bpRNA: large-scale automated annotation and analysis of RNA secondary structure

By Padideh Danaee, Mason Rouches, M. Wiley, Dezhong Deng, Liang Huang, David A. Hendrix. 2018

219 citations recorded by the source corpus.

While RNA secondary structure prediction from sequence data has made remarkable progress, there is a need for improved strategies for annotating the features of RNA secondary structures. Here we present bpRNA, a novel annotation tool capable of parsing RNA structures, including complex pseudoknot-containing RNAs, to yield an objective, precise, compact, unambiguous, easily-interpretable description of all loops, stems, and pseudoknots, along with the positions, sequence, and flanking base pairs of each such structural feature. We also introduce several new informative representations of RNA structure types to improve structure visualization and interpretation. We have further used bpRNA to generate a web-accessible meta-database, “bpRNA-1m”, of over 100,000 single-molecule, known secondary structures; this is both more fully and accurately annotated and over 20-times larger than existing databases. We use a subset of the database with highly similar (≥90% identical) sequences filtered out to report on statistical trends in sequence, flanking base pairs, and length. Both the bpRNA method and the bpRNA-1m database will be valuable resources both for specific analysis of individual RNA molecules and large-scale analyses such as are useful for updating RNA energy parameters for computational thermodynamic predictions, improving machine learning models for structure prediction, and for benchmarking structure-prediction algorithms.

Subfields: Molecular Biology

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In Molecular Biology

1. Sheng Wang
2. Robin Pearce
3. Yaoqi Zhou
4. Jinbo Xu
5. Kuldip K. Paliwal
6. Yang Zhang
7. Jack Hanson
8. Siqi Sun

Citations in the corpus

• Accurate RNA 3D structure prediction using a language model-based deep learning approach (2022, positive): “To build our training dataset, we curated all available RNA 3D structures from the PDB, using the BGSU representative sets of RNA structures (version 2022-04-13) 24 .”
• RNA secondary structure prediction using deep learning with thermodynamic integration (2020, methodology): “To minimize the objective function (2), we employ the Adam optimizer35.”
• De Novo RNA Tertiary Structure Prediction at Atomic Resolution Using Geometric Potentials from Deep Learning (2022, methodology): “Since the number of solved RNA structures is low, we also collected a non-redundant distillation set of 16,842 RNAs from the bp-RNA-1m database 34 that were predicted to have regular secondary structures.”
• RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning (2019, methodology): “The datasets for initial training were obtained from bpRNA-1m (Version 1.0) 34 , which consists of 102,348 RNA sequences with annotated secondary structure.”