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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

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...

Improving Translational Accuracy

Improving Translational Accuracy

Alternative RNA Splicing

Alternative 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...

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Related Experiment Video

Updated: Jan 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

Improving spliced alignment by modeling splice sites with deep learning.

Siying Yang^1,2, Neng Huang^1,2, Heng Li^1,2,3

¹Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck St, Boston, MA 02215, USA.

|September 22, 2025

Summary

This summary is machine-generated.

This study introduces minisplice, a new method using convolutional neural networks to improve spliced alignment accuracy for RNA and protein sequences. It enhances gene annotation and function studies, especially with noisy data.

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

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

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Analysis of Spliceosomal snRNA Localization in Human Hela Cells Using Microinjection

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Spliced alignment is crucial for gene annotation and function studies.
Current methods use simple splice site models, limiting accuracy with dissimilar sequences.

Purpose of the Study:

To develop a sophisticated model for splice site detection.
To improve the accuracy of spliced alignment in bioinformatics.

Main Methods:

Implemented minisplice, a 1D-CNN model trained on 7,026 parameters for vertebrate and insect genomes.
Estimated empirical splicing probabilities for GT/AG dinucleotides.
Modified minimap2 and miniprot to incorporate splicing probabilities.

Main Results:

The model captures conserved splice signals across phyla and identifies GC-rich introns in mammals and birds.
Improved junction accuracy was observed on human long-read RNA-seq data.
Enhanced accuracy for proteins with distant homology was achieved.

Conclusions:

Minisplice significantly improves spliced alignment accuracy, particularly for challenging datasets.
This advancement aids gene annotation and functional genomics research.