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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
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Outlier detection for improved differential splicing quantification from RNA-Seq experiments with replicates.

Scott S Norton1, Jorge Vaquero-Garcia1,2, Nicholas F Lahens3

  • 1Department of Genetics, Perelman School of Medicine.

Bioinformatics (Oxford, England)
|December 14, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel probability model for RNA-Seq analysis, effectively identifying and downweighting outlier samples. This approach enhances statistical power in differential splicing detection, improving upon existing methods.

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Area of Science:

  • Genomics and Bioinformatics
  • Molecular Biology
  • Computational Biology

Background:

  • RNA-Seq studies often involve comparing multiple replicates across experimental conditions to understand biological and experimental variability.
  • Identifying and handling 'bad' replicates is crucial but often ill-defined, potentially leading to data loss or analysis issues.

Purpose of the Study:

  • To develop a probability model for weighting RNA-Seq samples in alternative splicing analysis.
  • To detect and manage outlier samples that deviate significantly from their experimental condition.
  • To improve statistical power in differential splicing detection by downweighting problematic samples.

Main Methods:

  • Development of a probability model to assess sample representativeness within an experimental condition.
  • Implementation of a weighting scheme to downweight outlier samples and splicing variations.
  • Generalization of the MAJIQ algorithm for differential splicing analysis incorporating sample weights.

Main Results:

  • The model successfully identifies outlier samples consistently different from others in the same condition.
  • Downweighting outliers, instead of discarding samples, increases statistical power for differential splicing detection.
  • The generalized MAJIQ algorithm shows favorable performance against other tools using synthetic and real data.

Conclusions:

  • The developed outlier detection algorithm can be integrated into various splicing analysis pipelines.
  • A generalized and improved MAJIQ algorithm is provided for more robust differential splicing detection.
  • The study offers evaluation metrics, code, and data for differential splicing analysis.