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1. LinearPartition: linear-time approximation of RNA folding partition function and base-pairing probabilities

   https://doi.org/10.1093/bioinformatics/btaa460  

   Zhang, He; Zhang, Liang; Mathews, David H; Huang, Liang (July 2020, Bioinformatics)

   Abstract Motivation RNA secondary structure prediction is widely used to understand RNA function. Recently, there has been a shift away from the classical minimum free energy methods to partition function-based methods that account for folding ensembles and can therefore estimate structure and base pair probabilities. However, the classical partition function algorithm scales cubically with sequence length, and is therefore prohibitively slow for long sequences. This slowness is even more severe than cubic-time free energy minimization due to a substantially larger constant factor in runtime. Results Inspired by the success of our recent LinearFold algorithm that predicts the approximate minimum free energy structure in linear time, we design a similar linear-time heuristic algorithm, LinearPartition, to approximate the partition function and base-pairing probabilities, which is shown to be orders of magnitude faster than Vienna RNAfold and CONTRAfold (e.g. 2.5 days versus 1.3 min on a sequence with length 32 753 nt). More interestingly, the resulting base-pairing probabilities are even better correlated with the ground-truth structures. LinearPartition also leads to a small accuracy improvement when used for downstream structure prediction on families with the longest length sequences (16S and 23S rRNAs), as well as a substantial improvement on long-distance base pairs (500+ nt apart). Availability and implementation Code: http://github.com/LinearFold/LinearPartition; Server: http://linearfold.org/partition. Supplementary information Supplementary data are available at Bioinformatics online. 

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2. OUP accepted manuscript

   https://doi.org/10.1093/nar/gkab1074  

   Laiyi Fu, Yingxin Cao (November 2021, Nucleic Acids Research)

   null (Ed.)

   Motivation For many RNA molecules, the secondary structure is essential for the correct function of the RNA. Predicting RNA secondary structure from nucleotide sequences is a long-standing problem in genomics, but the prediction performance has reached a plateau over time. Traditional RNA secondary structure prediction algorithms are primarily based on thermodynamic models through free energy minimization, which imposes strong prior assumptions and is slow to run. Results Here we propose a deep learning-based method, called UFold, for RNA secondary structure prediction, trained directly on annotated data without any thermodynamic assumptions. UFold improves substantially upon previous models, with approximately 10~30% improvement over traditional thermodynamic models and 14% improvement over other learning-based methods. It achieves an F1 score of 0.91 on base pair prediction accuracy on an RNA structure prediction benchmark dataset. UFold is also fast with an inference time about 160ms per sequence up to 1600bp length. We provide an online web server that implements UFold for RNA structure prediction and is made freely available. Availability An online web server running UFold is available at https://ufold.ics.uci.edu. Code is available at https://github.com/uci-cbcl/UFold. 

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3. R2DT: a comprehensive platform for visualizing RNA secondary structure

   https://doi.org/10.1093/nar/gkaf032  

   McCann, Holly; Meade, Caeden D; Williams, Loren Dean; Petrov, Anton S; Johnson, Philip Z; Simon, Anne E; Hoksza, David; Nawrocki, Eric P; Chan, Patricia P; Lowe, Todd M; et al (February 2025, Nucleic Acids Research)

   Abstract RNA secondary (2D) structure visualization is an essential tool for understanding RNA function. R2DT is a software package designed to visualize RNA 2D structures in consistent, recognizable, and reproducible layouts. The latest release, R2DT 2.0, introduces multiple significant features, including the ability to display position-specific information, such as single nucleotide polymorphisms or SHAPE reactivities. It also offers a new template-free mode allowing visualization of RNAs without pre-existing templates, alongside a constrained folding mode and support for animated visualizations. Users can interactively modify R2DT diagrams, either manually or using natural language prompts, to generate new templates or create publication-quality images. Additionally, R2DT features faster performance, an expanded template library, and a growing collection of compatible tools and utilities. Already integrated into multiple biological databases, R2DT has evolved into a comprehensive platform for RNA 2D visualization, accessible at https://r2dt.bio. 

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4. Deep learning methods for protein function prediction

   https://doi.org/10.1002/pmic.202300471  

   Boadu, Frimpong; Lee, Ahhyun; Cheng, Jianlin (January 2025, PROTEOMICS)

   Abstract Predicting protein function from protein sequence, structure, interaction, and other relevant information is important for generating hypotheses for biological experiments and studying biological systems, and therefore has been a major challenge in protein bioinformatics. Numerous computational methods had been developed to advance protein function prediction gradually in the last two decades. Particularly, in the recent years, leveraging the revolutionary advances in artificial intelligence (AI), more and more deep learning methods have been developed to improve protein function prediction at a faster pace. Here, we provide an in‐depth review of the recent developments of deep learning methods for protein function prediction. We summarize the significant advances in the field, identify several remaining major challenges to be tackled, and suggest some potential directions to explore. The data sources and evaluation metrics widely used in protein function prediction are also discussed to assist the machine learning, AI, and bioinformatics communities to develop more cutting‐edge methods to advance protein function prediction. 

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5. Advancing viral RNA structure prediction: measuring the thermodynamics of pyrimidine-rich internal loops

   Phan A, Mailey K (February 2017, RNA)

   Accurate thermodynamic parameters improve RNA structure predictions and thus accelerate understanding of RNA function and the identification of RNA drug binding sites. Many viral RNA structures, such as internal ribosome entry sites, have internal loops and bulges that are potential drug target sites. Current models used to predict internal loops are biased toward small, symmetric purine loops, and thus poorly predict asymmetric, pyrimidine-rich loops with >6 nucleotides (nt) that occur frequently in viral RNA. This article presents new thermodynamic data for 40 pyrimidine loops, many of which can form UU or protonated CC base pairs. Uracil and protonated cytosine base pairs stabilize asymmetric internal loops. Accurate prediction rules are presented that account for all thermodynamic measurements of RNA asymmetric internal loops. New loop initiation terms for loops with >6 nt are presented that do not follow previous assumptions that increasing asymmetry destabilizes loops. Since the last 2004 update, 126 new loops with asymmetry or sizes greater than 2 × 2 have been measured. These new measurements significantly deepen and diversify the thermodynamic database for RNA. These results will help better predict internal loops that are larger, pyrimidine-rich, and occur within viral structures such as internal ribosome entry sites. 

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