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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...

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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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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
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Related Experiment Video

Updated: Jan 31, 2026

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

Discerning novel splice junctions derived from RNA-seq alignment: a deep learning approach.

Yi Zhang¹, Xinan Liu², James MacLeod³

¹Department of Computer Science, University of Kentucky, Lexington, KY, 40506, USA. yi.zhang@uky.edu.

|December 29, 2018

Summary

DeepSplice, a deep learning model, accurately classifies splice junctions from RNA sequencing data. It significantly reduces false positives, improving splice variant discovery and abundance estimation in transcriptomics.

Keywords:

Deep learning Exon splicing RNA-seq Splice junction

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Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Exon splicing is crucial for gene transcription, with RNA sequencing (RNA-seq) offering detailed transcriptome analysis.
Current ab initio aligners produce false positive splice junctions, complicating downstream analyses.
Accurate splice junction identification is essential for understanding gene structure and splicing variants.

Purpose of the Study:

To develop a deep learning model for accurate splice junction classification from RNA-seq data.
To improve the reliability of splice variant discovery and abundance estimation.
To reduce false positive predictions generated by existing alignment tools.

Main Methods:

A deep learning approach using convolutional neural networks (CNNs) was employed.
The model, DeepSplice, was trained on annotated exon junction sequences.
Candidate splice junctions were classified using DeepSplice.

Main Results:

DeepSplice demonstrated superior performance in splice site classification compared to state-of-the-art methods on the HS3D benchmark dataset.
High accuracy was achieved for splice junction classification using GENCODE annotation.
Application to 21,504 human RNA-seq datasets reduced 43 million candidate junctions to approximately 3 million confident novel junctions.

Conclusions:

A robust deep learning model, DeepSplice, was successfully implemented for classifying novel splice junctions from RNA-seq data.
The model shows reliable performance and broad usability for transcriptomic analyses.
DeepSplice effectively enhances the accuracy of splice junction identification, aiding in splice variant discovery.