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Alternative RNA Splicing02:18

Alternative RNA Splicing

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Alternative RNA Splicing02:18

Alternative RNA Splicing

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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.
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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RNA Splicing01:32

RNA Splicing

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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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Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

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Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
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Exon Recombination02:32

Exon Recombination

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The evolution of new genes is critical for speciation. Exon recombination, also known as exon shuffling or domain shuffling, is an important means of new gene formation. It is observed across vertebrates, invertebrates, and in some plants such as potatoes and sunflowers. During exon recombination, exons from the same or different genes recombine and produce new exon-intron combinations, which might evolve into new genes. 
Exon shuffling follows “splice frame rules.” Each exon...
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Related Experiment Video

Updated: Jan 1, 2026

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
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SimSpliceEvol: alternative splicing-aware simulation of biological sequence evolution.

Esaie Kuitche1, Safa Jammali2,3, Aïda Ouangraoua2

  • 1Department of Computer Science, University of Sherbrooke, 2500 Boulevard de l'Université, Quebec, J1K2R1, Canada. Esaie.Kuitche.Kamela@USherbrooke.ca.

BMC Bioinformatics
|December 18, 2019
PubMed
Summary

SimSpliceEvol simulates the evolution of alternative splicing and gene structure, generating crucial data for testing sequence analysis methods. This new tool aids in evaluating splice-aware bioinformatics tools by providing realistic evolutionary scenarios.

Keywords:
Alternative splicingEvolutionExon-intron structureSimulation

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Eukaryotic genes produce multiple transcripts via alternative splicing and transcription.
  • Lack of real data for alternative splicing necessitates simulated data for method evaluation.
  • Existing sequence evolution simulators do not model alternative splicing.

Purpose of the Study:

  • To develop a novel method for simulating the evolution of alternative splicing.
  • To generate realistic datasets for testing splice-aware sequence analysis tools.

Main Methods:

  • SimSpliceEvol simulates the evolution of alternative transcript sets along gene trees.
  • Incorporates gene exon-intron structure evolution and alternative splicing events.
  • Implemented in Python with freely available source code.

Main Results:

  • SimSpliceEvol successfully simulates the evolutionary dynamics of alternative splicing.
  • The method generates data reflecting both sequence and structural evolution.
  • Provides a valuable resource for benchmarking bioinformatics tools.

Conclusions:

  • Data from SimSpliceEvol are essential for testing spliced RNA sequence analysis methods.
  • Facilitates evaluation of spliced alignment, multiple sequence alignment, and phylogenetic inference tools.
  • Enables accurate assessment of methods requiring knowledge of real evolutionary relationships.