RNA-protein Binding Sites Research Articles

RNA-Protein Binding Sites Prediction via Multi Scale Convolutional Gated Recurrent Unit Networks.

RNA-Protein binding plays important roles in the field of gene expression. With the development of high throughput sequencing, several conventional methods and deep learning-based methods have been proposed to predict the binding preference of RNA-protein binding. These methods can hardly meet the need of consideration of the dependencies between subsequence and the various motif lengths of different translation factors (TFs). To overcome such limitations, we propose a predictive model that utilizes a combination of multi-scale convolutional layers and bidirectional gated recurrent unit (GRU) layer. Multi-scale convolution layer has the ability to capture the motif features of different lengths, and bidirectional GRU layer is able to capture the dependencies among subsequence. Experimental results show that the proposed method performs better than four state-of-the-art methods in this field. In addition, we investigate the effect of model structure on model performance by performing our proposed method with a different convolution layer and a different number of kernel size. We also demonstrate the effectiveness of bidirectional GRU in improving model performance through comparative experiments.

Investigating the Roles of RNA Binding Protein Combinations in Neuronal Function and Organismal Behavior

The Norris lab recently identified two RNA binding proteins required for proper neuron-specific splicing. The lab conducted touch- response behavioral assays to assess the function of these proteins in touch-sensing neurons. After isolating C. elegans worms with specific phenotypes, the lab used automated computer tracking and video analysis to record the worms’ behavior. The behavior of mutant worms differed from that of wild-type worms. The Norris lab also discovered two possible RNA binding protein sites in SAD-1, a neuronal gene implicated in the neuronal development of C. elegans1. These two binding sites may control the splicing of SAD-1. The lab transferred mutated DNA into the genome of wild-type worms by injecting a mutated plasmid. The newly transformed worms fluoresced green, indicating that the two binding sites control SAD-1 splicing.

Partner-specific prediction of RNA-binding residues in proteins: A critical assessment.

RNA‐protein interactions play essential roles in regulating gene expression. While some RNA‐protein interactions are “specific”, that is, the RNA‐binding proteins preferentially bind to particular RNA sequence or structural motifs, others are “non‐RNA specific.” Deciphering the protein‐RNA recognition code is essential for comprehending the functional implications of these interactions and for developing new therapies for many diseases. Because of the high cost of experimental determination of protein‐RNA interfaces, there is a need for computational methods to identify RNA‐binding residues in proteins. While most of the existing computational methods for predicting RNA‐binding residues in RNA‐binding proteins are oblivious to the characteristics of the partner RNA, there is growing interest in methods for partner‐specific prediction of RNA binding sites in proteins. In this work, we assess the performance of two recently published partner‐specific protein‐RNA interface prediction tools, PS‐PRIP, and PRIdictor, along with our own new tools. Specifically, we introduce a novel metric, RNA‐specificity metric (RSM), for quantifying the RNA‐specificity of the RNA binding residues predicted by such tools. Our results show that the RNA‐binding residues predicted by previously published methods are oblivious to the characteristics of the putative RNA binding partner. Moreover, when evaluated using partner‐agnostic metrics, RNA partner‐specific methods are outperformed by the state‐of‐the‐art partner‐agnostic methods. We conjecture that either (a) the protein‐RNA complexes in PDB are not representative of the protein‐RNA interactions in nature, or (b) the current methods for partner‐specific prediction of RNA‐binding residues in proteins fail to account for the differences in RNA partner‐specific versus partner‐agnostic protein‐RNA interactions, or both.

Genome-wide identification of protein binding sites on RNAs in mammalian cells

RNA-binding proteins (RBPs) are proteins that bind to the RNA and participate in forming ribonucleoprotein complexes. They have crucial roles in various biological processes such as RNA splicing, editing, transport, maintenance, degradation, intracellular localization and translation. The RBPs bind RNA with different RNA-sequence specificities and affinities, thus, identification of protein binding sites on RNAs (R-PBSs) will deeper our understanding of RNA-protein interactions. Currently, high-throughput sequencing of RNA isolated by crosslinking immunoprecipitation (HITS-CLIP, also known as CLIP-Seq) is one of the most powerful methods to map RNA-protein binding sites or RNA modification sites. However, this method is only used for identification of single known RBPs and antibodies for RBPs are required. Here we developed a novel method, called capture of protein binding sites on RNAs (RPBS-Cap) to identify genome-wide protein binding sites on RNAs without using antibodies. Double click strategy is used for the RPBS-Cap assay. Proteins and RNAs are UV-crosslinked in vivo first, then the proteins are crosslinked to the magnetic beads. The RNA elements associated with proteins are captured, reverse transcribed and sequenced. Our approach has potential applications for studying genome-wide RNA-protein interactions.

Deciphering human ribonucleoprotein regulatory networks.

RNA-binding proteins (RBPs) control and coordinate each stage in the life cycle of RNAs. Although in vivo binding sites of RBPs can now be determined genome-wide, most studies typically focused on individual RBPs. Here, we examined a large compendium of 114 high-quality transcriptome-wide in vivo RBP–RNA cross-linking interaction datasets generated by the same protocol in the same cell line and representing 64 distinct RBPs. Comparative analysis of categories of target RNA binding preference, sequence preference, and transcript region specificity was performed, and identified potential posttranscriptional regulatory modules, i.e. specific combinations of RBPs that bind to specific sets of RNAs and targeted regions. These regulatory modules represented functionally related proteins and exhibited distinct differences in RNA metabolism, expression variance, as well as subcellular localization. This integrative investigation of experimental RBP–RNA interaction evidence and RBP regulatory function in a human cell line will be a valuable resource for understanding the complexity of post-transcriptional regulation.

Individually double minimum-distance definition of protein-RNA binding residues and application to structure-based prediction.

Identifying protein-RNA binding residues is essential for understanding the mechanism of protein-RNA interactions. So far, rigid distance thresholds are commonly used to define protein-RNA binding residues. However, after investigating 182 non-redundant protein-RNA complexes, we find that it would be unsuitable for a certain amount of complexes since the distances between proteins and RNAs vary widely. In this work, a novel definition method was proposed based on a flexible distance cutoff. This method can fully consider the individual differences among complexes by setting a variable tolerance limit of protein-RNA interactions, i.e. the double minimum-distance by which different distance thresholds are achieved for different complexes. In order to validate our method, a comprehensive comparison between our flexible method and traditional rigid methods was implemented in terms of interface structure, amino acid composition, interface area and interaction force, etc. The results indicate that this method is more reasonable because it incorporates the specificity of different complexes by extracting the important residues lost by rigid distance methods and discarding some redundant residues. Finally, to further test our double minimum-distance definition strategy, we developed a classifier to predict those binding sites derived from our new method by using structural features and a random forest machine learning algorithm. The model achieved a satisfactory prediction performance and the accuracy on independent data sets reaches to 85.0%. To the best of our knowledge, it is the first prediction model to define positive and negative samples using a flexible cutoff. So the comparison analysis and modeling results have demonstrated that our method would be a very promising strategy for more precisely defining protein-RNA binding sites.

Monitored eCLIP: high accuracy mapping of RNA-protein interactions.

CLIP-seq methods provide transcriptome-wide snapshots of RNA-protein interactions in live cells. Reverse transcriptases stopping at cross-linked nucleotides sign for RNA-protein binding sites. Reading through cross-linked positions results in false binding site assignments. In the ‘monitored enhanced CLIP’ (meCLIP) method, a barcoded biotinylated linker is ligated at the 5′ end of cross-linked RNA fragments to purify RNA prior to the reverse transcription. cDNAs keeping the barcode sequence correspond to reverse transcription read-throughs. Read through occurs in unpredictable proportions, representing up to one fourth of total reads. Filtering out those reads strongly improves reliability and precision in protein binding site assignment.

A Novel Saturation Mutagenesis Approach: Single Step Characterization of Regulatory Protein Binding Sites in RNA Using Phosphorothioates.

Gene regulation plays an important role in development. Numerous DNA- and RNA-binding proteins bind their target sequences with high specificity to control gene expression. These regulatory proteins control gene expression either at the level of DNA (transcription) or at the level of RNA (pre-mRNA splicing, polyadenylation, mRNA transport, decay, and translation). Identification of regulatory sequences helps understand not only how a gene is switched on or off, but also which downstream genes are regulated by a particular regulatory protein. Here, we describe a one-step approach that allows saturation mutagenesis of a protein binding site in RNA. It involves doping DNA template with non-wild-type nucleotides within the binding site, synthesis of separate RNAs with each phosphorothioate nucleotide, and isolation of the bound fraction following incubation with protein. Interference from non-wild-type nucleotides results in their preferential exclusion from the protein-bound fraction. This is monitored by gel electrophoresis following selective chemical cleavage with iodine of phosphodiester bonds containing phosphorothioates (phosphorothioate mutagenesis or PTM). This single-step saturation mutagenesis approach is applicable to the characterization of any protein binding site in RNA.

A Novel Saturation Mutagenesis Approach: Single Step Characterization of Regulatory Protein Binding Sites in RNA Using Phosphorothioates

A Novel Saturation Mutagenesis Approach: Single Step Characterization of Regulatory Protein Binding Sites in RNA Using Phosphorothioates

Data Science Issues in Studying Protein–RNA Interactions with CLIP Technologies

An interplay of experimental and computational methods is required to achieve a comprehensive understanding of protein-RNA interactions. UV crosslinking and immunoprecipitation (CLIP) identifies endogenous interactions by sequencing RNA fragments that copurify with a selected RNA-binding protein under stringent conditions. Here we focus on approaches for the analysis of the resulting data and appraise the methods for peak calling, visualization, analysis, and computational modeling of protein-RNA binding sites. We advocate that the sensitivity and specificity of data be assessed in combination for computational quality control. Moreover, we demonstrate the value of analyzing sequence motif enrichment in peaks assigned from CLIP data and of visualizing RNA maps, which examine the positional distribution of peaks around regulated landmarks in transcripts. We use these to assess how variations in CLIP data quality and in different peak calling methods affect the insights into regulatory mechanisms. We conclude by discussing future opportunities for the computational analysis of protein-RNA interaction experiments.

Highlights of This Issue

Cancer-associated fibroblasts (CAFs) promote cancer growth and metastasis. However, mechanistic understanding by which cancer cells promote activation of healthy fibroblasts into CAFs remains elusive. In the current issue, Goulet and colleagues demonstrate that exosomes released by bladder cancer cells are internalized by healthy fibroblasts (HFs) to promote CAF differentiation through the activation of the TGFβ/SMAD signaling pathway. In fact, exosomal TGFβ, shown to be localized inside the vesicle, contributed 53 to 86% of the total TGFβ present in the bladder cancer cell conditioned medium. In conclusion, this study highlights that TGFβ-derived cancer exosomes are important molecular modulators of tumor microenvironment and CAF differentiation.PARP inhibitors (PARPi) are synthetically lethal in tumors carrying BRCA1/2 mutations and with deficiencies in homologous recombination (HR)-mediated DNA repair. However, BRCA mutations are rare; thus, most tumors are resistant to PARPi. Sizemore and colleagues demonstrate that ionizing radiation (IR) induces BRCA1 sequestration to the cytoplasm and DNA repair deficiency, and subsequent PARPi synthetic lethality in multiple tumor model systems. Importantly, p53 status was discovered to be a significant determining factor of tumor cell sensitization to PARPi following IR. These results have the potential to significantly increase the number of patients who may clinically benefit from PARPi-based therapies.Cancer genomics has highlighted the prevalence of sequence variants of unknown significance. Singh and colleagues hypothesized that a fraction of these mutations reflect RNA-related selection processes, thereby impacting RNA metabolism and contributing to oncogenic processes. Analysis of whole-genome sequencing (WGS) data from multiple cancers uncovered frequent mutations in binding sites for RNA binding proteins and splicing regulators. Furthermore, RNA sequencing (RNA-seq) data from the same specimens validated a change in RNA processing in association to these mutations. The study describes new alterations in cancer that impact RNA processing and proposes a systematic method for the interpretation of non-coding variants in cancer genomes.Histone 2B monoubiquitination (H2Bub1) is commonly lost during carcinogenesis and is deubiquitinated by ubiquitin-specific peptidase 22 (USP22). Here, the impact of USP22 on stem cell characteristics and cisplatin resistance was investigated in cancer-initiating cells (CICs) derived from human lung adenocarcinomas. Elevated USP22 expression associated with stem-like characteristics and chemotherapy resistance. Furthermore, ALDH1A3 which has been implicated in cisplatin resistance in lung adenocarcinoma was down-regulated upon USP22 knockdown. These data demonstrate that USP22 plays a critical role in tumorgenicity and cisplatin resistance in lung adenocarcinoma and may be a useful target for therapy.

Genome Sequencing and RNA-Motif Analysis Reveal Novel Damaging Noncoding Mutations in Human Tumors

A major challenge in cancer research is to determine the biological and clinical significance of somatic mutations in noncoding regions. This has been studied in terms of recurrence, functional impact, and association to individual regulatory sites, but the combinatorial contribution of mutations to common RNA regulatory motifs has not been explored. Therefore, we developed a new method, MIRA (mutation identification for RNA alterations), to perform an unbiased and comprehensive study of significantly mutated regions (SMR) affecting binding sites for RNA-binding proteins (RBP) in cancer. Extracting signals related to RNA-related selection processes and using RNA sequencing (RNA-seq) data from the same specimens, we identified alterations in RNA expression and splicing linked to mutations on RBP binding sites. We found SRSF10 and MBNL1 motifs in introns, HNRPLL motifs at 5' UTRs, as well as 5' and 3' splice-site motifs, among others, with specific mutational patterns that disrupt the motif and impact RNA processing. MIRA facilitates the integrative analysis of multiple genome sites that operate collectively through common RBPs and aids in the interpretation of noncoding variants in cancer. MIRA is available at https://github.com/comprna/miraImplications: The study of recurrent cancer mutations on potential RBP binding sites reveals new alterations in introns, untranslated regions, and long noncoding RNAs that impact RNA processing and provide a new layer of insight that can aid in the interpretation of noncoding variants in cancer genomes. Mol Cancer Res; 16(7); 1112-24. ©2018 AACR.

Learning distributed representations of RNA sequences and its application for predicting RNA-protein binding sites with a convolutional neural network

Abstract RNA-binding proteins (RBPs) play a crucial role in gene regulation. Unfortunately, experimental approaches for detecting the RNA-protein binding sites on RNAs are still high-cost and time-consuming. Computer algorithms are therefore wanted for automatic detection of the binding sites from sequences. This usually requires efficient representations of sequences of various length into vectors of the same size. For example, the k-mers representation as one-hot encoding is a simple widely used approach in the RBP binding sites prediction field. However, the k-mer feature representation will lead to extremely high-dimensional and sparse problems. Furthermore, the k-mer feature representation ignores the positional information within the sequences, which could negatively impact its predictive power. In this study, we present a deep learning based approach iDeepV. It first applies an unsupervised shallow two-layer neural network to automatically learn the distributed representation of k-mers by considering their neighbor context. Compared to the conventional k-mers approach, the new distributed representation captures the latent relationship of k-mers, in which the similarity between k-mers is taken into consideration. Then, the learned distributed representations of the input sequences are used as inputs for a convolutional neural network (CNN) to discriminate the RBP bound sites from the unbound sites. We comprehensively evaluate the iDeepV on two large-scale RBP binding sites datasets. The results show that iDeepV can yield comparable performance than the state-of-the-art methods. The iDeepV algorithm is available at https://github.com/xypan1232/iDeepV .

Predicting RNA-protein binding sites and motifs through combining local and global deep convolutional neural networks.

RNA-binding proteins (RBPs) take over 5-10% of the eukaryotic proteome and play key roles in many biological processes, e.g. gene regulation. Experimental detection of RBP binding sites is still time-intensive and high-costly. Instead, computational prediction of the RBP binding sites using patterns learned from existing annotation knowledge is a fast approach. From the biological point of view, the local structure context derived from local sequences will be recognized by specific RBPs. However, in computational modeling using deep learning, to our best knowledge, only global representations of entire RNA sequences are employed. So far, the local sequence information is ignored in the deep model construction process. In this study, we present a computational method iDeepE to predict RNA-protein binding sites from RNA sequences by combining global and local convolutional neural networks (CNNs). For the global CNN, we pad the RNA sequences into the same length. For the local CNN, we split a RNA sequence into multiple overlapping fixed-length subsequences, where each subsequence is a signal channel of the whole sequence. Next, we train deep CNNs for multiple subsequences and the padded sequences to learn high-level features, respectively. Finally, the outputs from local and global CNNs are combined to improve the prediction. iDeepE demonstrates a better performance over state-of-the-art methods on two large-scale datasets derived from CLIP-seq. We also find that the local CNN runs 1.8 times faster than the global CNN with comparable performance when using GPUs. Our results show that iDeepE has captured experimentally verified binding motifs. https://github.com/xypan1232/iDeepE. Supplementary data are available at Bioinformatics online.

Distinct and Modular Organization of Protein Interacting Sites in Long Non-coding RNAs.

Background: Long non-coding RNAs (lncRNAs), are being reported to be extensively involved in diverse regulatory roles and have exhibited numerous disease associations. LncRNAs modulate their function through interaction with other biomolecules in the cell including DNA, RNA, and proteins. The availability of genome-scale experimental datasets of RNA binding proteins (RBP) motivated us to understand the role of lncRNAs in terms of its interactions with these proteins. In the current report, we demonstrate a comprehensive study of interactions between RBP and lncRNAs at a transcriptome scale through extensive analysis of the crosslinking and immunoprecipitation (CLIP) experimental datasets available for 70 RNA binding proteins.Results: Our analysis suggests that density of interaction sites for these proteins was significantly higher for specific sub-classes of lncRNAs when compared to protein-coding transcripts. We also observe a positional preference of these RBPs across lncRNA and protein coding transcripts in addition to a significant co-occurrence of RBPs having similar functions, suggesting a modular organization of these elements across lncRNAs.Conclusion: The significant enrichment of RBP sites across some lncRNA classes is suggestive that these interactions might be important in understanding the functional role of lncRNA. We observed a significant enrichment of RBPs which are involved in functional roles such as silencing, splicing, mRNA processing, and transport, indicating the potential participation of lncRNAs in such processes.

GoldCLIP: Gel-omitted Ligation-dependent CLIP

Protein–RNA interaction networks are essential to understand gene regulation control. Identifying binding sites of RNA-binding proteins (RBPs) by the UV-crosslinking and immunoprecipitation (CLIP) represents one of the most powerful methods to map protein–RNA interactions in vivo. However, the traditional CLIP protocol is technically challenging, which requires radioactive labeling and suffers from material loss during PAGE-membrane transfer procedures. Here we introduce a super-efficient CLIP method (GoldCLIP) that omits all gel purification steps. This nonisotopic method allows us to perform highly reproducible CLIP experiments with polypyrimidine tract-binding protein (PTB), a classical RBP in human cell lines. In principle, our method guarantees sequencing library constructions, providing the protein of interest can be successfully crosslinked to RNAs in living cells. GoldCLIP is readily applicable to diverse proteins to uncover their endogenous RNA targets.

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.

Landscape and evolution of tissue-specific alternative polyadenylation across Drosophila species

BackgroundDrosophila melanogaster has one of best-described transcriptomes of any multicellular organism. Nevertheless, the paucity of 3′-sequencing data in this species precludes comprehensive assessment of alternative polyadenylation (APA), which is subject to broad tissue-specific control.ResultsHere, we generate deep 3′-sequencing data from 23 developmental stages, tissues, and cell lines of D. melanogaster, yielding a comprehensive atlas of ~ 62,000 polyadenylated ends. These data broadly extend the annotated transcriptome, identify ~ 40,000 novel 3′ termini, and reveal that two-thirds of Drosophila genes are subject to APA. Furthermore, we dramatically expand the numbers of genes known to be subject to tissue-specific APA, such as 3′ untranslated region (UTR) lengthening in head and 3′ UTR shortening in testis, and characterize new tissue and developmental 3′ UTR patterns. Our thorough 3′ UTR annotations permit reassessment of post-transcriptional regulatory networks, via conserved miRNA and RNA binding protein sites. To evaluate the evolutionary conservation and divergence of APA patterns, we generate developmental and tissue-specific 3′-seq libraries from Drosophila yakuba and Drosophila virilis. We document broadly analogous tissue-specific APA trends in these species, but also observe significant alterations in 3′ end usage across orthologs. We exploit the population of functionally evolving poly(A) sites to gain clear evidence that evolutionary divergence in core polyadenylation signal (PAS) and downstream sequence element (DSE) motifs drive broad alterations in 3′ UTR isoform expression across the Drosophila phylogeny.ConclusionsThese data provide a critical resource for the Drosophila community and offer many insights into the complex control of alternative tissue-specific 3′ UTR formation and its consequences for post-transcriptional regulatory networks.

CSCD: a database for cancer-specific circular RNAs.

Circular RNA (circRNA) is a large group of RNA family extensively existed in cells and tissues. High-throughput sequencing provides a way to view circRNAs across different samples, especially in various diseases. However, there is still no comprehensive database for exploring the cancer-specific circRNAs. We collected 228 total RNA or polyA(-) RNA-seq samples from both cancer and normal cell lines, and identified 272 152 cancer-specific circRNAs. A total of 950 962 circRNAs were identified in normal samples only, and 170 909 circRNAs were identified in both tumor and normal samples, which could be further used as non-tumor background. We constructed a cancer-specific circRNA database (CSCD, http://gb.whu.edu.cn/CSCD). To understand the functional effects of circRNAs, we predicted the microRNA response element sites and RNA binding protein sites for each circRNA. We further predicted potential open reading frames to highlight translatable circRNAs. To understand the association between the linear splicing and the back-splicing, we also predicted the splicing events in linear transcripts of each circRNA. As the first comprehensive cancer-specific circRNA database, we believe CSCD could significantly contribute to the research for the function and regulation of cancer-associated circRNAs.

Alterations in mRNA 3′ UTR Isoform Abundance Accompany Gene Expression Changes in Human Huntington’s Disease Brains

The huntingtin gene has two mRNA isoforms that differ in their 3' UTR length. The relationship of these isoforms with Huntington's disease is not established. We provide evidence that the abundance of huntingtin 3' UTR isoforms differs between patient and control neural stem cells, fibroblasts, motor cortex, and cerebellum. Huntingtin 3' UTR isoforms, including a mid-3' UTR isoform, have different localizations, half-lives, polyA tail lengths, microRNA sites, and RNA-binding protein sites. Isoform shifts in Huntington's disease motor cortex are not limited to huntingtin; 11% of alternatively polyadenylated genes change the abundance of their 3' UTR isoforms. Altered expression of RNA-binding proteins may be associated with aberrant isoform abundance; knockdown of the RNA-binding protein CNOT6 in control fibroblasts leads to huntingtin isoform differences similar to those in disease fibroblasts. These findings demonstrate that mRNA 3'UTR isoform changes are a feature of molecular pathology in the Huntington's disease brain.