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RNA-seq

RNA-seq

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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RNA Structure

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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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Nucleic Acid Structure

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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Updated: Apr 28, 2026

DNA-Tethered RNA Polymerase for Programmable In vitro Transcription and Molecular Computation

DNA-Tethered RNA Polymerase for Programmable In vitro Transcription and Molecular Computation

Published on: December 29, 2021

Modular Deep Learning for Direct RNA Sequence Design via Self-Contained RNA Units.

Jian Wang, Nikolay V Dokholyan

Biorxiv : the Preprint Server for Biology

|April 27, 2026

Summary

Designing RNA sequences is crucial for synthetic biology. This study introduces SCRU-DB, a database of RNA units, enabling faster and more accurate RNA sequence design with new deep learning models like SCRU-Seq and SCRU-Diff.

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2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications

2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications

Published on: July 10, 2020

Area of Science:

Synthetic biology
Computational biology
Structural biology

Background:

Deep learning for RNA sequence design is limited by scarce high-resolution 3D structure data.
Existing methods like NA-MPNN and RiboDiffusion are computationally intensive due to limited training data.

Purpose of the Study:

To address the data scarcity and granularity issues in RNA sequence design.
To develop a scalable and physically grounded framework for generating accurate RNA sequences.

Main Methods:

Introduction of SCRU-DB, a database of over 61,000 Self-contained RNA Units (SCRUs) derived from tertiary contact clustering.
Development of SCRU-Seq (a direct GNN) and SCRU-Diff (an iterative diffusion model) leveraging the SCRU-DB.
Validation of structural fidelity using 3D backbone superposition and C4' RMSD.

Main Results:

SCRU-DB contains over 8,200 unique structural clusters of SCRUs.
SCRU-Seq achieved 63.7% native sequence recovery (NSR) on the set112 benchmark.
SCRU-Diff achieved a superior Best NSR of 79.2% with high structural fidelity (1.5Å C4' RMSD).

Conclusions:

The SCRU-DB framework provides a scalable solution for RNA sequence design by utilizing modular RNA units.
SCRU-Seq and SCRU-Diff demonstrate improved accuracy and efficiency in generating structurally sound RNA sequences.
The modular approach ensures the generation of diverse and physically realistic RNA sequences.