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RNA Splicing

RNA Splicing

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...

Pre-mRNA Processing: RNA Splicing

Pre-mRNA Processing: RNA Splicing

Alternative RNA Splicing

Alternative RNA Splicing

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...

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Chromatin Structure and RNA Splicing

Chromatin Structure and RNA Splicing

Single Nucleotide Polymorphisms-SNPs

Single Nucleotide Polymorphisms-SNPs

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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Related Experiment Video

Updated: Aug 26, 2025

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

EnsembleSplice: ensemble deep learning model for splice site prediction.

Victor Akpokiro¹, Trevor Martin², Oluwatosin Oluwadare³

¹Department of Computer Science, University of Colorado, Colorado Springs, CO, 80918, USA.

BMC Bioinformatics

|October 6, 2022

Summary

EnsembleSplice, a novel ensemble learning model, significantly improves splice site detection accuracy in genomic DNA sequencing. This convolutional neural network (CNN) architecture enhances prediction accuracy and reduces error rates for biomedical research.

Keywords:

Convolutional neural network (CNN)Deep learning (DL)Dense neural network (DNN)Ensemble learning Feature extraction Splice sites (SS)

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Last Updated: Aug 26, 2025

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

A Reporter Based Cellular Assay for Monitoring Splicing Efficiency

A Reporter Based Cellular Assay for Monitoring Splicing Efficiency

Published on: September 15, 2021

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Accurate splice site identification is crucial for genomic DNA sequencing in biomedical and pharmaceutical research.
While neural networks show promise, improvements in splice site prediction accuracy and error rates are still needed.

Purpose of the Study:

To develop an advanced computational model for highly accurate splice site detection.
To enhance existing splice site prediction methods by improving accuracy and reducing error rates.

Main Methods:

An ensemble learning architecture, EnsembleSplice, was developed using four distinct convolutional neural network (CNN) models.
Models were trained and evaluated using a five-fold cross-validation approach on human and Arabidopsis thaliana genomic datasets.

Main Results:

EnsembleSplice demonstrated superior performance compared to existing methods in splice site detection.
Achieved 94.16% accuracy for acceptor and 95.97% accuracy for donor splice sites in human datasets.
Reported error rates of 5.84% for acceptor and 4.03% for donor splice sites in human datasets.

Conclusions:

The five-fold cross-validation confirmed the consistent prediction accuracy of the developed models.
All datasets, models, and results are publicly available on GitHub for reproducibility.