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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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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.
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Related Experiment Video

Updated: Mar 13, 2026

Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA

Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA

Published on: July 9, 2021

Retentive Network promotes efficient RNA language modeling of long sequences.

Yi Shen¹, Guangshuo Cao², Yueming Hu¹

¹Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, China.

Communications Biology

|March 12, 2026

Summary

This summary is machine-generated.

RNAret, a novel RNA language model, overcomes Transformer limitations for long RNA sequences. This efficient model excels in predicting RNA interactions, structures, and classifications, advancing RNA biology understanding.

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Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Latent features of RNA sequences are vital for understanding their functions.
Transformer models are widely used for nucleotide language modeling but have O(n²) complexity, limiting long sequence processing.

Purpose of the Study:

To propose RNAret, an efficient RNA language model with O(n) complexity.
To enable effective processing of long RNA sequences for feature extraction and biological insights.

Main Methods:

Developed RNAret based on the Retention Network mechanism.
Pretrained RNAret using self-supervised masked language modeling on 29.8 million RNA sequences.
Achieved training parallelism, low computational overhead, and long-sequence processing.

Main Results:

RNAret demonstrated superior performance on RNA-RNA interaction prediction.
Achieved high accuracy in RNA secondary structure prediction.
Showcased effectiveness in mRNA/lncRNA classification tasks.

Conclusions:

RNAret is a powerful RNA language model with O(n) complexity, suitable for long sequences.
The model effectively extracts latent features from RNA sequences.
RNAret holds significant potential for advancing RNA biology research.