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how_are_we_stranded_here: quick determination of RNA-Seq strandedness.

Brandon Signal1, Tim Kahlke2

  • 1School of Medicine, College of Health and Medicine, University of Tasmania, Hobart, TAS, Australia. brandon.signal@utas.edu.au.

BMC Bioinformatics
|January 23, 2022
PubMed
Summary

The how_are_we_stranded_here Python library quickly determines RNA-Sequencing (RNA-Seq) read strandedness. This tool aids in identifying potential sample contamination and improves downstream analysis accuracy.

Keywords:
BioinformaticsQuality controlRNA-Sequencing

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Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • RNA-Sequencing (RNA-Seq) quality control is crucial for identifying issues in sequenced reads.
  • Common checks include sequence quality, contamination, and complexity.
  • Strand-specificity is often overlooked but critical for accurate and reproducible RNA-Seq analysis.

Purpose of the Study:

  • To develop a tool for inferring the strandedness of paired-end RNA-Sequencing data.
  • To provide a user-friendly method for assessing strand-specificity in RNA-Seq experiments.

Main Methods:

  • Development of the 'how_are_we_stranded_here' Python library.
  • Testing the library on both simulated and real RNA-Sequencing datasets.

Main Results:

  • The 'how_are_we_stranded_here' library accurately infers strandedness in paired-end RNA-Seq data.
  • Deviations from expected strandedness measures can indicate sample contamination.

Conclusions:

  • 'how_are_we_stranded_here' is a fast and user-friendly tool for RNA-Seq quality control.
  • It can be easily integrated into existing bioinformatics pipelines.
  • The library is freely available for public use.