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Identifying inaccuracies in gene expression estimates from unstranded RNA-seq data.

Mikhail Pomaznoy1, Ashu Sethi2, Jason Greenbaum2

  • 1Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, United States. mikhail@lji.org.

Scientific Reports
|November 10, 2019
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Summary
This summary is machine-generated.

Ignoring RNA strand information in RNA sequencing (RNA-seq) can lead to inaccurate gene expression estimates for many genes. A new machine learning model helps identify these affected genes in unstranded datasets.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • RNA sequencing (RNA-seq) is a powerful tool for transcriptomic profiling.
  • Non-strand-specific RNA-seq protocols can lead to erroneous gene expression quantification.
  • A significant amount of existing RNA-seq data lacks strand information.

Purpose of the Study:

  • To identify genes with expression biases due to non-strand-specific RNA-seq.
  • To develop a method for detecting potential misquantification in unstranded RNA-seq data.
  • To provide a solution for accurate differential expression analysis with unstranded data.

Main Methods:

  • Utilized a comprehensive stranded RNA-seq dataset from 15 blood cell types.
  • Developed a machine learning model based on read alignment parameters to predict expression bias.
  • Implemented a strategy to correct biased expression estimates by considering reads spanning exonic boundaries.

Main Results:

  • Approximately 10% of all genes and 2.5% of protein-coding genes showed a two-fold or higher expression difference when strand information was ignored.
  • A machine learning model was successfully constructed to identify genes with potentially incorrect expression estimates in unstranded data.
  • Differential expression analysis accuracy was improved by filtering reads to those spanning exonic boundaries.

Conclusions:

  • Non-strand-specific RNA-seq can significantly impact gene expression quantification, affecting a substantial number of genes.
  • The developed machine learning approach effectively identifies genes susceptible to misquantification in unstranded datasets.
  • The provided package, uslcount, offers a reliable method for accurate transcriptomic analysis using unstranded RNA-seq data.