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Related Concept Videos

Pre-mRNA Processing: RNA Splicing01:32

Pre-mRNA Processing: 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...
RNA Splicing01:32

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...
RNA Splicing01:32

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...
Alternative RNA Splicing02:18

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...
Alternative RNA Splicing02:18

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...
Long-patch Base Excision Repair01:02

Long-patch Base Excision Repair

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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  2. Improving Splice Site Usage Prediction With Splaire.
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  2. Improving Splice Site Usage Prediction With Splaire.

Related Experiment Video

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
09:58

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

Published on: December 9, 2016

Improving splice site usage prediction with SPLAIRE.

Matthew Runyan, Saumya Gupta, Yul Leshaem

    Biorxiv : the Preprint Server for Biology
    |June 22, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    Deep learning models accurately predict splice sites, but struggle with low-usage and tissue-specific variants. A novel model trained on airway epithelial cells shows improved performance in splice site identification and usage quantification.

    More Related Videos

    A Reporter Based Cellular Assay for Monitoring Splicing Efficiency
    08:53

    A Reporter Based Cellular Assay for Monitoring Splicing Efficiency

    Published on: September 15, 2021

    Related Experiment Videos

    Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
    09:58

    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
    08:53

    A Reporter Based Cellular Assay for Monitoring Splicing Efficiency

    Published on: September 15, 2021

    Area of Science:

    • Genomics
    • Computational Biology
    • Molecular Biology

    Background:

    • Alternative splicing impacts over 95% of human genes and is crucial in disease.
    • The spliceosome identifies splice sites using sequence motifs at exon-intron junctions.
    • Deep learning models have advanced splice site prediction and pathogenic variant identification.

    Purpose of the Study:

    • To evaluate the performance of current splice site prediction models.
    • To develop a novel splicing model optimized for specific cell types.
    • To address limitations in predicting low-usage and tissue-specific splice sites.

    Main Methods:

    • Leveraged one of the largest paired RNA and genotyping datasets.
    • Trained a dilated convolutional neural network on human airway epithelial cell data from 100 donors.
  • Evaluated model performance on splice site identification and usage quantification.
  • Main Results:

    • Current deep learning models show substantial gaps in predicting low-usage and tissue-specific splice sites.
    • The novel model significantly outperforms state-of-the-art methods in splice site identification.
    • The model demonstrates superior splice site usage quantification, even on tissues not used in training.

    Conclusions:

    • A comprehensive evaluation reveals both strengths and weaknesses in current splicing models.
    • The developed model offers improved accuracy for splice site prediction and usage quantification.
    • Identified key areas for future development in splicing prediction models.