to improve the accuracy of the proposed method. Four benchmark datasets, including RPI2241, RPI488, RPI1807, and NPInter v2.0, were employed for the unbiased evaluation of five established prediction tools: RPI-Pred, IPMiner, RPISeq-RF, lncPro, and RPI-SAN. The experimental results show that our RPI-SAN model achieves much better performance than other methods, with accuracies of 90.77%, 89.7%, 96.1%, and 99.33%, respectively. It is anticipated that RPI-SAN can be used as an effective computational tool for future biomedical researches and can accurately predict the potential ncRNA-protein interacted pairs, which provides reliable guidance for biological research.

PAR-CLIP for Discovering Target Sites of RNA-Binding Proteins

RNA-binding proteins (RBPs) establish posttranscriptional gene regulation (PTGR) by coordinating the maturation, editing, transport, stability, and translation of cellular RNAs. A variety of experimental approaches have been developed to characterize the RNAs associated with RBPs in vitro as well as in vivo. Our laboratory developed Photoactivatable-Ribonucleoside-Enhanced Cross-Linking and Immunoprecipitation (PAR-CLIP), which in combination with next-generation sequencing enables the identification of RNA targets of RBPs at a nucleotide-level resolution. Here we present an updated and condensed step-by-step PAR-CLIP protocol followed by the description of our RNA-seq data analysis pipeline.

Methods for Identification of Protein-RNA Interaction

The importance of RNA-protein interactions in regulation of mRNA and non-coding RNA function is increasingly appreciated. With the development of next generation high-throughput sequencing technologies, a variety of methods have been proposed to comprehensively identify RNA-protein interactions. In this chapter, we discussed the traditional and state-of-the-art technologies that were used to study RNA-protein interaction, including experimental and computational methods. To help highlight the biological significance of RNA-protein interaction in complex diseases, online resources on RNA-protein interactions were briefly discussed. Finally, we discussed the interaction among noncoding RNAs (such as long noncoding RNAs and microRNAs) and proteins, as well as the dysregulation of RNA-protein interaction in complex diseases. These summarization will ultimately provide a more complete picture for understanding of the function of RNA-protein interactions, including how these interaction assembled and how they modulate cellular function in complex diseases.

Roquin Suppresses the PI3K-mTOR Signaling Pathway to Inhibit T Helper Cell Differentiation and Conversion of Treg to Tfr Cells

Roquin proteins preclude spontaneous T cell activation and aberrant differentiation of T follicular helper (Tfh) or T helper 17 (Th17) cells. Here we showed that deletion of Roquin-encoding alleles specifically in regulatory T (Treg) cells also caused the activation of conventional T cells. Roquin-deficient Treg cells downregulated CD25, acquired a follicular Treg (Tfr) cell phenotype, and suppressed germinal center reactions but could not protect from colitis. Roquin inhibited the PI3K-mTOR signaling pathway by upregulation of Pten through interfering with miR-17∼92 binding to an overlapping cis-element in the Pten 3' UTR, and downregulated the Foxo1-specific E3 ubiquitin ligase Itch. Loss of Roquin enhanced Akt-mTOR signaling and protein synthesis, whereas inhibition of PI3K or mTOR in Roquin-deficient T cells corrected enhanced Tfh and Th17 or reduced iTreg cell differentiation. Thereby, Roquin-mediated control of PI3K-mTOR signaling prevents autoimmunity by restraining activation and differentiation of conventional T cells and specialization of Treg cells.

Argonaute proteins regulate HIV-1 multiply spliced RNA and viral production in a Dicer independent manner

Argonaute (Ago) proteins associate with microRNAs (miRNAs) to form the core of the RNA-induced silencing complex (RISC) that mediates post-transcriptional gene silencing of target mRNAs. As key players in anti-viral defense, Ago proteins are thought to have the ability to interact with human immunodeficiency virus type 1 (HIV-1) RNA. However, the role of this interaction in regulating HIV-1 replication has been debated. Here, we used high throughput sequencing of RNA isolated by cross-linking immunoprecipitation (HITS-CLIP) to explore the interaction between Ago2 and HIV-1 RNA in infected cells. By only considering reads of 50 nucleotides length in our analysis, we identified more than 30 distinct binding sites for Ago2 along the viral RNA genome. Using reporter assays, we found four binding sites, located near splice donor sites, capable of repressing Luciferase gene expression in an Ago-dependent manner. Furthermore, inhibition of Ago1 and Ago2 levels in cells expressing HIV-1 led to an increase of viral multiply spliced transcripts and to a strong reduction in the extracellular CAp24 level. Depletion of Dicer did not affect these activities. Our results highlight a new role of Ago proteins in the control of multiply spliced HIV-1 transcript levels and viral production, independently of the miRNA pathway.

TFAP2A is a component of the ZEB1/2 network that regulates TGFB1-induced epithelial to mesenchymal transition

Background

The transition between epithelial and mesenchymal phenotypes (EMT) occurs in a variety of contexts. It is critical for mammalian development and it is also involved in tumor initiation and progression. Master transcription factor (TF) regulators of this process are conserved between mouse and human.

Methods

From a computational analysis of a variety of high-throughput sequencing data sets we initially inferred that TFAP2A is connected to the core EMT network in both species. We then analysed publicly available human breast cancer data for TFAP2A expression and also studied the expression (by mRNA sequencing), activity (by monitoring the expression of its predicted targets), and binding (by electrophoretic mobility shift assay and chromatin immunoprecipitation) of this factor in a mouse mammary gland EMT model system (NMuMG) cell line.

Results

We found that upon induction of EMT, the activity of TFAP2A, reflected in the expression level of its predicted targets, is up-regulated in a variety of systems, both murine and human, while TFAP2A’s expression is increased in more “stem-like” cancers. We provide strong evidence for the direct interaction between the TFAP2A TF and the ZEB2 promoter and we demonstrate that this interaction affects ZEB2 expression. Overexpression of TFAP2A from an exogenous construct perturbs EMT, however, in a manner similar to the downregulation of endogenous TFAP2A that takes place during EMT.

Conclusions

Our study reveals that TFAP2A is a conserved component of the core network that regulates EMT, acting as a repressor of many genes, including ZEB2.

Reviewers

This article has been reviewed by Dr. Martijn Huynen and Dr. Nicola Aceto.

Electronic supplementary material

The online version of this article (doi:10.1186/s13062-017-0180-7) contains supplementary material, which is available to authorized users.

Optimization of PAR-CLIP for transcriptome-wide identification of binding sites of RNA-binding proteins

Photoactivatable-Ribonucleoside-Enhanced Crosslinking and Immunoprecipitation (PAR-CLIP) in combination with next-generation sequencing is a powerful method for identifying endogenous targets of RNA-binding proteins (RBPs). Depending on the characteristics of each RBP, key steps in the PAR-CLIP procedure must be optimized. Here we present a comprehensive step-by-step PAR-CLIP protocol with detailed explanations of the critical steps. Furthermore, we report the application of a new PAR-CLIP data analysis pipeline to three distinct RBPs targeting different annotation categories of cellular RNAs.

High-throughput identification of C/D box snoRNA targets with CLIP and RiboMeth-seq

High-throughput sequencing has greatly facilitated the discovery of long and short non-coding RNAs (ncRNAs), which frequently guide ribonucleoprotein complexes to RNA targets, to modulate their metabolism and expression. However, for many ncRNAs, the targets remain to be discovered. In this study, we developed computational methods to map C/D box snoRNA target sites using data from core small nucleolar ribonucleoprotein crosslinking and immunoprecipitation and from transcriptome-wide mapping of 2΄-O-ribose methylation sites. We thereby assigned the snoRNA guide to a known methylation site in the 18S rRNA, we uncovered a novel partially methylated site in the 28S ribosomal RNA, and we captured a site in the 28S rRNA in interaction with multiple snoRNAs. Although we also captured mRNAs in interaction with snoRNAs, we did not detect 2΄-O-methylation of these targets. Our study provides an integrated approach to the comprehensive characterization of 2΄-O-methylation targets of snoRNAs in species beyond those in which these interactions have been traditionally studied and contributes to the rapidly developing field of 'epitranscriptomics'.

A Brief Review of RNA-Protein Interaction Database Resources

RNA–Protein interactions play critical roles in various biological processes. By collecting and analyzing the RNA–Protein interactions and binding sites from experiments and predictions, RNA–Protein interaction databases have become an essential resource for the exploration of the transcriptional and post-transcriptional regulatory network. Here, we briefly review several widely used RNA–Protein interaction database resources developed in recent years to provide a guide of these databases. The content and major functions in databases are presented. The brief description of database helps users to quickly choose the database containing information they interested. In short, these RNA–Protein interaction database resources are continually updated, but the current state shows the efforts to identify and analyze the large amount of RNA–Protein interactions.

Structure alignment-based classification of RNA-binding pockets reveals regional RNA recognition motifs on protein surfaces

Background

Many critical biological processes are strongly related to protein-RNA interactions. Revealing the protein structure motifs for RNA-binding will provide valuable information for deciphering protein-RNA recognition mechanisms and benefit complementary structural design in bioengineering. RNA-binding events often take place at pockets on protein surfaces. The structural classification of local binding pockets determines the major patterns of RNA recognition.

Results

In this work, we provide a novel framework for systematically identifying the structure motifs of protein-RNA binding sites in the form of pockets on regional protein surfaces via a structure alignment-based method. We first construct a similarity network of RNA-binding pockets based on a non-sequential-order structure alignment method for local structure alignment. By using network community decomposition, the RNA-binding pockets on protein surfaces are clustered into groups with structural similarity. With a multiple structure alignment strategy, the consensus RNA-binding pockets in each group are identified. The crucial recognition patterns, as well as the protein-RNA binding motifs, are then identified and analyzed.

Conclusions

Large-scale RNA-binding pockets on protein surfaces are grouped by measuring their structural similarities. This similarity network-based framework provides a convenient method for modeling the structural relationships of functional pockets. The local structural patterns identified serve as structure motifs for the recognition with RNA on protein surfaces.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1410-1) contains supplementary material, which is available to authorized users.

Computational and Experimental Identification of Tissue-Specific MicroRNA Targets

In this chapter we discuss computational methods for the prediction of microRNA (miRNA) targets. More specifically, we consider machine learning-based approaches and explain why these methods have been relatively unsuccessful in reducing the number of false positive predictions. Further we suggest approaches designed to improve their performance by considering tissue-specific target regulation. We argue that the miRNA targetome differs depending on the tissue type and introduce a novel algorithm that predicts miRNA targets specifically for colorectal cancer. We discuss features of miRNAs and target sites that affect target recognition, and how next-generation sequencing data can support the identification of novel miRNAs, differentially expressed miRNAs and their tissue-specific mRNA targets. In addition, we introduce some experimental approaches for the validation of miRNA targets as well as web-based resources sharing predicted and validated miRNA target interactions.

Crosslinking Methods to Identify RNA Methyltransferase Targets In Vivo

Several crosslinking methods have been developed to identify interacting RNAs for proteins of interest. Here, we describe variants of the UV crosslinking and analysis of cDNA (CRAC) method that allow target identification of RNA methyltransferases on a genome-wide scale. We present a detailed protocol for the application of CRAC in human cells that stably express the protein of interest fused to a tandem affinity tag. After the introduction of a covalent link between the protein and its target RNAs, protein-RNA complexes are purified and bound RNAs trimmed, ligated to adapters, reverse transcribed, and amplified. Sequences obtained from next-generation sequencing are then mapped onto the human genome allowing the identification of possible substrates. For some RNA methyltransferases, e.g., m⁵C MTases, their catalytic mechanism can be exploited for chemical crosslinking approaches instead of UV based crosslinking.

Bioinformatic tools for analysis of CLIP ribonucleoprotein data

Investigating the interactions of RNA-binding proteins (RBPs) with RNAs is a complex task for molecular and computational biologists. The molecular biology techniques and the computational approaches to understand RBP-RNA (or ribonucleoprotein, RNP) interactions have advanced considerably over the past few years and numerous and diverse software tools have been developed to analyze these data. Accordingly, laboratories interested in RNP biology face the challenge of choosing adequately among the available software tools those that best address the biological problem they are studying. Here, we focus on state-of-the-art molecular biology techniques that employ crosslinking and immunoprecipitation (CLIP) of an RBP to study and map RNP interactions. We review the different software tools and databases available to analyze the most widely used CLIP methods, HITS-CLIP, PAR-CLIP, and iCLIP. WIREs RNA 2017, 8:e1404. doi: 10.1002/wrna.1404 For further resources related to this article, please visit the WIREs website.

Integrating Epigenomics into the Understanding of Biomedical Insight

Epigenetics is one of the most rapidly expanding fields in biomedical research, and the popularity of the high-throughput next-generation sequencing (NGS) highlights the accelerating speed of epigenomics discovery over the past decade. Epigenetics studies the heritable phenotypes resulting from chromatin changes but without alteration on DNA sequence. Epigenetic factors and their interactive network regulate almost all of the fundamental biological procedures, and incorrect epigenetic information may lead to complex diseases. A comprehensive understanding of epigenetic mechanisms, their interactions, and alterations in health and diseases genome widely has become a priority in biological research. Bioinformatics is expected to make a remarkable contribution for this purpose, especially in processing and interpreting the large-scale NGS datasets. In this review, we introduce the epigenetics pioneering achievements in health status and complex diseases; next, we give a systematic review of the epigenomics data generation, summarize public resources and integrative analysis approaches, and finally outline the challenges and future directions in computational epigenomics.

POSTAR: a platform for exploring post-transcriptional regulation coordinated by RNA-binding proteins

We present POSTAR (http://POSTAR.ncrnalab.org), a resource of POST-trAnscriptional Regulation coordinated by RNA-binding proteins (RBPs). Precise characterization of post-transcriptional regulatory maps has accelerated dramatically in the past few years. Based on new studies and resources, POSTAR supplies the largest collection of experimentally probed (∼23 million) and computationally predicted (approximately 117 million) RBP binding sites in the human and mouse transcriptomes. POSTAR annotates every transcript and its RBP binding sites using extensive information regarding various molecular regulatory events (e.g., splicing, editing, and modification), RNA secondary structures, disease-associated variants, and gene expression and function. Moreover, POSTAR provides a friendly, multi-mode, integrated search interface, which helps users to connect multiple RBP binding sites with post-transcriptional regulatory events, phenotypes, and diseases. Based on our platform, we were able to obtain novel insights into post-transcriptional regulation, such as the putative association between CPSF6 binding, RNA structural domains, and Li-Fraumeni syndrome SNPs. In summary, POSTAR represents an early effort to systematically annotate post-transcriptional regulatory maps and explore the putative roles of RBPs in human diseases.

IPMiner: hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate computational prediction

BACKGROUND

Non-coding RNAs (ncRNAs) play crucial roles in many biological processes, such as post-transcription of gene regulation. ncRNAs mainly function through interaction with RNA binding proteins (RBPs). To understand the function of a ncRNA, a fundamental step is to identify which protein is involved into its interaction. Therefore it is promising to computationally predict RBPs, where the major challenge is that the interaction pattern or motif is difficult to be found.

RESULTS

In this study, we propose a computational method IPMiner (Interaction Pattern Miner) to predict ncRNA-protein interactions from sequences, which makes use of deep learning and further improves its performance using stacked ensembling. One of the IPMiner's typical merits is that it is able to mine the hidden sequential interaction patterns from sequence composition features of protein and RNA sequences using stacked autoencoder, and then the learned hidden features are fed into random forest models. Finally, stacked ensembling is used to integrate different predictors to further improve the prediction performance. The experimental results indicate that IPMiner achieves superior performance on the tested lncRNA-protein interaction dataset with an accuracy of 0.891, sensitivity of 0.939, specificity of 0.831, precision of 0.945 and Matthews correlation coefficient of 0.784, respectively. We further comprehensively investigate IPMiner on other RNA-protein interaction datasets, which yields better performance than the state-of-the-art methods, and the performance has an increase of over 20 % on some tested benchmarked datasets. In addition, we further apply IPMiner for large-scale prediction of ncRNA-protein network, that achieves promising prediction performance.

CONCLUSION

By integrating deep neural network and stacked ensembling, from simple sequence composition features, IPMiner can automatically learn high-level abstraction features, which had strong discriminant ability for RNA-protein detection. IPMiner achieved high performance on our constructed lncRNA-protein benchmark dataset and other RNA-protein datasets. IPMiner tool is available at http://www.csbio.sjtu.edu.cn/bioinf/IPMiner .

A Pipeline for PAR-CLIP Data Analysis

Photo-activatable ribonucleoside cross-linking and immunoprecipitation (PAR-CLIP) is a method to detect binding sites of RNA-binding proteins (RBPs) transcriptome-wide. This chapter covers the computational analysis of the high-throughput sequencing reads generated from PAR-CLIP experiments. It explains how the reads are mutated due to UV cross-linking and how to appropriately pre-process and align them to a reference sequence. Aligned reads are then aggregated into clusters which represent putative RBP-binding sites. Mapping artifacts are a source of false positives, which can be controlled by means of a mapping decoy and adaptive quality filtering of the read clusters. A step-by-step explanation of this procedure is given. All necessary tools are open source, including the scripts presented and used in this chapter.

A comprehensive analysis of 3' end sequencing data sets reveals novel polyadenylation signals and the repressive role of heterogeneous ribonucleoprotein C on cleavage and polyadenylation

Alternative polyadenylation (APA) is a general mechanism of transcript diversification in mammals, which has been recently linked to proliferative states and cancer. Different 3′ untranslated region (3′ UTR) isoforms interact with different RNA-binding proteins (RBPs), which modify the stability, translation, and subcellular localization of the corresponding transcripts. Although the heterogeneity of pre-mRNA 3′ end processing has been established with high-throughput approaches, the mechanisms that underlie systematic changes in 3′ UTR lengths remain to be characterized. Through a uniform analysis of a large number of 3′ end sequencing data sets, we have uncovered 18 signals, six of which are novel, whose positioning with respect to pre-mRNA cleavage sites indicates a role in pre-mRNA 3′ end processing in both mouse and human. With 3′ end sequencing we have demonstrated that the heterogeneous ribonucleoprotein C (HNRNPC), which binds the poly(U) motif whose frequency also peaks in the vicinity of polyadenylation (poly(A)) sites, has a genome-wide effect on poly(A) site usage. HNRNPC-regulated 3′ UTRs are enriched in ELAV-like RBP 1 (ELAVL1) binding sites and include those of the CD47 gene, which participate in the recently discovered mechanism of 3′ UTR–dependent protein localization (UDPL). Our study thus establishes an up-to-date, high-confidence catalog of 3′ end processing sites and poly(A) signals, and it uncovers an important role of HNRNPC in regulating 3′ end processing. It further suggests that U-rich elements mediate interactions with multiple RBPs that regulate different stages in a transcript's life cycle.

Tiny giants of gene regulation: experimental strategies for microRNA functional studies

The discovery over two decades ago of short regulatory microRNAs (miRNAs) has led to the inception of a vast biomedical research field dedicated to understanding these powerful orchestrators of gene expression. Here we aim to provide a comprehensive overview of the methods and techniques underpinning the experimental pipeline employed for exploratory miRNA studies in animals. Some of the greatest challenges in this field have been uncovering the identity of miRNA-target interactions and deciphering their significance with regard to particular physiological or pathological processes. These endeavors relied almost exclusively on the development of powerful research tools encompassing novel bioinformatics pipelines, high-throughput target identification platforms, and functional target validation methodologies. Thus, in an unparalleled manner, the biomedical technology revolution unceasingly enhanced and refined our ability to dissect miRNA regulatory networks and understand their roles in vivo in the context of cells and organisms. Recurring motifs of target recognition have led to the creation of a large number of multifactorial bioinformatics analysis platforms, which have proved instrumental in guiding experimental miRNA studies. Subsequently, the need for discovery of miRNA-target binding events in vivo drove the emergence of a slew of high-throughput multiplex strategies, which now provide a viable prospect for elucidating genome-wide miRNA-target binding maps in a variety of cell types and tissues. Finally, deciphering the functional relevance of miRNA post-transcriptional gene silencing under physiological conditions, prompted the evolution of a host of technologies enabling systemic manipulation of miRNA homeostasis as well as high-precision interference with their direct, endogenous targets. For further resources related to this article, please visit the WIREs website.

Polypyrimidine tract binding protein 1 protects mRNAs from recognition by the nonsense-mediated mRNA decay pathway

The nonsense-mediated mRNA decay (NMD) pathway degrades mRNAs containing long 3'UTRs to perform dual roles in mRNA quality control and gene expression regulation. However, expansion of vertebrate 3'UTR functions has required a physical expansion of 3'UTR lengths, complicating the process of detecting nonsense mutations. We show that the polypyrimidine tract binding protein 1 (PTBP1) shields specific retroviral and cellular transcripts from NMD. When bound near a stop codon, PTBP1 blocks the NMD protein UPF1 from binding 3'UTRs. PTBP1 can thus mark specific stop codons as genuine, preserving both the ability of NMD to accurately detect aberrant mRNAs and the capacity of long 3'UTRs to regulate gene expression. Illustrating the wide scope of this mechanism, we use RNA-seq and transcriptome-wide analysis of PTBP1 binding sites to show that many human mRNAs are protected by PTBP1 and that PTBP1 enrichment near stop codons correlates with 3'UTR length and resistance to NMD.

DOI:http://dx.doi.org/10.7554/eLife.11155.001

Genes are used as templates to create molecules of messenger RNA (mRNA) that contain all the information needed to make a protein. This information begins with a 'start site' and ends with a 'stop site.' The regions of the mRNA outside of the start and stop sites are called untranslated regions.

Not all mRNAs are correctly made, and cells combat this problem by detecting and destroying faulty mRNAs before they are translated into protein. One way cells do this is by recognizing and destroying mRNAs that include long untranslated regions, which can indicate that the mRNA might have a stop site too early in its sequence. A key problem with this mechanism, however, is that long untranslated regions also serve important roles in the cell: for example, by determining where and when mRNA molecules are read to make protein. How then do mRNAs with long but important untranslated regions escape detection and degradation?

Ge et al. have now investigated this question using an approach that allows a 'handle' to be attached to particular RNA molecules. This allows the RNA and any proteins bound to it to be purified away from all other RNAs and proteins in the cell, and the proteins can then be identified by a technique called mass spectrometry.

Ge at al. found that mRNAs can recruit a protein called PTBP1 to part of the RNA sequence near the stop site. This prevents an RNA decay protein recognizing and triggering the degradation of the mRNA, even if the mRNA has a long untranslated region.

Thus, PTBP1 plays a crucial role in protecting human RNAs with long untranslated regions from destruction by the nonsense-mediated decay pathway. Some viral RNAs are also able to evade decay, and so Ge et al. hypothesize that the virus stole this method for maintaining its RNAs from host cells. A future goal is to understand whether this system works the same way in all cell types or protects different RNAs in different cells.

DOI:http://dx.doi.org/10.7554/eLife.11155.002

PAR-CLIP: A Method for Transcriptome-Wide Identification of RNA Binding Protein Interaction Sites

During post-transcriptional gene regulation (PTGR), RNA binding proteins (RBPs) interact with all classes of RNA to control RNA maturation, stability, transport, and translation. Here, we describe Photoactivatable-Ribonucleoside-Enhanced Crosslinking and Immunoprecipitation (PAR-CLIP), a transcriptome-scale method for identifying RBP binding sites on target RNAs with nucleotide-level resolution. This method is readily applicable to any protein directly contacting RNA, including RBPs that are predicted to bind in a sequence- or structure-dependent manner at discrete RNA recognition elements (RREs), and those that are thought to bind transiently, such as RNA polymerases or helicases.

Introduction to Bioinformatics Resources for Post-transcriptional Regulation of Gene Expression

Untranslated regions (UTRs) and, to a lesser extent, coding sequences of mRNAs are involved in defining the fate of the mature transcripts through the modulation of three primary control processes, mRNA localization, degradation and translation; the action of trans-factors such as RNA-binding proteins (RBPs) and noncoding RNAs (ncRNAs) combined with the presence of defined sequence and structural cis-elements ultimately determines translation levels. Identifying functional regions in UTRs and uncovering post-transcriptional regulators acting upon these regions is thus of paramount importance to understand the spectrum of regulatory possibilities for any given mRNA. This tasks can now be approached computationally, to reduce the space of testable hypotheses and to drive experimental validation.This chapter focuses on presenting databases and tools allowing to study the various aspects of post-transcriptional regulation, including motif search (sequence and secondary structure), prediction of regulatory networks (e.g., RBP and ncRNA binding sites), profiling of the mRNAs translational state, and other aspects of this level of gene expression regulation. Two analysis pipelines are also presented as practical examples of how the described tools could be integrated and effectively employed.

HIV-1 RNAs are Not Part of the Argonaute 2 Associated RNA Interference Pathway in Macrophages

Background

MiRNAs and other small noncoding RNAs (sncRNAs) are key players in post-transcriptional gene regulation. HIV-1 derived small noncoding RNAs (sncRNAs) have been described in HIV-1 infected cells, but their biological functions still remain to be elucidated. Here, we approached the question whether viral sncRNAs may play a role in the RNA interference (RNAi) pathway or whether viral mRNAs are targeted by cellular miRNAs in human monocyte derived macrophages (MDM).

Methods

The incorporation of viral sncRNAs and/or their target RNAs into RNA-induced silencing complex was investigated using photoactivatable ribonucleoside-induced cross-linking and immunoprecipitation (PAR-CLIP) as well as high-throughput sequencing of RNA isolated by cross-linking immunoprecipitation (HITS-CLIP), which capture Argonaute2-bound miRNAs and their target RNAs. HIV-1 infected monocyte-derived macrophages (MDM) were chosen as target cells, as they have previously been shown to express HIV-1 sncRNAs. In addition, we applied small RNA deep sequencing to study differential cellular miRNA expression in HIV-1 infected versus non-infected MDMs.

Results and Conclusion

PAR-CLIP and HITS-CLIP data demonstrated the absence of HIV-1 RNAs in Ago2-RISC, although the presence of a multitude of HIV-1 sncRNAs in HIV-1 infected MDMs was confirmed by small RNA sequencing. Small RNA sequencing revealed that 1.4% of all sncRNAs were of HIV-1 origin. However, neither HIV-1 derived sncRNAs nor putative HIV-1 target sequences incorporated into Ago2-RISC were identified suggesting that HIV-1 sncRNAs are not involved in the canonical RNAi pathway nor is HIV-1 targeted by this pathway in HIV-1 infected macrophages.

Comparative assessment of methods for the computational inference of transcript isoform abundance from RNA-seq data

Background

Understanding the regulation of gene expression, including transcription start site usage, alternative splicing, and polyadenylation, requires accurate quantification of expression levels down to the level of individual transcript isoforms. To comparatively evaluate the accuracy of the many methods that have been proposed for estimating transcript isoform abundance from RNA sequencing data, we have used both synthetic data as well as an independent experimental method for quantifying the abundance of transcript ends at the genome-wide level.

Results

We found that many tools have good accuracy and yield better estimates of gene-level expression compared to commonly used count-based approaches, but they vary widely in memory and runtime requirements. Nucleotide composition and intron/exon structure have comparatively little influence on the accuracy of expression estimates, which correlates most strongly with transcript/gene expression levels. To facilitate the reproduction and further extension of our study, we provide datasets, source code, and an online analysis tool on a companion website, where developers can upload expression estimates obtained with their own tool to compare them to those inferred by the methods assessed here.

Conclusions

As many methods for quantifying isoform abundance with comparable accuracy are available, a user’s choice will likely be determined by factors such as the memory and runtime requirements, as well as the availability of methods for downstream analyses. Sequencing-based methods to quantify the abundance of specific transcript regions could complement validation schemes based on synthetic data and quantitative PCR in future or ongoing assessments of RNA-seq analysis methods.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-015-0702-5) contains supplementary material, which is available to authorized users.

Revealing protein-lncRNA interaction

Long non-coding RNAs (lncRNAs) are associated to a plethora of cellular functions, most of which require the interaction with one or more RNA-binding proteins (RBPs); similarly, RBPs are often able to bind a large number of different RNAs. The currently available knowledge is already drawing an intricate network of interactions, whose deregulation is frequently associated to pathological states. Several different techniques were developed in the past years to obtain protein–RNA binding data in a high-throughput fashion. In parallel, in silico inference methods were developed for the accurate computational prediction of the interaction of RBP–lncRNA pairs. The field is growing rapidly, and it is foreseeable that in the near future, the protein–lncRNA interaction network will rise, offering essential clues for a better understanding of lncRNA cellular mechanisms and their disease-associated perturbations.

Design and bioinformatics analysis of genome-wide CLIP experiments

The past decades have witnessed a surge of discoveries revealing RNA regulation as a central player in cellular processes. RNAs are regulated by RNA-binding proteins (RBPs) at all post-transcriptional stages, including splicing, transportation, stabilization and translation. Defects in the functions of these RBPs underlie a broad spectrum of human pathologies. Systematic identification of RBP functional targets is among the key biomedical research questions and provides a new direction for drug discovery. The advent of cross-linking immunoprecipitation coupled with high-throughput sequencing (genome-wide CLIP) technology has recently enabled the investigation of genome-wide RBP–RNA binding at single base-pair resolution. This technology has evolved through the development of three distinct versions: HITS-CLIP, PAR-CLIP and iCLIP. Meanwhile, numerous bioinformatics pipelines for handling the genome-wide CLIP data have also been developed. In this review, we discuss the genome-wide CLIP technology and focus on bioinformatics analysis. Specifically, we compare the strengths and weaknesses, as well as the scopes, of various bioinformatics tools. To assist readers in choosing optimal procedures for their analysis, we also review experimental design and procedures that affect bioinformatics analyses.

Computational challenges, tools, and resources for analyzing co- and post-transcriptional events in high throughput

Co- and post-transcriptional regulation of gene expression is complex and multifaceted, spanning the complete RNA lifecycle from genesis to decay. High-throughput profiling of the constituent events and processes is achieved through a range of technologies that continue to expand and evolve. Fully leveraging the resulting data is nontrivial, and requires the use of computational methods and tools carefully crafted for specific data sources and often intended to probe particular biological processes. Drawing upon databases of information pre-compiled by other researchers can further elevate analyses. Within this review, we describe the major co- and post-transcriptional events in the RNA lifecycle that are amenable to high-throughput profiling. We place specific emphasis on the analysis of the resulting data, in particular the computational tools and resources available, as well as looking toward future challenges that remain to be addressed.

CLIPdb: a CLIP-seq database for protein-RNA interactions

Background

RNA-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.

Description

Here, 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.

Conclusion

Manually 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 material

The online version of this article (doi:10.1186/s12864-015-1273-2) contains supplementary material, which is available to authorized users.