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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...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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...
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...

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Related Experiment Video

Updated: May 14, 2026

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
08:35

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Benchmarking foundation models for splice site and exon annotation.

Zitong He1, Liliana Florea1,2

  • 1Department of Computer Science, Johns Hopkins University, Baltimore, MD 21205.

Biorxiv : the Preprint Server for Biology
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

Foundation and deep learning models excel at genome annotation but struggle with complex gene elements not well-represented in training data. Specialized models are needed for diverse gene feature annotation, including transposable element-derived exons.

Keywords:
BenchmarkingExonFoundation ModelsSplice Site

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

Published on: December 9, 2016

Related Experiment Videos

Last Updated: May 14, 2026

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
08:35

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

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

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Foundation and deep learning models have advanced genome annotation, identifying genes and splice sites.
  • Current models are trained on limited datasets, potentially missing biological complexity in diverse gene elements.

Purpose of the Study:

  • Evaluate foundation models for gene and splice site annotation across various exon classes.
  • Assess model performance on coding, non-coding, constitutive, alternatively spliced, and transposable element-derived exons.

Main Methods:

  • Evaluated transformer-based (SegmentNT, Enformer, Borzoi) and CNN-based (SpliceAI, AlphaGenome) models.
  • Introduced and tested a fine-tuned CNN model (STEP2h) on specialized transposable element-exon data.

Main Results:

  • Model performance was highest for exon classes present in training data (e.g., protein-coding, constitutive exons).
  • Performance significantly decreased (2-4 fold) for poorly represented classes like non-coding, terminal, and alternatively spliced exons.
  • Performance was also impaired on transposable element-derived exons, but a specialized fine-tuned model showed improvement.

Conclusions:

  • Foundation models face challenges annotating diverse gene features not well-represented in training data.
  • Further fine-tuning on specific data classes or developing task-specific models is crucial for comprehensive gene feature annotation.