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Related Concept Videos

Alternative RNA Splicing02:18

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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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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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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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Updated: Jan 13, 2026

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
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Detection of alternative splicing: deep sequencing or deep learning?

Lena Maria Hackl1, Fabian Neuhaus1, Sabine Ameling2

  • 1Institute for Computational Systems Biology, University of Hamburg, Albert-Einstein-Ring 8-10, Hamburg 22761, Hamburg, Germany.

Briefings in Bioinformatics
|January 11, 2026
PubMed
Summary

Deep learning models can detect alternative splicing events from low-depth RNA sequencing data, aiding disease research. Validation with high-depth sequencing is still crucial for confirming these alternative splicing findings.

Keywords:
RNA sequencingRNA splicingalternative splicing predictioncomputational transcriptomicsdeep learning modelslow sequencing depth

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Alternative splicing regulates gene expression, producing diverse isoforms crucial for tissue-specific functions.
  • Dysregulation of alternative splicing is implicated in diseases like cancer and neurological disorders.
  • Accurate detection of alternative splicing events is challenging, often requiring high-depth sequencing data.

Purpose of the Study:

  • To investigate the utility of publicly available, low-depth RNA sequencing data for comprehensive alternative splicing detection.
  • To evaluate the potential of deep learning models for predicting alternative splicing events.

Main Methods:

  • Utilized sequence-based deep learning tools (AlphaGenome, SpliceAI, DeepSplice) to analyze RNA sequencing data.
  • Assessed the performance of these tools on low-depth sequencing datasets.
  • Compared deep learning predictions with standard RNA sequencing analysis pipelines.

Main Results:

  • Deep learning models show potential for initial alternative splicing event detection and hypothesis generation using low-depth data.
  • These tools can serve as valuable filters in standard RNA sequencing pipelines when sequencing depth is limited.
  • The study highlights the effectiveness of sequence-based deep learning for alternative splicing prediction.

Conclusions:

  • Deep learning approaches offer a promising strategy for alternative splicing detection in resource-limited settings.
  • Integrative methods combining genomic sequence and RNA sequencing data are essential for accurate prediction.
  • Further validation with high-depth sequencing is necessary to confirm identified splice events.