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Updated: Jul 12, 2025

A Rapid High-throughput Method for Mapping Ribonucleoproteins RNPs on Human pre-mRNA

A Rapid High-throughput Method for Mapping Ribonucleoproteins RNPs on Human pre-mRNA

Published on: December 2, 2009

DeepRaccess: high-speed RNA accessibility prediction using deep learning.

Kaisei Hara^1,2, Natsuki Iwano¹, Tsukasa Fukunaga³

¹Department of Electrical Engineering and Bioscience, Graduate School of Advanced Science and Engineering, Waseda University, Tokyo, Japan.

Frontiers in Bioinformatics

|October 26, 2023

Summary

This summary is machine-generated.

DeepRaccess, a new deep learning tool, accurately predicts RNA accessibility and protein abundance in E.coli. It offers significant speed-up for transcriptome-scale analysis compared to traditional methods.

Keywords:

RNA accessibility RNA secondary structure acceleration machine learning translation efficiency prediction

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RNA Secondary Structure Prediction Using High-throughput SHAPE

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

RNA secondary structure, specifically RNA accessibility, is crucial for predicting RNA-RNA interactions and translation efficiency in prokaryotes.
Existing tools like Raccess are computationally intensive, limiting their application in large-scale transcriptome analyses.

Purpose of the Study:

To develop a computationally efficient deep learning model, DeepRaccess, for predicting RNA accessibility.
To evaluate DeepRaccess's accuracy against established methods and its ability to predict biological outcomes like protein abundance.

Main Methods:

Developed DeepRaccess using deep learning, trained on artificial RNA sequences.
Validated predictions against Raccess calculations using simulation and empirical datasets.
Assessed DeepRaccess's correlation with protein abundance in E.coli near the start codon.

Main Results:

DeepRaccess predictions showed high correlation with Raccess-calculated accessibility.
The model demonstrated moderate accuracy in predicting E.coli protein abundance from RNA sequences.
Achieved significant computational speed-up (tens to hundreds of times) in a GPU environment.

Conclusions:

DeepRaccess provides an accurate and computationally efficient alternative for RNA accessibility prediction.
The tool has potential applications in predicting translation efficiency and protein abundance.
Freely available source code and models facilitate broader research use.