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Challenges in detecting and quantifying intron retention from next generation sequencing data.

Lucile Broseus1, William Ritchie1

  • 1IGH, Centre National de la Recherche Scientifique, University of Montpellier, Montpellier, France.

Computational and Structural Biotechnology Journal
|March 25, 2020
PubMed
Summary
This summary is machine-generated.

Intron retention (IR), where introns persist in mature mRNA, plays a key role in biology and disease. This study reviews computational methods for detecting IR from sequencing data, highlighting their varying assumptions and potential for different outcomes.

Keywords:
AS, alternative splicingBioinformaticsGene expressionIR, Intron retentionIntron retentionRNA sequencingRNA-seq, RNA sequencingmRNA splicing

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • Intron retention (IR) is a post-transcriptional regulatory mechanism where intronic sequences are retained in mature messenger RNA (mRNA).
  • IR has been increasingly recognized for its significant roles in various biological processes and its association with diverse diseases.
  • Understanding IR requires robust methods for its detection and analysis in transcriptomic data.

Purpose of the Study:

  • To provide a comprehensive overview of computational approaches for detecting intron retention events using sequencing data.
  • To analyze the biological and computational assumptions underlying different IR detection methods.
  • To discuss strategies for error mitigation and the identification of IR signatures across different experimental conditions.

Main Methods:

  • Review and categorization of existing computational tools and algorithms for intron retention detection.
  • Comparative analysis of the assumptions and methodologies employed by different IR detection approaches.
  • Exploration of techniques for validating IR events and identifying condition-specific IR patterns.

Main Results:

  • Computational methods for IR detection vary significantly based on their underlying biological and computational assumptions.
  • These variations can lead to substantially different results in identifying and quantifying IR events.
  • Effective strategies exist for reducing errors in IR detection and for discovering biologically relevant IR signatures.

Conclusions:

  • The choice of computational method significantly impacts the detection and interpretation of intron retention.
  • Awareness of method-specific assumptions is crucial for accurate IR analysis.
  • Further development and standardization of IR detection tools are needed to improve reliability and reproducibility in the field.