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lncRNA - Long Non-coding RNAs

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...

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Updated: Jan 4, 2026

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

Identifying Cancer-Specific circRNA-RBP Binding Sites Based on Deep Learning.

Zhengfeng Wang^1,2, Xiujuan Lei¹, Fang-Xiang Wu³

¹School of Computer Science, Shaanxi Normal University, Xi'an 710119, China.

Molecules (Basel, Switzerland)

|November 10, 2019

Summary

A new deep learning method, CSCRSites, identifies cancer-specific circular RNA-RNA binding protein (circRNA-RBP) binding sites using only nucleotide sequences. This tool aids in understanding circRNA functions in cancer by predicting binding interactions.

Keywords:

RNA binding protein cancer-specific circRNA convolutional neural network

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Last Updated: Jan 4, 2026

Identification of Circular RNAs using RNA Sequencing

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Published on: November 14, 2019

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PAR-CliP - A Method to Identify Transcriptome-wide the Binding Sites of RNA Binding Proteins

PAR-CliP - A Method to Identify Transcriptome-wide the Binding Sites of RNA Binding Proteins

Published on: July 2, 2010

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Circular RNAs (circRNAs) are prevalent in cells and tissues, regulating biological processes and human diseases.
circRNAs can act as RNA binding protein (RBP) sponges, and RBPs influence circRNA back-splicing.
Understanding circRNA-RBP interactions is vital for elucidating circRNA functions, particularly in cancer.

Purpose of the Study:

To develop a novel deep learning-based method for identifying cancer-specific circRNA-RBP binding sites.
To utilize only nucleotide sequences as input for the prediction model.
To facilitate the functional analysis of circRNAs associated with human cancers.

Main Methods:

A deep learning architecture, CSCRSites, employing multiple convolution layers to extract features from raw circRNA sequences.
A fully connected layer with softmax output for identifying circRNA-RBP binding sites.
Comparison of CSCRSites performance against conventional machine learning and other deep learning methods.

Main Results:

CSCRSites demonstrated superior performance compared to existing methods on benchmark datasets.
Learned features from CSCRSites were converted into sequence motifs.
Identified motifs showed similarity to known human RNA motifs implicated in diseases, especially cancer.

Conclusions:

CSCRSites is an effective deep learning tool for predicting cancer-specific circRNA-RBP binding sites.
The method contributes to the functional analysis of cancer-associated circRNAs.
The findings highlight the potential of deep learning in uncovering complex RNA-protein interactions in disease contexts.