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

Zooming in on protein-RNA interactions: a multi-level workflow to identify interaction partners

Interactions between proteins and RNA are at the base of numerous cellular regulatory and functional phenomena. The investigation of the biological relevance of non-coding RNAs has led to the identification of numerous novel RNA-binding proteins (RBPs). However, defining the RNA sequences and structures that are selectively recognised by an RBP remains challenging, since these interactions can be transient and highly dynamic, and may be mediated by unstructured regions in the protein, as in the case of many non-canonical RBPs. Numerous experimental and computational methodologies have been developed to predict, identify and verify the binding between a given RBP and potential RNA partners, but navigating across the vast ocean of data can be frustrating and misleading. In this mini-review, we propose a workflow for the identification of the RNA binding partners of putative, newly identified RBPs. The large pool of potential binders selected by in-cell experiments can be enriched by in silico tools such as catRAPID, which is able to predict the RNA sequences more likely to interact with specific RBP regions with high accuracy. The RNA candidates with the highest potential can then be analysed in vitro to determine the binding strength and to precisely identify the binding sites. The results thus obtained can furthermore validate the computational predictions, offering an all-round solution to the issue of finding the most likely RNA binding partners for a newly identified potential RBP.

Mutant FUS and ELAVL4 (HuD) Aberrant Crosstalk in Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) has been genetically linked to mutations in RNA-binding proteins (RBPs), including FUS. Here, we report the RNA interactome of wild-type and mutant FUS in human motor neurons (MNs). This analysis identified a number of RNA targets. Whereas the wild-type protein preferentially binds introns, the ALS mutation causes a shift toward 3' UTRs. Neural ELAV-like RBPs are among mutant FUS targets. As a result, ELAVL4 protein levels are increased in mutant MNs. ELAVL4 and mutant FUS interact and co-localize in cytoplasmic speckles with altered biomechanical properties. Upon oxidative stress, ELAVL4 and mutant FUS are engaged in stress granules. In the spinal cord of FUS ALS patients, ELAVL4 represents a neural-specific component of FUS-positive cytoplasmic aggregates, whereas in sporadic patients it co-localizes with phosphorylated TDP-43-positive inclusions. We propose that pathological mutations in FUS trigger an aberrant crosstalk with ELAVL4 with implications for ALS.

BayMAP: a Bayesian hierarchical model for the analysis of PAR-CLIP data

MOTIVATION

Photoactivatable-Ribonucleoside-Enhanced Crosslinking and Immunoprecipitation (PAR-CLIP) is a biochemical method for detecting interaction sites of proteins with mRNA. This method introduces T-to-C substitutions at sequenced cDNA that help to detect binding sites on mRNA. However, T-to-C substitutions can also occur due to other reasons such as mismatches or SNPs. Only few statistical procedures exist for detecting binding sites in PAR-CLIP data. Most of these methods do not account for other types of substitutions than those induced by PAR-CLIP, and therefore, also report positions with high T-to-C substitution rates, e.g. SNPs, as binding sites. Moreover, none of these procedures allow to include additional information, e.g. the type of mRNA region, relevant for the biology of microRNA-binding sites.

RESULTS

We have developed BayMAP, a procedure based on a fully Bayesian hierarchical model that takes other sources of substitutions into account. Furthermore, this model enables the incorporation of additional information into the analysis of PAR-CLIP data. This incorporation does not only permit a better detection of binding sites, but also a better understanding of the data and the biology of binding sites. In applications to simulated PAR-CLIP data, BayMAP distinguishes binding sites from noise better than existing methods. Additionally, it yields good estimates of the influence of the additional information. We here demonstrate BayMAP's usability for real datasets even when noisy data is present.

AVAILABILITY AND IMPLEMENTATION

BayMAP is freely available as an R package at http://stat.math.uni-duesseldorf.de/baymap.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

omniCLIP: probabilistic identification of protein-RNA interactions from CLIP-seq data

CLIP-seq methods allow the generation of genome-wide maps of RNA binding protein – RNA interaction sites. However, due to differences between different CLIP-seq assays, existing computational approaches to analyze the data can only be applied to a subset of assays. Here, we present a probabilistic model called omniCLIP that can detect regulatory elements in RNAs from data of all CLIP-seq assays. omniCLIP jointly models data across replicates and can integrate background information. Therefore, omniCLIP greatly simplifies the data analysis, increases the reliability of results and paves the way for integrative studies based on data from different assays.

LRPPRC-mediated folding of the mitochondrial transcriptome

The expression of the compact mammalian mitochondrial genome requires transcription, RNA processing, translation and RNA decay, much like the more complex chromosomal systems, and here we use it as a model system to understand the fundamental aspects of gene expression. Here we combine RNase footprinting with PAR-CLIP at unprecedented depth to reveal the importance of RNA-protein interactions in dictating RNA folding within the mitochondrial transcriptome. We show that LRPPRC, in complex with its protein partner SLIRP, binds throughout the mitochondrial transcriptome, with a preference for mRNAs, and its loss affects the entire secondary structure and stability of the transcriptome. We demonstrate that the LRPPRC-SLIRP complex is a global RNA chaperone that stabilizes RNA structures to expose the required sites for translation, stabilization, and polyadenylation. Our findings reveal a general mechanism where extensive RNA-protein interactions ensure that RNA is accessible for its biological functions.

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.

The PARA-suite: PAR-CLIP specific sequence read simulation and processing

Background

Next-generation sequencing technologies have profoundly impacted biology over recent years. Experimental protocols, such as photoactivatable ribonucleoside-enhanced cross-linking and immunoprecipitation (PAR-CLIP), which identifies protein–RNA interactions on a genome-wide scale, commonly employ deep sequencing. With PAR-CLIP, the incorporation of photoactivatable nucleosides into nascent transcripts leads to high rates of specific nucleotide conversions during reverse transcription. So far, the specific properties of PAR-CLIP-derived sequencing reads have not been assessed in depth.

Methods

We here compared PAR-CLIP sequencing reads to regular transcriptome sequencing reads (RNA-Seq) to identify distinctive properties that are relevant for reference-based read alignment of PAR-CLIP datasets. We developed a set of freely available tools for PAR-CLIP data analysis, called the PAR-CLIP analyzer suite (PARA-suite). The PARA-suite includes error model inference, PAR-CLIP read simulation based on PAR-CLIP specific properties, a full read alignment pipeline with a modified Burrows–Wheeler Aligner algorithm and CLIP read clustering for binding site detection.

Results

We show that differences in the error profiles of PAR-CLIP reads relative to regular transcriptome sequencing reads (RNA-Seq) make a distinct processing advantageous. We examine the alignment accuracy of commonly applied read aligners on 10 simulated PAR-CLIP datasets using different parameter settings and identified the most accurate setup among those read aligners. We demonstrate the performance of the PARA-suite in conjunction with different binding site detection algorithms on several real PAR-CLIP and HITS-CLIP datasets. Our processing pipeline allowed the improvement of both alignment and binding site detection accuracy.

Availability

The PARA-suite toolkit and the PARA-suite aligner are available at https://github.com/akloetgen/PARA-suite and https://github.com/akloetgen/PARA-suite_aligner, respectively, under the GNU GPLv3 license.