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Related Concept Videos

RNA-seq03:21

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Related Experiment Video

Updated: Aug 29, 2025

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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ABEILLE: a novel method for ABerrant Expression Identification empLoying machine LEarning from RNA-sequencing data.

Justine Labory1,2, Gwendal Le Bideau2, David Pratella1

  • 1Université Côte d'Azur, Center of Modeling, Simulation and Interactions, Nice 06000, France.

Bioinformatics (Oxford, England)
|September 5, 2022
PubMed
Summary

This study introduces ABEILLE, a machine learning tool for identifying aberrant gene expression (AGE) in rare disease diagnosis. ABEILLE utilizes a variational autoencoder (VAE) to detect potential pathogenic genes from RNA-seq data without requiring control groups or replicates.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Omics technologies advance rare disease diagnosis by identifying causative genes.
  • Transcriptomic data analysis for aberrant gene expression (AGE) can reveal pathogenic events.
  • Current AGE identification methods rely on arbitrary statistical cut-offs and require multiple replicates, which are often unavailable in clinical settings.

Purpose of the Study:

  • To develop a novel machine learning-based method for identifying aberrant gene expression (AGE) from RNA-seq data.
  • To overcome limitations of existing AGE identification approaches, specifically the need for replicates and control groups.
  • To provide a flexible and robust tool for identifying potential pathogenic genes in rare disease research.

Main Methods:

  • Developed ABerrant Expression Identification empLoying machine LEarning from sequencing data (ABEILLE), a variational autoencoder (VAE)-based method.
  • Utilized a VAE to model RNA-seq data without distributional assumptions, combined with a decision tree for gene classification.
  • Incorporated an anomaly score to stratify identified aberrant genes by severity.

Main Results:

  • ABEILLE successfully identified aberrant gene expression (AGE) from RNA-seq data without requiring replicates or a control group.
  • The method demonstrated flexibility in VAE configuration for identifying potential pathogenic candidates.
  • Performance was validated on both semi-synthetic and experimental datasets.

Conclusions:

  • ABEILLE offers a powerful, flexible, and data-efficient approach for identifying aberrant gene expression in rare disease contexts.
  • The VAE-based methodology overcomes key limitations of traditional statistical methods for AGE detection.
  • ABEILLE facilitates the discovery of novel genetic factors in rare diseases through advanced transcriptomic analysis.