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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...

Chromatin Structure and RNA Splicing

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Pre-mRNA Processing: Modification of pre-mRNA Ends

Pre-mRNA Processing: Modification of pre-mRNA Ends

In eukaryotic cells, transcripts made by RNA polymerase are modified and processed before exiting the nucleus. Unprocessed RNA is called precursor mRNA or pre-mRNA to distinguish it from mature mRNA.
Once about 20-40 ribonucleotides have been joined together by RNA polymerase, a group of enzymes adds a cap to the 5' end of the growing transcript. In this process, a 5' phosphate is replaced by modified guanosine that has a methyl group attached (7-methyl guanosine). This 5' cap helps...

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Since the discovery of the two BER pathways, there has been a debate about how a cell chooses one pathway over the other and the factors determining this selection. Numerous in vitro experiments have pointed out multiple determinants for the sub-pathway selection. These are:

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Updated: Jun 13, 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

Splam: a deep-learning-based splice site predictor that improves spliced alignments.

Kuan-Hao Chao^1,2, Alan Mao^3,4,5, Steven L Salzberg^3,4,5,6

¹Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA. kh.chao@cs.jhu.edu.

|September 16, 2024

Summary

This summary is machine-generated.

We developed Splam, a new deep learning method for predicting splice junctions in DNA. Splam achieves 96% accuracy in human splice junction prediction, outperforming existing methods.

Keywords:

Convolutional neural network Deep learning Dilated convolution Residual block Splice site predictor Spliced alignment

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Last Updated: Jun 13, 2025

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

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:

Messenger RNA (mRNA) splicing is a critical process for gene expression, involving the removal of introns to form mature mRNA.
Splice junctions, the boundaries between exons and introns, are essential for accurate gene and gene variant formation.
Accurate prediction of splice junctions is crucial for understanding gene regulation and disease mechanisms.

Purpose of the Study:

To introduce Splam, a novel deep learning method for predicting splice junctions in DNA.
To improve the accuracy and biological relevance of splice junction prediction models.
To provide a more effective tool for analyzing gene splicing processes.

Main Methods:

Development of Splam, a deep residual convolutional neural network model.
Utilizing a 400-base-pair window flanking each splice site to capture relevant biological signals.
Training the model on both donor and acceptor splice sites simultaneously to mimic the splicing machinery's recognition process.

Main Results:

Splam demonstrates high accuracy in predicting human splice junctions, achieving 96% accuracy.
The method outperforms existing models, such as SpliceAI, in predictive accuracy.
The model's design, incorporating flanking sequence windows and joint training, reflects key aspects of biological splicing.

Conclusions:

Splam represents a significant advancement in splice junction prediction accuracy.
The method's biologically informed approach enhances its reliability and utility in genomic research.
Splam offers a powerful new tool for investigating gene splicing and its role in genetic diversity and disease.