PIPE-CLIP: a comprehensive online tool for CLIP-seq data analysis

Global RNA recognition patterns of post-transcriptional regulators Hfq and CsrA revealed by UV crosslinking in vivo

The molecular roles of many RNA‐binding proteins in bacterial post‐transcriptional gene regulation are not well understood. Approaches combining in vivo UV crosslinking with RNA deep sequencing (CLIP‐seq) have begun to revolutionize the transcriptome‐wide mapping of eukaryotic RNA‐binding protein target sites. We have applied CLIP‐seq to chart the target landscape of two major bacterial post‐transcriptional regulators, Hfq and CsrA, in the model pathogen Salmonella Typhimurium. By detecting binding sites at single‐nucleotide resolution, we identify RNA preferences and structural constraints of Hfq and CsrA during their interactions with hundreds of cellular transcripts. This reveals 3′‐located Rho‐independent terminators as a universal motif involved in Hfq–RNA interactions. Additionally, Hfq preferentially binds 5′ to sRNA‐target sites in mRNAs, and 3′ to seed sequences in sRNAs, reflecting a simple logic in how Hfq facilitates sRNA–mRNA interactions. Importantly, global knowledge of Hfq sites significantly improves sRNA‐target predictions. CsrA binds AUGGA sequences in apical loops and targets many Salmonella virulence mRNAs. Overall, our generic CLIP‐seq approach will bring new insights into post‐transcriptional gene regulation by RNA‐binding proteins in diverse bacterial species.

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

Guardian of Genetic Messenger-RNA-Binding Proteins

RNA in cells is always associated with RNA-binding proteins that regulate all aspects of RNA metabolism including RNA splicing, export from the nucleus, RNA localization, mRNA turn-over as well as translation. Given their diverse functions, cells express a variety of RNA-binding proteins, which play important roles in the pathologies of a number of diseases. In this review we focus on the effect of alcohol on different RNA-binding proteins and their possible contribution to alcohol-related disorders, and discuss the role of these proteins in the development of neurological diseases and cancer. We further discuss the conventional methods and newer techniques that are employed to identify RNA-binding proteins.

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.

CLIPSeqTools--a novel bioinformatics CLIP-seq analysis suite

Immunoprecipitation of RNA binding proteins (RBPs) after in vivo crosslinking, coupled with sequencing of associated RNA footprints (HITS-CLIP, CLIP-seq), is a method of choice for the identification of RNA targets and binding sites for RBPs. Compared with RNA-seq, CLIP-seq analysis is widely diverse and depending on the RBPs that are analyzed, the approaches vary significantly, necessitating the development of flexible and efficient informatics tools. In this study, we present CLIPSeqTools, a novel, highly flexible computational suite that can perform analysis from raw sequencing data with minimal user input. It contains a wide array of tools to provide an in-depth view of CLIP-seq data sets. It supports extensive customization and promotes improvization, a critical virtue, since CLIP-seq analysis is rarely well defined a priori. To highlight CLIPSeqTools capabilities, we used the suite to analyze Ago-miRNA HITS-CLIP data sets that we prepared from human brains.

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.

TauCstF-64 Mediates Correct mRNA Polyadenylation and Splicing of Activator and Repressor Isoforms of the Cyclic AMP-Responsive Element Modulator (CREM) in Mouse Testis

Spermatogenesis is coordinated by the spatial and temporal expression of many transcriptional and posttranscriptional factors. The cyclic AMP-responsive element modulator (CREM) gene encodes both activator and repressor isoforms that act as transcription factors to regulate spermiogenesis. We found that the testis-expressed paralog of CstF-64, tauCstF-64 (gene symbol Cstf2t), is involved in a polyadenylation site choice switch of Crem mRNA and leads to an overall decrease of the Crem mRNAs that are generated from internal promoters in Cstf2t(-/-) mice. More surprisingly, loss of tauCstF-64 also leads to alternative splicing of Crem exon 4, which contains an important activation domain. Thus, testis-specific CREMtau2 isoform protein levels are reduced in Cstf2t(-/-) mice. Consequently, expression of 15 CREM-regulated genes is decreased in testes of Cstf2t(-/-) mice at 25 days postpartum. These effects might further contribute to the infertility phenotype of these animals. This demonstrates that tauCstF-64 is an important stage-specific regulator of Crem mRNA processing that modulates the spatial and temporal expression of downstream stage-specific genes necessary for the proper development of sperm in mice.

Assessing Computational Steps for CLIP-Seq Data Analysis

RNA-binding protein (RBP) is a key player in regulating gene expression at the posttranscriptional level. CLIP-Seq, with the ability to provide a genome-wide map of protein-RNA interactions, has been increasingly used to decipher RBP-mediated posttranscriptional regulation. Generating highly reliable binding sites from CLIP-Seq requires not only stringent library preparation but also considerable computational efforts. Here we presented a first systematic evaluation of major computational steps for identifying RBP binding sites from CLIP-Seq data, including preprocessing, the choice of control samples, peak normalization, and motif discovery. We found that avoiding PCR amplification artifacts, normalizing to input RNA or mRNAseq, and defining the background model from control samples can reduce the bias introduced by RNA abundance and improve the quality of detected binding sites. Our findings can serve as a general guideline for CLIP experiments design and the comprehensive analysis of CLIP-Seq data.

BMix: probabilistic modeling of occurring substitutions in PAR-CLIP data

MOTIVATION

Photoactivatable ribonucleoside-enhanced cross-linking and immunoprecipitation (PAR-CLIP) is an experimental method based on next-generation sequencing for identifying the RNA interaction sites of a given protein. The method deliberately inserts T-to-C substitutions at the RNA-protein interaction sites, which provides a second layer of evidence compared with other CLIP methods. However, the experiment includes several sources of noise which cause both low-frequency errors and spurious high-frequency alterations. Therefore, rigorous statistical analysis is required in order to separate true T-to-C base changes, following cross-linking, from noise. So far, most of the existing PAR-CLIP data analysis methods focus on discarding the low-frequency errors and rely on high-frequency substitutions to report binding sites, not taking into account the possibility of high-frequency false positive substitutions.

RESULTS

Here, we introduce BMix, a new probabilistic method which explicitly accounts for the sources of noise in PAR-CLIP data and distinguishes cross-link induced T-to-C substitutions from low and high-frequency erroneous alterations. We demonstrate the superior speed and accuracy of our method compared with existing approaches on both simulated and real, publicly available human datasets.

AVAILABILITY AND IMPLEMENTATION

The model is freely accessible within the BMix toolbox at www.cbg.bsse.ethz.ch/software/BMix, available for Matlab and R.

SUPPLEMENTARY INFORMATION

Supplementary data is available at Bioinformatics online.

CONTACT

niko.beerenwinkel@bsse.ethz.ch.

Argonaute 2 Binds Directly to tRNA Genes and Promotes Gene Repression in cis

To further our understanding of the RNAi machinery within the human nucleus, we analyzed the chromatin and RNA binding of Argonaute 2 (AGO2) within human cancer cell lines. Our data indicated that AGO2 binds directly to nascent tRNA and 5S rRNA, and to the genomic loci from which these RNAs are transcribed, in a small RNA- and DICER-independent manner. AGO2 chromatin binding was not observed at non-TFIIIC-dependent RNA polymerase III (Pol III) genes or at extra-TFIIIC (ETC) sites, indicating that the interaction is specific for TFIIIC-dependent Pol III genes. A genome-wide analysis indicated that loss of AGO2 caused a global increase in mRNA expression level among genes that flank AGO2-bound tRNA genes. This effect was shown to be distinct from that of the disruption of DICER, DROSHA, or CTCF. We propose that AGO2 binding to tRNA genes has a novel and important regulatory role in human cells.

Improved binding site assignment by high-resolution mapping of RNA-protein interactions using iCLIP

Individual-nucleotide resolution crosslinking and immunoprecipitation (iCLIP) allows the determination of crosslinking sites of RNA-binding proteins (RBPs) on RNAs. iCLIP is based on ultraviolet light crosslinking of RBPs to RNA, reverse transcription and high-throughput sequencing of fragments terminating at the site of crosslinking. As a result, start sites of iCLIP fragments are expected to cluster with a narrow distribution, typically representing the site of direct interaction between the RBP and the RNA. Here we show that for several RBPs (eIF4A3, PTB, SRSF3, SRSF4 and hnRNP L), the start sites of iCLIP fragments show a fragment length-dependent broader distribution that can be shifted to positions upstream of the known RNA-binding site. We developed an analysis tool that identifies these shifts and can improve the positioning of RBP binding sites.

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.

Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data

BACKGROUND

PAR-CLIP is a recently developed Next Generation Sequencing-based method enabling transcriptome-wide identification of interaction sites between RNA and RNA-binding proteins. The PAR-CLIP procedure induces specific base transitions that originate from sites of RNA-protein interactions and can therefore guide the identification of binding sites. However, additional sources of transitions, such as cell type-specific SNPs and sequencing errors, challenge the inference of binding sites and suitable statistical approaches are crucial to control false discovery rates. In addition, a highly resolved delineation of binding sites followed by an extensive downstream analysis is necessary for a comprehensive characterization of the protein binding preferences and the subsequent design of validation experiments.

RESULTS

We present a statistical and computational framework for PAR-CLIP data analysis. We developed a sensitive transition-centered algorithm specifically designed to resolve protein binding sites at high resolution in PAR-CLIP data. Our method employes a Bayesian network approach to associate posterior log-odds with the observed transitions, providing an overall quantification of the confidence in RNA-protein interaction. We use published PAR-CLIP data to demonstrate the advantages of our approach, which compares favorably with alternative algorithms. Lastly, by integrating RNA-Seq data we compute conservative experimentally-based false discovery rates of our method and demonstrate the high precision of our strategy.

CONCLUSIONS

Our method is implemented in the R package wavClusteR 2.0. The package is distributed under the GPL-2 license and is available from BioConductor at http://www.bioconductor.org/packages/devel/bioc/html/wavClusteR.html .

Computational Methods for CLIP-seq Data Processing

RNA-binding proteins (RBPs) are at the core of post-transcriptional regulation and thus of gene expression control at the RNA level. One of the principal challenges in the field of gene expression regulation is to understand RBPs mechanism of action. As a result of recent evolution of experimental techniques, it is now possible to obtain the RNA regions recognized by RBPs on a transcriptome-wide scale. In fact, CLIP-seq protocols use the joint action of CLIP, crosslinking immunoprecipitation, and high-throughput sequencing to recover the transcriptome-wide set of interaction regions for a particular protein. Nevertheless, computational methods are necessary to process CLIP-seq experimental data and are a key to advancement in the understanding of gene regulatory mechanisms. Considering the importance of computational methods in this area, we present a review of the current status of computational approaches used and proposed for CLIP-seq data.