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RNA Splicing

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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...

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Related Experiment Video

Updated: Feb 17, 2026

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

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

Published on: December 9, 2016

Predicting human splicing branchpoints by combining sequence-derived features and multi-label learning methods.

Wen Zhang¹, Xiaopeng Zhu², Yu Fu³

¹School of Computer, Wuhan University, Wuhan, 430072, China. zhangwen@whu.edu.cn.

BMC Bioinformatics

|December 9, 2017

Summary

This summary is machine-generated.

Computational methods were developed to predict human splicing branchpoints, crucial indicators for alternative splicing. Two novel ensemble learning approaches demonstrated superior accuracy compared to existing methods in identifying these important genetic markers.

Keywords:

Genetic algorithm Human splicing branchpoint Logistic regression Multi-label learning

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Last Updated: Feb 17, 2026

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

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Published on: December 9, 2016

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

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Published on: June 24, 2021

Engineering Artificial Factors to Specifically Manipulate Alternative Splicing in Human Cells

Engineering Artificial Factors to Specifically Manipulate Alternative Splicing in Human Cells

Published on: April 26, 2017

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Alternative splicing is a key gene expression process.
Splicing branchpoints are vital indicators of alternative splicing.
Many human splicing branchpoints remain unidentified, necessitating predictive tools.

Purpose of the Study:

To develop computational methods for predicting human splicing branchpoints.
To guide and enhance experimental identification of splicing branchpoints.

Main Methods:

Intron sequences were analyzed for various features (e.g., profiles, motifs).
Multi-label learning methods (e.g., PLS, CCA) were employed.
Two ensemble learning schemes (genetic algorithm-based and logistic regression-based) were proposed.

Main Results:

The study treated branchpoint prediction as a multi-label learning problem.
Various sequence-derived features and multi-label classifiers were investigated.
Ensemble methods integrated diverse features and classifiers for improved prediction.

Conclusions:

The developed ensemble learning methods significantly outperformed benchmark prediction techniques.
High-accuracy predictions for human splicing branchpoints were achieved on a benchmark dataset.