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RNA-targeting small molecules are emerging as promising therapeutic modalities, but their development requires methods that define binding sites and evaluate RNA target selectivity. Existing approache...

RNA structure probing and post-transcriptional modifications mapping high-throughput data analysis - dincarnato/RNAFramework

RNA structure probing and post-transcriptional modifications mapping high-throughput data analysis - dincarnato/RNAFramework

Mapping pairwise nucleotide dependencies by leveraging genomic language models highlights functional genomic elements and predicts deleterious genetic variants more effectively than alignment-based conservation metrics.

Transcriptome-scale maps of RNA secondary structure ensembles in living cells detect candidate RNA structural switches.

Structural RNAs exhibit a vast array of recurrent short 3D elements involving non-Watson-Crick interactions that help arrange canonical double helices into tertiary structures. We present CaCoFold-R3D...

RNA structure probing and post-transcriptional modifications mapping high-throughput data analysis - dincarnato/RNAFramework

Using a deep neural network and statistical analyses of atomic force microscopy images of individual RNA molecules enables the mapping of RNA conformational space in solution.

RNA structure probing and post-transcriptional modifications mapping high-throughput data analysis - dincarnato/RNAFramework

The molecular mechanisms of how small changes in the degree of inclusion of a neuron-specific microexon in CPEB4 lead to dominant-negative effects in the expression of genes associated with autism spe...

The results of the Fifth RNA-Puzzles contest highlights advances in RNA three-dimensional structure prediction and uncovers new insights into RNA folding and structure.

Dimethyl sulfate (DMS) chemical mapping is widely used for probing RNA structure, with low reactivity interpreted as Watson-Crick (WC) base pairs and high reactivity as unpaired nucleotides. Despite i...

RhoFold+ is an end-to-end language model-based deep learning method to predict RNA three-dimensional structures of single-chain RNAs from sequences.