A deep learning method for recovering missing signals in transcriptome-wide RNA structure profiles from probing experiments

RASP v2.0: an updated atlas for RNA structure probing data

RNA molecules function in numerous biological processes by folding into intricate structures. Here we present RASP v2.0, an updated database for RNA structure probing data featuring a substantially expanded collection of datasets along with enhanced online structural analysis functionalities. Compared to the previous version, RASP v2.0 includes the following improvements: (i) the number of RNA structure datasets has increased from 156 to 438, comprising 216 transcriptome-wide RNA structure datasets, 141 target-specific RNA structure datasets, and 81 RNA-RNA interaction datasets, thereby broadening species coverage from 18 to 24, (ii) a deep learning-based model has been implemented to impute missing structural signals for 59 transcriptome-wide RNA structure datasets with low structure score coverage, significantly enhancing data quality, particularly for low-abundance RNAs, (iii) three new online analysis modules have been deployed to assist RNA structure studies, including missing structure score imputation, RNA secondary and tertiary structure prediction, and RNA binding protein (RBP) binding prediction. By providing a resource of much more comprehensive RNA structure data, RASP v2.0 is poised to facilitate the exploration of RNA structure-function relationships across diverse biological processes. RASP v2.0 is freely accessible at http://rasp2.zhanglab.net/.

The improved de Bruijn graph for multitask learning: predicting functions, subcellular localization, and interactions of noncoding RNAs

Noncoding RNA refers to RNA that does not encode proteins. The lncRNA and miRNA it contains play crucial regulatory roles in organisms, and their aberrant expression is closely related to various diseases. Traditional experimental methods for validating the interactions of these RNAs have limitations, and existing prediction models exhibit relatively limited functionality, relying on isolated feature extraction and performing poorly in handling various types of small sample tasks. This paper proposes an improved de Bruijn graph that can inject RNA structural information into the graph while preserving sequence information. Furthermore, the improved de Bruijn graph enables graph neural networks to learn broader dependencies and correlations among data by introducing richer edge relationships. Meanwhile, the multitask learning model, DVMnet, proposed in this paper can handle multiple related tasks, and we optimize model parameters by integrating the total loss of three tasks. This enables multitask prediction of RNA interactions, disease associations, and subcellular localization. Compared with the best existing models in this field, DVMnet has achieved the best performance with a 3% improvement in the area under the curve value and demonstrates robust results in predicting diseases and subcellular localization. The improved de Bruijn graph is also applicable to various scenarios and can unify the sequence and structural information of various nucleic acids into a single graph.

Identification of RNA structures and their roles in RNA functions

The development of high-throughput RNA structure profiling methods in the past decade has greatly facilitated our ability to map and characterize different aspects of RNA structures transcriptome-wide in cell populations, single cells and single molecules. The resulting high-resolution data have provided insights into the static and dynamic nature of RNA structures, revealing their complexity as they perform their respective functions in the cell. In this Review, we discuss recent technical advances in the determination of RNA structures, and the roles of RNA structures in RNA biogenesis and functions, including in transcription, processing, translation, degradation, localization and RNA structure-dependent condensates. We also discuss the current understanding of how RNA structures could guide drug design for treating genetic diseases and battling pathogenic viruses, and highlight existing challenges and future directions in RNA structure research.

Studying Complex Evolution of Hyperelastic Materials under External Field Stimuli using Artificial Neural Networks with Spatiotemporal Features in a Small-Scale Dataset

Deep-learning (DL) methods, in consideration of their excellence in dealing with highly complex structure-performance relationships for materials, are expected to become a new design paradigm for breakthroughs in material performance. However, in most cases, it is impractical to collect massive-scale experimental data or open-source theoretical databases to support training DL models with sufficient prediction accuracy. In a dataset consisting of 483 porous silicone rubber observations generated via ink-writing additive manufacturing, this work demonstrates that constructing low-dimensional, accurate descriptors is the prerequisite for obtaining high-precision DL models based on small experimental datasets. On this basis, a unique convolutional bidirectional long short-term memory model with spatiotemporal features extraction capability is designed, whose hierarchical learning mechanism further reduces the requirement for the amount of data by taking full advantage of data information. The proposed approach can be expected as a powerful tool for innovative material design on small experimental datasets, which can also be used to explore the evolutionary mechanisms of the structures and properties of materials under complex working conditions.