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Related Concept Videos

RNA Structure01:23

RNA Structure

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Overview
The basic structure of RNA consists of a five-carbon sugar and one of four nitrogenous bases. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
Different Types of RNA Have the Same Basic Structure
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The basic structure of RNA consists of a string of ribonucleotides attached by phosphodiester bonds. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
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RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Nucleic Acid Structure01:25

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The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms  a 5′ to 3′ phosphodiester linkage.
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RNA Stability01:53

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Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...
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RNA editing is a post-transcriptional modification where a precursor mRNA (pre-mRNA) nucleotide sequence is changed by base insertion, deletion, or modification. The extent of RNA editing varies from a few hundred bases, in mitochondrial DNA of trypanosomes, to a just single base, in nuclear genes of mammals. Even a single base change in the pre-mRNA can convert a codon for one amino acid into the codon for another amino acid or a stop codon. This type of re-coding can significantly affect the...
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ERNIE-RNA: an RNA language model with structure-enhanced representations.

Weijie Yin1, Zhaoyu Zhang1, Shuo Zhang1

  • 1MOE Key Laboratory of Bioinformatics and Bioinformatics Division, Center for Synthetic and Systems Biology, Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China.

Nature Communications
|November 18, 2025
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ERNIE-RNA, a novel RNA language model, integrates structural information for enhanced RNA sequence analysis. This model achieves state-of-the-art performance in predicting RNA structure and function.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Existing RNA language models (RLMs) often neglect crucial structural information in RNA sequences.
  • This oversight leads to incomplete feature extraction and suboptimal performance on various biological tasks.

Purpose of the Study:

  • To introduce ERNIE-RNA, an enhanced RNA language model incorporating base-pairing restrictions.
  • To demonstrate ERNIE-RNA's superior capability in capturing RNA structural features and improving downstream task performance.

Main Methods:

  • ERNIE-RNA is built upon a modified Bidirectional Encoder Representations from Transformers (BERT) architecture.
  • The model utilizes attention mechanisms to capture RNA structural information, enabling zero-shot prediction.

Main Results:

  • ERNIE-RNA's attention maps show enhanced ability to capture RNA structural features compared to traditional methods like RNAfold and RNAstructure.
  • Fine-tuned ERNIE-RNA achieves state-of-the-art results in RNA structure and function prediction tasks.
  • The model demonstrates natural development of comprehensive RNA architecture representations during pre-training.

Conclusions:

  • ERNIE-RNA offers versatile features for diverse RNA research applications.
  • Integrating knowledge-based priors into BERT-like frameworks can significantly improve language model performance for biological sequences.