RNA-protein Binding Sites Research Articles

Human cells contain myriad excised linear intron RNAs with links to gene regulation and potential utility as biomarkers.

A previous study using Thermostable Group II Intron Reverse Transcriptase sequencing (TGIRT-seq) found human plasma contains short (≤300 nt) structured full-length excised linear intron (FLEXI) RNAs with potential to serve as blood-based biomarkers. Here, TGIRT-seq identified >9,000 different FLEXI RNAs in human cell lines, including relatively abundant FLEXIs with cell-type-specific expression patterns. Analysis of public CLIP-seq datasets identified 126 RNA-binding proteins (RBPs) that have binding sites within the region corresponding to the FLEXI or overlapping FLEXI splice sites in pre-mRNAs, including 53 RBPs with binding sites for ≥30 different FLEXIs. These included splicing factors, transcription factors, a chromatin remodeling protein, cellular growth regulators, and proteins with cytoplasmic functions. Analysis of ENCODE datasets identified subsets of these RBPs whose knockdown impacted FLEXI host gene mRNA levels or proximate alternative splicing, indicating functional interactions. Hierarchical clustering identified six subsets of RBPs whose FLEXI binding sites were co-enriched in six subsets of functionally related host genes: AGO1-4 and DICER, including but not limited to agotrons or mirtron pre-miRNAs; DKC1, NOLC1, SMNDC1, and AATF (Apoptosis Antagonizing Transcription Factor), including but not limited to snoRNA-encoding FLEXIs; two subsets of alternative splicing factors; and two subsets that included RBPs with cytoplasmic functions (e.g., LARP4, PABPC4, METAP2, and ZNF622) together with regulatory proteins. Cell fractionation experiments showed cytoplasmic enrichment of FLEXI RNAs with binding sites for RBPs with cytoplasmic functions. The subsets of host genes encoding FLEXIs with binding sites for different subsets of RBPs were co-enriched with non-FLEXI other short and long introns with binding sites for the same RBPs, suggesting overarching mechanisms for coordinately regulating expression of functionally related genes. Our findings identify FLEXIs as a previously unrecognized large class of cellular RNAs and provide a comprehensive roadmap for further analyzing their biological functions and the relationship of their RBPs to cellular regulatory mechanisms.

MAHyNet: Parallel Hybrid Network for RNA-Protein Binding Sites Prediction Based on Multi-Head Attention and Expectation Pooling.

RNA-binding proteins (RBPs) can regulate biological functions by interacting with specific RNAs, and play an important role in many life activities. Therefore, the rapid identification of RNA-protein binding sites is crucial for functional annotation and site-directed mutagenesis. In this work, a new parallel network that integrates the multi-head attention mechanism and the expectation pooling is proposed, named MAHyNet. The left-branch network of MAHyNet hybrids convolutional neural networks (CNNs) and gated recurrent neural network (GRU) to extract the features of one-hot. The right-branch network is a two-layer CNN network to analyze physicochemical properties of RNA base. Specifically, the multi-head attention mechanism is a computational collection of multiple independent layers of attention, which can extract feature information from multiple dimensions. The expectation pooling combines probabilistic thinking with global pooling. This approach helps to reduce model parameters and enhance the model performance. The combination of CNN and GRU enables further extraction of high-level features in sequences. In addition, the study shows that appropriate hyperparameters have a positive impact on the model performance. Physicochemical properties can be used to supplement characterization information to improving model performance. The experimental results show that MAHyNet has better performance than other models.

EPDRNA: A Model for Identifying DNA-RNA Binding Sites in Disease-Related Proteins.

Protein-DNA and protein-RNA interactions are involved in many biological processes and regulate many cellular functions. Moreover, they are related to many human diseases. To understand the molecular mechanism of protein-DNA binding and protein-RNA binding, it is important to identify which residues in the protein sequence bind to DNA and RNA. At present, there are few methods for specifically identifying the binding sites of disease-related protein-DNA and protein-RNA. In this study, so we combined four machine learning algorithms into an ensemble classifier (EPDRNA) to predict DNA and RNA binding sites in disease-related proteins. The dataset used in model was collated from UniProt and PDB database, and PSSM, physicochemical properties and amino acid type were used as features. The EPDRNA adopted soft voting and achieved the best AUC value of 0.73 at the DNA binding sites, and the best AUC value of 0.71 at the RNA binding sites in 10-fold cross validation in the training sets. In order to further verify the performance of the model, we assessed EPDRNA for the prediction of DNA-binding sites and the prediction of RNA-binding sites on the independent test dataset. The EPDRNA achieved 85% recall rate and 25% precision on the protein-DNA interaction independent test set, and achieved 82% recall rate and 27% precision on the protein-RNA interaction independent test set. The online EPDRNA webserver is freely available at http://www.s-bioinformatics.cn/epdrna .

T helper cells exhibit a dynamic and reversible 3'-UTR landscape.

3' untranslated regions (3' UTRs) are critical elements of messenger RNAs, as they contain binding sites for RNA-binding proteins (RBPs) and microRNAs that affect various aspects of the RNA life cycle including transcript stability and cellular localization. In response to T cell receptor activation, T cells undergo massive expansion during the effector phase of the immune response and dynamically modify their 3' UTRs. Whether this serves to directly regulate the abundance of specific mRNAs or is a secondary effect of proliferation remains unclear. To study 3'-UTR dynamics in T helper cells, we investigated division-dependent alternative polyadenylation (APA). In addition, we generated 3' end UTR sequencing data from naive, activated, memory, and regulatory CD4+ T cells. 3'-UTR length changes were estimated using a nonnegative matrix factorization approach and were compared with those inferred from long-read PacBio sequencing. We found that APA events were transient and reverted after effector phase expansion. Using an orthogonal bulk RNA-seq data set, we did not find evidence of APA association with differential gene expression or transcript usage, indicating that APA has only a marginal effect on transcript abundance. 3'-UTR sequence analysis revealed conserved binding sites for T cell-relevant microRNAs and RBPs in the alternative 3' UTRs. These results indicate that poly(A) site usage could play an important role in the control of cell fate decisions and homeostasis.

EquiPNAS: improved protein-nucleic acid binding site prediction using protein-language-model-informed equivariant deep graph neural networks.

Protein language models (pLMs) trained on a large corpus of protein sequences have shown unprecedented scalability and broad generalizability in a wide range of predictive modeling tasks, but their power has not yet been harnessed for predicting protein-nucleic acid binding sites, critical for characterizing the interactions between proteins and nucleic acids. Here, we present EquiPNAS, a new pLM-informed E(3) equivariant deep graph neural network framework for improved protein-nucleic acid binding site prediction. By combining the strengths of pLM and symmetry-aware deep graph learning, EquiPNAS consistently outperforms the state-of-the-art methods for both protein-DNA and protein-RNA binding site prediction on multiple datasets across a diverse set of predictive modeling scenarios ranging from using experimental input to AlphaFold2 predictions. Our ablation study reveals that the pLM embeddings used in EquiPNAS are sufficiently powerful to dramatically reduce the dependence on the availability of evolutionary information without compromising on accuracy, and that the symmetry-aware nature of the E(3) equivariant graph-based neural architecture offers remarkable robustness and performance resilience. EquiPNAS is freely available at https://github.com/Bhattacharya-Lab/EquiPNAS.

Improved discovery of RNA-binding protein binding sites in eCLIP data using DEWSeq.

Enhanced crosslinking and immunoprecipitation (eCLIP) sequencing is a method for transcriptome-wide detection of binding sites of RNA-binding proteins (RBPs). However, identified crosslink sites can deviate from experimentally established functional elements of even well-studied RBPs. Current peak-calling strategies result in low replication and high false positive rates. Here, we present the R/Bioconductor package DEWSeq that makes use of replicate information and size-matched input controls. We benchmarked DEWSeq on 107 RBPs for which both eCLIP data and RNA sequence motifs are available and were able to more than double the number of motif-containing binding regions relative to standard eCLIP processing. The improvement not only relates to the number of binding sites (3.1-fold with known motifs for RBFOX2), but also their subcellular localization (1.9-fold of mitochondrial genes for FASTKD2) and structural targets (2.2-fold increase of stem-loop regions for SLBP. On several orthogonal CLIP-seq datasets, DEWSeq recovers a larger number of motif-containing binding sites (3.3-fold). DEWSeq is a well-documented R/Bioconductor package, scalable to adequate numbers of replicates, and tends to substantially increase the proportion and total number of RBP binding sites containing biologically relevant features.

Identification of region-specific splicing QTLs in human hippocampal tissue and its distinctive role in brain disorders

Alternative splicing (AS) regulation has an essential role in complex diseases. However, the AS profiles in the hippocampal (HIPPO) region of human brain are underexplored. Here, we investigated cis-acting sQTLs of HIPPO region in 264 samples and identified thousands of significant sQTLs. By enrichment analysis and functional characterization of these sQTLs, we found that the HIPPO sQTLs were enriched among histone-marked regions, transcription factors binding sites, RNA binding proteins sites, and brain disorders-associated loci. Comparative analyses with the dorsolateral prefrontal cortex revealed the importance of AS regulation in HIPPO (rg= 0.87). Furthermore, we performed a transcriptome-wide association study of Alzheimer's disease and identified 16 significant genes whose genetically regulated splicing levels may have a causal role in Alzheimer. Overall, our study improves our knowledge of the transcriptome gene regulation in the HIPPO region and provides novel insights into elucidating the pathogenesis of potential genes associated with brain disorders.

WVDL: weighted voting deep learning model for predicting RNA-protein binding sites.

RNA-binding proteins are important for the process of cell life activities. High-throughput technique experimental method to discover RNA-protein binding sites is time-consuming and expensive. Deep learning is an effective theory for predicting RNA-protein binding sites. Using weighted voting method to integrate multiple basic classifier models can improve model performance. Thus, in our study, we propose a weighted voting deep learning model (WVDL), which uses weighted voting method to combine convolutional neural network (CNN), long short term memory network (LSTM) and residual network (ResNet). First, the final forecast result of WVDL outperforms the basic classifier models and other ensemble strategies. Second, WVDL can extract more effective features by using weighted voting to find the best weighted combination. And, the CNN model also can draw the predicted motif pictures. Third, WVDL gets a competitive experiment result on public RBP-24 datasets comparing with other state-of-the-art methods. The source code of our proposed WVDL can be found in https://github.com/biomg/WVDL.

A systematic benchmark of machine learning methods for protein-RNA interaction prediction.

RNA-binding proteins (RBPs) are central actors of RNA post-transcriptional regulation. Experiments to profile-binding sites of RBPs in vivo are limited to transcripts expressed in the experimental cell type, creating the need for computational methods to infer missing binding information. While numerous machine-learning based methods have been developed for this task, their use of heterogeneous training and evaluation datasets across different sets of RBPs and CLIP-seq protocols makes a direct comparison of their performance difficult. Here, we compile a set of 37 machine learning (primarily deep learning) methods for in vivo RBP-RNA interaction prediction and systematically benchmark a subset of 11 representative methods across hundreds of CLIP-seq datasets and RBPs. Using homogenized sample pre-processing and two negative-class sample generation strategies, we evaluate methods in terms of predictive performance and assess the impact of neural network architectures and input modalities on model performance. We believe that this study will not only enable researchers to choose the optimal prediction method for their tasks at hand, but also aid method developers in developing novel, high-performing methods by introducing a standardized framework for their evaluation.

Optimized infrared photoactivatable ribonucleoside-enhanced crosslinking and immunoprecipitation (IR-PAR-CLIP) protocol identifies novel IGF2BP3-interacting RNAs in colon cancer cells.

The conserved family of RNA-binding proteins (RBPs), IGF2BPs, plays an essential role in posttranscriptional regulation controlling mRNA stability, localization, and translation. Mammalian cells express three isoforms of IGF2BPs: IGF2BP1-3. IGF2BP3 is highly overexpressed in cancer cells, and its expression correlates with a poor prognosis in various tumors. Therefore, revealing its target RNAs with high specificity in healthy tissues and in cancer cells is of crucial importance. Photoactivatable-ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP) identifies the binding sites of RBPs on their target RNAs at nucleotide resolution in a transcriptome-wide manner. Here, we optimized the PAR-CLIP protocol to study RNA targets of endogenous IGF2BP3 in a human colorectal carcinoma cell line. To this end, we first established an immunoprecipitation protocol to obtain highly pure endogenous IGF2BP3-RNA complexes. Second, we modified the protocol to use highly sensitive infrared (IR) fluorescent dyes instead of radioactive probes to visualize IGF2BP3-crosslinked RNAs. We named the modified method "IR-PAR-CLIP." Third, we compared RNase cleavage conditions and found that sequence preferences of the RNases impact the number of the identified IGF2BP3 targets and introduce a systematic bias in the identified RNA motifs. Fourth, we adapted the single adapter circular ligation approach to increase the efficiency in library preparation. The optimized IR-PAR-CLIP protocol revealed novel RNA targets of IGF2BP3 in a human colorectal carcinoma cell line. We anticipate that our IR-PAR-CLIP approach provides a framework for studies of other RBPs.

Circular RNAs arising from synaptic host genes during human neuronal differentiation are modulated by SFPQ RNA-binding protein

BackgroundCircular RNA (circRNA) molecules, generated through non-canonical back-splicing of exon-exon junctions, have recently been implicated in diverse biological functions including transcriptional regulation and modulation of protein interactions. CircRNAs are emerging as a key component of the complex neural transcriptome implicated in brain development. However, the specific expression patterns and functions of circRNAs in human neuronal differentiation have not been explored.ResultsUsing total RNA sequencing analysis, we identified expressed circRNAs during the differentiation of human neuroepithelial stem (NES) cells into developing neurons and discovered that many circRNAs originated from host genes associated with synaptic function. Interestingly, when assessing population data, exons giving rise to circRNAs in our dataset had a higher frequency of genetic variants. Additionally, screening for RNA-binding protein sites identified enrichment of Splicing Factor Proline and Glutamine Rich (SFPQ) motifs in increased circRNAs, several of which were reduced by SFPQ knockdown and enriched in SFPQ ribonucleoprotein complexes.ConclusionsOur study provides an in-depth characterisation of circRNAs in a human neuronal differentiation model and highlights SFPQ as both a regulator and binding partner of circRNAs elevated during neuronal maturation.

CRMSNet: a deep learning model that uses convolution and residual multi-head self-attention block to predict RBPs for RNA sequence.

RNA-binding proteins (RBPs) play significant roles in many biological life activities, many algorithms and tools are proposed to predict RBPs for researching biological mechanisms of RNA-protein binding sites. Deep learning algorithms based on traditional machine learning get better result for predicting RBPs. Recently, deep learning method fused with attention mechanism has attracted huge attention in many fields and gets competitive result. Thus, attention mechanism module may also improve model performance for predicting RNA-protein binding sites. In this study, we propose convolutional residual multi-head self-attention network (CRMSNet) that combines CNN, ResNet and multi-head self-attention blocks to find RBPs for RNA sequence. First, CRMSNet incorporates convolutional neural networks, recurrent neural networks and multi-head self-attention block. Second, CRMSNet can draw binding motif pictures from the convolutional layer parameters. Third, attention mechanism module combines the local and global RNA sequence information for capturing long sequence feature. CRMSNet gets competitive AUC (area under the ROC curve) result in a large-scale dataset RBP-24. And CRMSNet experiment result is also compared with other state-of-the-art methods. The source code of our proposed CRMSNet method can be found in https://github.com/biomg/CRMSNet. This article is protected by copyright. All rights reserved.

MCNN: Multiple Convolutional Neural Networks for RNA-Protein Binding Sites Prediction.

Computational prediction of the RBP bound sites using features learned from existing annotation knowledge is an effective method because high-throughput experiments are complex, expensive and time-consuming. Many methods have been proposed to predict RNA-protein binding sites. However, the partial information of RNA sequence is not fully used. In this study, we propose multiple convolutional neural networks (MCNN) method, which predicts RNA-protein binding sites by integrating multiple convolutional neural networks constructed by RNA sequence information extracted from windows with different lengths. First, MCNN trains multiple CNNs base on RNA sequences extracted by different window lengths. Second, MCNN can extract more binding patterns of RBPs by combining these trained multiple CNNs previously. Third, MCNN only uses RNA base sequence information for RNA-protein binding sites prediction, which extracts sequence binding features and predicts the result with same architecture. This avoids the information loss of feature extraction step. Our proposed MCNN demonstrates a competitive performance comparing with other methods on a large-scale dataset derived from CLIP-seq, which is an effective method for RNA-protein binding sites prediction. The source code of our proposed MCNN method can be found in https://github.com/biomg/MCNN.

Self-Attention Based Neural Network for Predicting RNA-Protein Binding Sites.

Proteins binding to Ribonucleic Acid (RNA) inside cells are called RNA-binding proteins (RBP), which play a crucial role in gene regulation. The identification of RNA-protein binding sites helps to understand the function of RBP better. Although many computational methods have been developed to predict RNA-protein binding sites, their prediction accuracy on small sample datasets needs improvement. To overcome this limitation, we propose a novel model called SA-Net, which utilizes k-mer embedding to encode RNA sequences and a self-attention-based neural network to extract sequence features. K-mer embedding assists the model to discover significant subsequence fragments associated with binding sites. The self-attention mechanism captures contextual information from the entire input sequence globally, performing well in small sample sequence learning. Experimental results demonstrate that SA-Net attains state-of-the-art results on the RBP-24 dataset. We find that 4-mer embedding aids the model to achieve optimal performance. We also show that the self-attention network outperforms the commonly used CNN and CNN-BLSTM models in sequence feature extraction.

Specific Signal Enhancement on an RNA-Protein Interface by Dynamic Nuclear Polarization.

Sensitivity and specificity are both crucial for the efficient solid-state NMR structure determination of large biomolecules. We present an approach that features both advantages by site-specific enhancement of NMR spectroscopic signals from the protein-RNA binding site within a ribonucleoprotein (RNP) by dynamic nuclear polarization (DNP). This approach uses modern biochemical techniques for sparse isotope labeling and exploits the molecular dynamics of 13 C-labeled methyl groups exclusively present in the protein. These dynamics drive heteronuclear cross relaxation and thus allow specific hyperpolarization transfer across the biomolecular complex's interface. For the example of the L7Ae protein in complex with a 26mer guide RNA minimal construct from the box C/D complex in archaea, we demonstrate that a single methyl-nucleotide contact is responsible for most of the polarization transfer to the RNA, and that this specific transfer can be used to boost both NMR spectral sensitivity and specificity by DNP.

Transcriptomics-proteomics Integration reveals alternative polyadenylation driving inflammation-related protein translation in patients with diabetic nephropathy

BackgroundDiabetic nephropathy (DN) is a complex disease involving the upregulation of many inflammation-related proteins. Alternative polyadenylation (APA), a crucial post-transcriptional regulatory mechanism, has been proven to play vital roles in many inflammatory diseases. However, it is largely unknown whether and how APA exerts function in DN.MethodsWe performed transcriptomics and proteomics analysis of glomeruli samples isolated from 50 biopsy-proven DN patients and 25 control subjects. DaPars and QAPA algorithms were adopted to identify APA events from RNA-seq data. The qRT-PCR analysis was conducted to verify 3′UTR length alteration. Short and long 3ʹUTRs isoforms were also overexpressed in podocytes under hyperglycemia condition for examining protein expression.ResultsWe detected transcriptome-wide 3′UTR APA events in DN, and found that APA-mediated 3ʹUTR lengthening of genes (APA genes) increased their expression at protein but not mRNA level. Increased protein level of 3′UTR lengthening gene was validated in podocytes under hyperglycemia condition. Pathway enrichment analysis showed that APA genes were enriched in inflammation-related biological processes including endoplasmic reticulum stress pathways, NF-κB signaling and autophagy. Further bioinformatics analysis demonstrated that 3′UTR APA of genes probably altered the binding sites for RNA-binding proteins, thus enhancing protein translation.ConclusionThis study revealed for the first time that 3′UTR lengthening of APA genes contributed to the progression of DN by elevating the translation of corresponding proteins, providing new insight and a rich resource for investigating DN mechanisms.

Revised iCLIP-seq Protocol for Profiling RNA-protein Interaction Sites at Individual Nucleotide Resolution in Living Cells.

Individual nucleotide resolution UV cross-linking and immunoprecipitation followed by high-throughput sequencing (iCLIP-seq) is a powerful technique that is used to identify RNA-binding proteins' (RBP) binding sites on target RNAs and to characterize the molecular basis of posttranscriptional regulatory pathways. Several variants of CLIP have been developed to improve its efficiency and simplify the protocol [e.g., iCLIP2 and enhanced CLIP (eCLIP)]. We have recently reported that transcription factor SP1 functions in the regulation of alternative cleavage and polyadenylation through direct RNA binding. We utilized a modified iCLIP method to identify RNA-binding sites for SP1 and several of the cleavage and polyadenylation complex subunits, including CFIm25, CPSF7, CPSF100, CPSF2, and Fip1. Our revised protocol takes advantage of several features of the eCLIP procedure and also improves on certain steps of the original iCLIP method, including optimization of circularization of cDNA. Herein, we describe a step-by-step procedure for our revised iCLIP-seq protocol, that we designate as iCLIP-1.5, and provide alternative approaches for certain difficult-to-CLIP proteins. Key features Identification of RNA-binding sites of RNA-binding proteins (RBPs) at nucleotide resolution. iCLIP-seq provides precise positional and quantitative information on the RNA-binding sites of RBPs in living cells. iCLIP facilitates the identification of sequence motifs recognized by RBPs. Allows quantitative analysis of genome-wide changes in protein-RNA interactions. Revised iCLIP-1.5 protocol is more efficient and highly robust; it provides higher coverage even for low-input samples. Graphical overview.

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.

Multiplexed transcriptome discovery of RNA-binding protein binding sites by antibody-barcode eCLIP

Ultraviolet crosslinking and immunoprecipitation (CLIP) methodologies enable the identification of RNA binding sites of RNA-binding proteins (RBPs). Despite improvements in the library preparation of RNA fragments, the enhanced CLIP (eCLIP) protocol requires 4 days of hands-on time and lacks the ability to process several RBPs in parallel. We present a new method termed antibody-barcode eCLIP that utilizes DNA-barcoded antibodies and proximity ligation of the DNA oligonucleotides to RBP-protected RNA fragments to interrogate several RBPs simultaneously. We observe performance comparable with that of eCLIP with the advantage of dramatically increased scaling while maintaining the same material requirement of a single eCLIP experiment.

Transcriptome - wide bioinformatics analysis of the binding sites of RNA - binding proteins and their putative role in mendelian diseases

Background: Post-transcriptional regulation is the control of gene expression at the RNA level. After produced, the stability and distribution of the different transcripts are regulated by means of RNA-binding proteins (RBPs). Mutations in RNA-binding proteins can cause Mendelian diseases - prominently neuromuscular disorders and cancers. This study determines the interaction between RBPs and target-RNA complexes from public data of the ENCODE project and identifies mutations associated with Mendelian diseases that could disrupt the RBP-RNA interactions. Materials and methods: we performed a transcriptome - wide bioinformatics prediction of the binding sites of RBPs in the human transcriptome from public data of the ENCODE project. Results: The majority (54%) of pathogenic mutation putatively affecting the binding sites of RBPs are located in protein - coding genes and are mainly classified as loss - of - function mutations. Mutations located in the binding sites of RBPs related to RNA processing. For 13 diseases, Familial hypercholesterolemia is the most significant disease with about 40% of mutations in ClinVar database located into the binding sites of RBPs (p=2.3e-65), but congenital hypogonadotropic hypogonadism is the disease with the highest percentage of mutations affecting the binding sites of RBPs (98%, p=2.7e-25). The RBPs most involved in human Mendelian diseases by binding sites-disrupting mutations are YBX3, AQR and PRPF8. Conclusions: A large number of Mendelian diseases are potentially mediated by disease - causing variants that potentially disrupt the binding sites of RBPs. This will provide insight sharper on post - transcriptional mechanisms. Besides, it is useful to know the role of protein - RNA interactome networks in pathologies, thereby serving the treatment of diseases. Key words: bioinformatics analysis, ENCODE project, ClinVar, RNA-binding proteins, Mendelian diseases