Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Conserved Binding Sites01:49

Conserved Binding Sites

5.0K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
5.0K
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

7.2K
Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form...
7.2K
RNA-seq03:21

RNA-seq

11.8K
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...
11.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same authorSame Topic

Benchmarking protein sequence and structure search methods for remote homology detection.

Genome biology·2026
Same author

Skin barrier dysfunction and correlation with the onset and progression of psoriasis.

Frontiers in immunology·2026
Same author

BindRNAgen: Protein-binding RNA Sequence Generation Using Latent Diffusion Models.

Journal of molecular biology·2026
Same author

DrugDL: dual-modal deep learning framework for multi-property drug prediction and targeted therapy discovery.

Bioinformatics (Oxford, England)·2026
Same author

Structure-driven RNA remodeling underlies broad substrate recognition by NSUN2.

Science China. Life sciences·2026
Same author

Dermal mesenchymal stem cells promote angiogenesis in HMEC-1 via activation of the angiopoietin 1/Tie2 pathway in psoriasis.

Frontiers in cell and developmental biology·2026

Related Experiment Video

Updated: Jan 19, 2026

PAR-CliP - A Method to Identify Transcriptome-wide the Binding Sites of RNA Binding Proteins
12:24

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

Published on: July 2, 2010

54.2K

CRIP: predicting circRNA-RBP-binding sites using a codon-based encoding and hybrid deep neural networks.

Kaiming Zhang1, Xiaoyong Pan2,3, Yang Yang1,4

  • 1Center for Brain-Like Computing and Machine Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

RNA (New York, N.Y.)
|September 21, 2019
PubMed
Summary

We developed CRIP, a machine learning tool to predict RNA-binding protein (RBP) binding sites on circular RNAs (circRNAs) using only RNA sequences. CRIP accurately identifies these crucial interactions, advancing gene regulation and disease research.

Keywords:
RNA–protein interactioncircular RNAcodon-based encodingdeep learning

More Related Videos

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions
10:52

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Published on: September 28, 2017

8.5K
In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
10:27

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

Published on: October 21, 2022

2.0K

Related Experiment Videos

Last Updated: Jan 19, 2026

PAR-CliP - A Method to Identify Transcriptome-wide the Binding Sites of RNA Binding Proteins
12:24

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

Published on: July 2, 2010

54.2K
Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions
10:52

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Published on: September 28, 2017

8.5K
In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
10:27

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

Published on: October 21, 2022

2.0K

Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • Circular RNAs (circRNAs) play vital roles in gene regulation and disease development.
  • Understanding circRNA-RNA-binding protein (RBP) interactions is key to elucidating their function.
  • Large-scale CLIP-seq data enables analysis of circRNA-RBP interactions, but computational tools are lacking.

Purpose of the Study:

  • To develop a machine learning-based computational tool for predicting RBP-binding sites on circRNAs using RNA sequences.
  • To introduce CRIP (CircRNAs Interact with Proteins), a novel tool for this prediction task.

Main Methods:

  • CRIP utilizes a stacked codon-based encoding scheme for RNA sequences.
  • A hybrid deep learning architecture combining Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) is employed.
  • Thirty-seven datasets were constructed, each containing sequence fragments of binding sites on circRNAs for a specific RBP.

Main Results:

  • The novel encoding scheme significantly outperforms existing RNA sequence feature representation methods.
  • The hybrid CNN-RNN model demonstrates superior performance compared to conventional classifiers.
  • Both CNN and RNN components were found to contribute to the improved prediction accuracy.

Conclusions:

  • CRIP provides an effective computational approach for predicting RBP-binding sites on circRNAs based solely on sequence information.
  • The developed tool and encoding scheme advance the large-scale analysis of circRNA-RBP interactions.
  • This work facilitates further research into the regulatory roles of circRNAs in biological processes and diseases.