PubMed · 8804828
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
1996. Nucleic acids.. https://doi.org/10.1016/s0959-440x(96)80063-7
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
RNA secondary structure is essential for understanding the functions of non-coding RNAs, ribosomal RNAs, and viral genomes. However, accurate prediction of long RNA structures remains challenging due to complex long-range interactions and the limited availability of long-RNA training data. We present UFold-X, a dual-branch deep learning framework that combines a convolutional encoder for local structure modeling with a Mamba-based Visual State Space Module for capturing long-range dependencies. A dynamic gating mechanism adaptively integrates the two branches according to sequence length. UFold-X was evaluated on multiple benchmark datasets containing RNAs up to 5000 nucleotides. To rigorously assess generalization, we introduced a cross-clan benchmark for long RNAs. Under this stringent setting, UFold-X achieved performance comparable to state-of-the-art classical approaches while achieving the best performance among deep learning-based methods. Additional cross-family and within-family evaluations further demonstrated robust transferability and competitive predictive performance. UFold-X also maintained excellent computational efficiency, requiring only 0.08 s per sequence on average. To assess biological consistency, we developed a SHAPE-based reactivity prediction variant (UFold-X-R) and an integrated metric, the Hybrid Reactivity-Pairing Score (HRPS). UFold-X-R showed strong agreement with experimental icSHAPE data and achieved the highest HRPS among all evaluated methods. A user-friendly web server is available at https://ufold-x.ai4bread.com.
MOTIVATION: RNA molecules adopt dynamic ensembles of secondary structures, where the local structural context of each nucleotide-such as whether it resides in a stem or a specific type of loop-strongly shapes molecular interactions and regulatory function. Structural-context probabilities therefore provide a more functionally informative view of RNA folding than the minimum free energy structures or base-pairing probabilities. However, existing tools either require O(N3) time or employ span-restricted approximations that omit long-range base-pairs, limiting their applicability to large and biologically important RNAs. RESULTS: We introduce LinearCapR, enabling linear-time, span-unrestricted computation of structural-context marginalized probabilities, using beam-pruned Stochastic Context Free Grammar-based computation. LinearCapR retains global ensemble features lost by span-limited methods and yields superior predictive power on bpRNA-1m(90) dataset, especially for multiloops and exterior regions, as well as long-distance stems. LinearCapR supports analysis of long RNAs, demonstrated on the full genome of SARS-CoV-2. LinearCapR provides the first base-pair-span-unrestricted, linear-time framework for RNA structural-context analysis, retaining key thermodynamic ensemble features essential for functional interpretation. It enables large-scale studies of viral genomes, long non-coding RNAs, and downstream analyses such as RNA-binding protein site prediction. AVAILABILITY AND IMPLEMENTATION: The source code of LinearCapR is available at https://github.com/hoget157/LinearCapR. The archived software release used in this work is available at Zenodo: https://doi.org/10.5281/zenodo.19450645.
Eukaryotic rRNAs are produced by cleavage of a large 35 to 45 S pre-rRNA transcript which initially must be fully transcribed and assembled into an 80 to 90 S nucleolar ribonucleoprotein particle. Despite this need for a completed transcript, several investigations have reported a split processing scheme for independent maturation of the large and small subunit rRNAs. Here, an efficiently expressed rDNA plasmid was used to quantitatively analyze the effects of mutations in the internal transcribed spacer (ITS) region in the yeast, Schizosaccharomyces pombe. The results show that substitution of ITS regions inhibits the processing of distant external transcribed spacers (ETS) and that deletion of the ITS2 spacer not only prevents the maturation of the large subunit, but severely affects maturation of the small subunit rRNA. This indicates that the processing mechanisms are not fully split and, when taken together with other evidence of interdependences in rRNA maturation, the results suggest that the interdependences act as a quality control mechanism to help ensure that only functional rRNA is incorporated into ribosomes.