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

RNA Splicing01:32

RNA Splicing

60.9K
Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...
60.9K
Pre-mRNA Processing: RNA Splicing01:36

Pre-mRNA Processing: RNA Splicing

7.1K
7.1K
Long-patch Base Excision Repair01:02

Long-patch Base Excision Repair

8.1K
Since the discovery of the two BER pathways, there has been a debate about how a cell chooses one pathway over the other and the factors determining this selection. Numerous in vitro experiments have pointed out multiple determinants for the sub-pathway selection. These are:
8.1K
Structural Classification of Joints01:20

Structural Classification of Joints

7.7K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
7.7K
Chromatin Structure and RNA Splicing02:41

Chromatin Structure and RNA Splicing

3.5K
3.5K
Alternative RNA Splicing02:18

Alternative RNA Splicing

25.4K
Alternative RNA splicing is the regulated splicing of exons and introns to produce different mature mRNAs from a single pre-mRNA. Unlike in constitutive splicing where a single gene produces a single type of mRNA, alternative splicing allows an organism to produce multiple proteins from a single gene and plays an important role in protein diversity.
There are five types of alternative RNA splicing that vary in the ways the pre-mRNA segments are removed or retained in the mature mRNA. The first...
25.4K

You might also read

Related Articles

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

Sort by
Same author

Systematic evaluation of computational methods for cell segmentation.

Briefings in bioinformatics·2026
Same author

TriCLFF: a multi-modal feature fusion framework using contrastive learning for spatial domain identification.

Briefings in bioinformatics·2025
Same author

Deciphering cell-cell communication at single-cell resolution for spatial transcriptomics with subgraph-based graph attention network.

Nature communications·2024
Same author

The Deep Learning Framework iCanTCR Enables Early Cancer Detection Using the T-cell Receptor Repertoire in Peripheral Blood.

Cancer research·2024
Same author

DeepCCI: a deep learning framework for identifying cell-cell interactions from single-cell RNA sequencing data.

Bioinformatics (Oxford, England)·2023
Same author

An interpretable single-cell RNA sequencing data clustering method based on latent Dirichlet allocation.

Briefings in bioinformatics·2023

Related Experiment Video

Updated: Feb 24, 2026

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
08:35

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

6.5K

iSS-PC: Identifying Splicing Sites via Physical-Chemical Properties Using Deep Sparse Auto-Encoder.

Zhao-Chun Xu1, Peng Wang2, Wang-Ren Qiu3,4

  • 1Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, 333403, China. jdzxuzhaochun@163.com.

Scientific Reports
|August 17, 2017
PubMed
Summary

Identifying splicing sites in DNA/RNA sequences is crucial for biomedical research. A new computational method, iSS-PC, accurately predicts splice donor and acceptor sites, outperforming existing techniques.

More Related Videos

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
09:58

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Published on: December 9, 2016

14.4K
Utilization of Grafix for the Detection of Transient Interactors of Saccharomyces cerevisiae Spliceosome Subcomplexes
05:44

Utilization of Grafix for the Detection of Transient Interactors of Saccharomyces cerevisiae Spliceosome Subcomplexes

Published on: November 9, 2020

4.1K

Related Experiment Videos

Last Updated: Feb 24, 2026

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
08:35

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

6.5K
Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
09:58

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Published on: December 9, 2016

14.4K
Utilization of Grafix for the Detection of Transient Interactors of Saccharomyces cerevisiae Spliceosome Subcomplexes
05:44

Utilization of Grafix for the Detection of Transient Interactors of Saccharomyces cerevisiae Spliceosome Subcomplexes

Published on: November 9, 2020

4.1K

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Gene splicing, particularly RNA splicing, is a vital process in eukaryotic gene expression.
  • Accurate identification of splicing sites is essential for biomedical research and drug discovery.
  • Experimental methods for identifying splicing sites are time-consuming and costly, necessitating computational approaches.

Purpose of the Study:

  • To develop a rapid and accurate computational method for identifying splice donor and acceptor sites.
  • To improve upon existing prediction methods for splicing site identification.

Main Methods:

  • A deep sparse auto-encoder model (iSS-PC) with two hidden layers was constructed.
  • Incorporated twelve physical-chemical properties of dinucleotides into Pseudo-dinucleotide Composition (PseDNC).
  • Utilized cross-covariance and auto-covariance transformations to formulate sequence samples.

Main Results:

  • The iSS-PC model demonstrated superior performance compared to existing prediction methods.
  • Five-fold cross-validation tests on benchmark datasets confirmed the model's accuracy.
  • The predictor achieved high accuracy and speed in identifying splicing sites.

Conclusions:

  • The iSS-PC model offers a significant advancement in computational splicing site identification.
  • This approach is expected to be applicable to other related bioinformatics problems.
  • A user-friendly web server for iSS-PC is available for free access, facilitating research.