ArticleNucleic acids research2020
DeepCLIP: predicting the effect of mutations on protein-RNA binding with deep learning.
Article in Nucleic acids research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers.
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Who cites it
63 citing papers in PubMed, 101 citations in OpenAlex.
- Bridging technical innovation and computational advances in studies of RNA-protein assemblies.Nature reviews. Genetics · 2026Review
- Bridging sequence-structure motifs and genetic variants for genome-wide dynamic RNA-protein interaction profiling.Nature communications · 2026Article
- Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity.Journal of cheminformatics · 2026Article
- Article
- Generation of functional noncanonical donor splice sites by +2T variants in breast cancer susceptibility genes: impact on clinical interpretation.The Journal of pathology · 2026Article
- Genetics of PLCG2 expression and splicing relative to Alzheimer's disease risk.Molecular neurodegeneration advances · 2026Article
- A therapeutic antisense oligonucleotide encompassing 2'-NAR molecular medicine · 2026Article
- mRNABERT: advancing mRNA sequence design with a universal language model and comprehensive dataset.Nature communications · 2025Article
- Unproductive alternative splicing of ATM exon 7: mapping of critical regulatory elements and identification of 34 spliceogenic variants.Journal of molecular medicine (Berlin, Germany) · 2025Article
- RNAtranslator: Modeling protein-conditional RNA design as sequence-to-sequence natural language translation.PLoS computational biology · 2025Article
- Decoding the interactions and functions of non-coding RNA with artificial intelligence.Nature reviews. Molecular cell biology · 2025Review
- hnRNP A1 induces aberrantLife science alliance · 2025Article
- Base-resolution binding profile prediction of proteins on RNAs with deep learning.Nucleic acids research · 2025Article
- RMDNet: RNA-aware dung beetle optimization-based multi-branch integration network for RNA-protein binding sites prediction.BMC bioinformatics · 2025Article
- LncPTPred: predicting lncRNA-protein interaction based on crosslinking and immunoprecipitation (CLIP-Seq) data.Briefings in bioinformatics · 2025Article
- TP53 minigene analysis of 161 sequence changes provides evidence for role of spatial constraint and regulatory elements on variant-induced splicing impact.NPJ genomic medicine · 2025Article
- RBPsuite 2.0: an updated RNA-protein binding site prediction suite with high coverage on species and proteins based on deep learning.BMC biology · 2025Article
- A deep learning model for characterizing protein-RNA interactions from sequences at single-base resolution.Patterns (New York, N.Y.) · 2025Article
- Emerging RNA-centric technologies to probe RNA-protein interactions: importance in decoding the life cycle of positive sense single strand RNA viruses and antiviral discovery.Frontiers in cellular and infection microbiology · 2025Review
- Splicing Analysis of Exonic TSC1 and TSC2 Gene Variants Causing Tuberous Sclerosis Complex.Human mutation · 2025Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
10 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nucleotide variants can cause functional changes by altering protein-RNA binding in various ways that are not easy to predict. This can affect processes such as splicing, nuclear shuttling, and stability of the transcript. Therefore, correct modeling of protein-RNA binding is critical when predicting the effects of sequence variations. Many RNA-binding proteins recognize a diverse set of motifs and binding is typically also dependent on the genomic context, making this task particularly challenging. Here, we present DeepCLIP, the first method for context-aware modeling and predicting protein binding to RNA nucleic acids using exclusively sequence data as input. We show that DeepCLIP outperforms existing methods for modeling RNA-protein binding. Importantly, we demonstrate that DeepCLIP predictions correlate with the functional outcomes of nucleotide variants in independent wet lab experiments. Furthermore, we show how DeepCLIP binding profiles can be used in the design of therapeutically relevant antisense oligonucleotides, and to uncover possible position-dependent regulation in a tissue-specific manner. DeepCLIP is freely available as a stand-alone application and as a webtool at http://deepclip.compbio.sdu.dk.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.