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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,...
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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.
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Updated: Mar 20, 2026

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
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Identification of donor splice sites using support vector machine: a computational approach based on positional,

Prabina Kumar Meher1, Tanmaya Kumar Sahu2, A R Rao2

  • 1Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012 India.

Algorithms for Molecular Biology : AMB
|June 3, 2016
PubMed
Summary

This study introduces a new, cross-species method for identifying splice sites, crucial for gene annotation. The approach demonstrates high accuracy and comparability with existing methods, offering a valuable complementary tool.

Keywords:
HspliceHybrid approachMachine learningSequence encoding

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Accurate splice site identification is vital for gene annotation.
  • Current methods often lack cross-species compatibility and require improvement.
  • Developing species-agnostic splice site prediction tools is a significant challenge.

Purpose of the Study:

  • To develop a novel, accurate, and cross-species compatible approach for splice site prediction.
  • To improve upon existing splice site identification methods.
  • To provide a user-friendly tool for splice site prediction.

Main Methods:

  • Splice site sequences were converted into numerical vectors incorporating positional, dependency, and compositional features.
  • Support vector machine (SVM) was employed for prediction using these numerical vectors.
  • The approach was evaluated on diverse datasets including human, cattle, fish, and worm.

Main Results:

  • The proposed method achieved high Area Under the ROC Curve (AUC-ROC) and Area Under the PR Curve (AUC-PR) across multiple species, even with imbalanced datasets.
  • Consistent accuracy was observed across different species, indicating strong cross-species compatibility.
  • Performance was found to be comparable to state-of-the-art splice site prediction methods.

Conclusions:

  • The developed approach offers consistent and accurate splice site prediction across various species.
  • It serves as a valuable complementary tool to existing splice site prediction methods.
  • A web server, 'HSplice', has been developed for accessible 5' splice site prediction.