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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.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Oct 3, 2025

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

HCRNet: high-throughput circRNA-binding event identification from CLIP-seq data using deep temporal convolutional

Yuning Yang¹, Zilong Hou², Yansong Wang²

¹School of Information Science and Technology, Northeast Normal University, Changchun, Jilin, China.

Briefings in Bioinformatics

|February 21, 2022

Summary

This summary is machine-generated.

A new computational model, HCRNet, accurately identifies genome-wide binding events between circular RNAs (circRNAs) and RNA-binding proteins (RBPs), advancing understanding of circRNA functions.

Keywords:

circRNA-RBP binding event identification deep learning deep temporal convolutional network natural language processing

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Last Updated: Oct 3, 2025

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution

iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution

Published on: April 30, 2011

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

Published on: October 5, 2018

Area of Science:

Computational biology
Genomics
Bioinformatics

Background:

Circular RNAs (circRNAs) and RNA-binding proteins (RBPs) play crucial roles in gene regulation.
Understanding circRNA-RBP interactions is key to deciphering circRNA functions.
Existing computational models lack flexibility for diverse circRNA data scales and feature representations.

Purpose of the Study:

To develop a novel, flexible, and high-performance computational framework for identifying circRNA-RBP binding events.
To improve the prediction accuracy and interpretability of circRNA-RBP interactions.
To provide a tool that accommodates various data scales and feature representations of circRNAs.

Main Methods:

Developed HCRNet, an end-to-end framework for circRNA-RBP binding event identification.
Fused multi-source biological information, including natural language sequence features, to represent circRNAs.
Employed a deep temporal convolutional network with global expectation pooling to analyze nucleotide dependencies.

Main Results:

HCRNet demonstrated superior performance compared to existing methods on 37 circRNA and 31 linear RNA datasets.
Model robustness was confirmed on a large dataset of 740 full-length circRNAs.
Motif analyses confirmed the interpretability of HCRNet in predicting circRNA-RBP binding sites.

Conclusions:

HCRNet offers a flexible and effective solution for identifying circRNA-RBP binding events.
The framework enhances the understanding of circRNA functional mechanisms.
HCRNet provides a valuable tool for genomic and bioinformatics research.