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Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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AuPairWise: A Method to Estimate RNA-Seq Replicability through Co-expression.

Sara Ballouz1, Jesse Gillis1

  • 1Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America.

Plos Computational Biology
|April 16, 2016
PubMed
Summary
This summary is machine-generated.

RNA sequencing (RNA-Seq) replicability estimates can be misleading. A new method uses co-expressing genes as pseudo-replicates to accurately assess expression changes, improving RNA-Seq data analysis.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • RNA sequencing (RNA-Seq) is a powerful tool for transcriptomic analysis, with high replicability often cited as a key advantage.
  • Current methods for assessing RNA-Seq replicability, such as gene expression correlations, can yield misleading estimates, particularly for conditional expression variations.
  • Existing quality control heuristics for differential gene expression analysis involve stringent filtering or technical replicates, which can be costly or arbitrary.

Purpose of the Study:

  • To develop a more accurate method for estimating the replicability of gene expression changes in RNA-Seq data.
  • To address the limitations of current replicability assessment methods in transcriptomic studies.
  • To provide a sensitive and cost-effective approach for evaluating the reliability of differential gene expression results.

Main Methods:

  • Re-analysis of existing RNA sequencing data (ENCODE).
  • Development of a novel method modeling gene-level replicability using co-expressing genes as pseudo-replicates.
  • Modeling the impact of noise on gene expression within typical distributions.

Main Results:

  • Replicability of transcript abundances provides misleading estimates of conditional variation in gene expression.
  • Sets of co-expressing genes, particularly housekeeping interactions, serve as sensitive pseudo-replicates for estimating expression change replicability.
  • The proposed method can detect a 5% perturbation in gene expression with an Area Under the Receiver Operating Characteristic curve (AUROC) of approximately 0.73.
  • The method is available as a set of R scripts.

Conclusions:

  • Standard measures of RNA-Seq replicability can be unreliable for assessing differential expression.
  • Co-expressing gene sets offer a robust and sensitive approach to estimating expression change replicability.
  • This novel method enhances the reliability of RNA-Seq data analysis and quality control.