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

62.0K
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...
62.0K
RNA Splicing01:32

RNA Splicing

21.2K
21.2K
Pre-mRNA Processing: RNA Splicing01:32

Pre-mRNA Processing: RNA Splicing

7.5K
7.5K
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

20.5K
A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
20.5K
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

4.4K
4.4K
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

12.3K
Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
12.3K

You might also read

Related Articles

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

Sort by
Same author

OptimGS: a dual integrative genomic prediction framework for improving cold stress tolerance in wheat.

Briefings in bioinformatics·2026
Same author

Predictive role of noncoding RNAs in growth-development and stress mechanisms of <i>Amaranthus hypochondriacus</i>.

3 Biotech·2025
Same author

Ensemble of Bayesian alphabets via constraint weight optimization strategy improves genomic prediction accuracy.

G3 (Bethesda, Md.)·2025
Same author

Genetic dissection of leaf rust resistance in a diversity panel of tetraploid wheat (Triticum turgidum).

BMC plant biology·2025
Same author

PredPSP: a novel computational tool to discover pathway-specific photosynthetic proteins in plants.

Plant molecular biology·2024
Same author

Systematic profiling and analysis of growth and development responsive DE-lncRNAs in cluster bean (Cyamopsis tetragonoloba).

International journal of biological macromolecules·2024

Related Experiment Video

Updated: Apr 20, 2026

A Reporter Based Cellular Assay for Monitoring Splicing Efficiency
08:53

A Reporter Based Cellular Assay for Monitoring Splicing Efficiency

Published on: September 15, 2021

3.4K

A statistical approach for 5' splice site prediction using short sequence motifs and without encoding sequence data.

Prabina Kumar Meher1, Tanmaya Kumar Sahu2, Atmakuri Ramakrishna Rao3

  • 1Division of Statistical Genetics, Indian Agricultural Statistics Research Institute, New Delhi, 110012, India. meherprabin@yahoo.com.

BMC Bioinformatics
|November 26, 2014
PubMed
Summary

This study introduces a novel method for predicting donor splice sites using short sequence motifs, improving accuracy for next-generation sequencing data. The approach avoids data encoding and determines optimal window sizes for better splice variant identification.

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

14.5K
Merging Absolute and Relative Quantitative PCR Data to Quantify STAT3 Splice Variant Transcripts
11:19

Merging Absolute and Relative Quantitative PCR Data to Quantify STAT3 Splice Variant Transcripts

Published on: October 9, 2016

15.7K

Related Experiment Videos

Last Updated: Apr 20, 2026

A Reporter Based Cellular Assay for Monitoring Splicing Efficiency
08:53

A Reporter Based Cellular Assay for Monitoring Splicing Efficiency

Published on: September 15, 2021

3.4K
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

14.5K
Merging Absolute and Relative Quantitative PCR Data to Quantify STAT3 Splice Variant Transcripts
11:19

Merging Absolute and Relative Quantitative PCR Data to Quantify STAT3 Splice Variant Transcripts

Published on: October 9, 2016

15.7K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Current splice site prediction methods often use long sequence windows, limiting their utility with short reads from next-generation sequencing.
  • Machine learning approaches require numerical encoding, which can affect prediction accuracy and introduce variability.
  • The need for accurate splice site prediction with short motifs and without data encoding motivated this research.

Purpose of the Study:

  • To develop a novel approach for predicting donor splice sites.
  • To enable accurate prediction using short sequence motifs, suitable for next-generation sequencing data.
  • To bypass the need for numerical encoding of sequence data in splice site prediction.

Main Methods:

  • Developed an approach to identify associations among nucleotide bases within splice site motifs.
  • Determined appropriate window sizes based on nucleotide associations.
  • Proposed a donor splice site prediction method utilizing the sum of absolute error criterion.

Main Results:

  • The proposed approach demonstrated comparable accuracy to Maximum Entropy Modeling (MEM) and Maximal Dependency Decomposition (MDD).
  • It outperformed the Weighted Matrix Method (WMM) and first-order Markov Model (MM1) in prediction accuracy.
  • The method effectively uses short sequence motifs for splice site prediction.

Conclusions:

  • The developed prediction approach offers high accuracy for donor splice sites using short sequence motifs.
  • It serves as a valuable complementary method to existing splice site prediction tools.
  • A web server has been developed for user-friendly donor splice site prediction (http://cabgrid.res.in:8080/sspred).