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mQC: A post-mapping data exploration tool for ribosome profiling.

Steven Verbruggen1, Gerben Menschaert1

  • 1BioBix, Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modelling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, Ghent 9000, Belgium.

Computer Methods and Programs in Biomedicine
|November 8, 2018
PubMed
Summary
This summary is machine-generated.

Researchers can now easily visualize and assess the quality of ribosome profiling data using mQC. This essential tool ensures accurate analysis of translation-level gene expression, preventing errors in biomedical research.

Keywords:
NGSQuality visualizationRibosome profilingTriplet periodicity

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Ribosome profiling (Ribo-Seq) is a powerful next-generation sequencing technique for studying gene expression at the translational level.
  • Current analysis workflows often lack user-friendly tools for quality control of mapped Ribo-Seq data.
  • Inadequate data quality checks can lead to erroneous conclusions in biomedical research.

Purpose of the Study:

  • To introduce mQC, a novel modular tool for visualizing the quality and features of mapped ribosome profiling data.
  • To address the unmet need for accessible quality assessment tools in Ribo-Seq data analysis.
  • To facilitate robust and reliable translational genomics research.

Main Methods:

  • mQC is implemented as a Bioconda package and available in the Galaxy tool shed.
  • The tool provides visualization of quality and general features of P-site corrected ribosome profiling reads.
  • It is designed for both bioinformaticians and non-expert users.

Main Results:

  • mQC was evaluated on multiple datasets, demonstrating its broad applicability.
  • The tool's performance was compared against existing tools with similar functionalities.
  • Results confirm mQC's effectiveness in exploring aligned and P-site corrected Ribo-Seq data.

Conclusions:

  • mQC fills a critical gap in the ribosome profiling data analysis pipeline.
  • The tool enables essential, Ribo-Seq-specific data exploration and quality control.
  • mQC promotes more accurate and reliable insights into translational gene expression.