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Related Experiment Videos

TRAPR: R Package for Statistical Analysis and Visualization of RNA-Seq Data.

Jae Hyun Lim1, Soo Youn Lee1, Ju Han Kim1

  • 1Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics and Systems Biomedical Informatics Research Center, Seoul National University College of Medicine, Seoul 110799, Korea.

Genomics & Informatics
|April 19, 2017
PubMed
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TRAPR is a new R-based pipeline for RNA sequencing (RNA-Seq) data analysis. It integrates statistical analysis and visualization, simplifying gene expression studies.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • RNA sequencing (RNA-Seq) is crucial for gene expression measurement.
  • Existing Bioconductor tools for RNA-Seq analysis are fragmented and lack integration.
  • Current tools lack comprehensive visualization for data integrity checks.

Purpose of the Study:

  • To introduce TRAPR, an integrated R-based pipeline for RNA-Seq data analysis.
  • To provide a unified framework for statistical analysis and visualization of RNA-Seq data.
  • To enable researchers to build customized RNA-Seq analysis workflows.

Main Methods:

  • Development of an R-based software package named TRAPR.
  • Implementation of functions for data management, quality filtering, normalization, and transformation.
Keywords:
base sequencegene expression profilingmolecular sequence dataprogramming languagessequence analysis/RNAsoftware

Related Experiment Videos

  • Integration of statistical analysis and data/result visualization modules.

    • Main Results:

    • TRAPR offers a cohesive solution for RNA-Seq data analysis.
    • The pipeline facilitates customized workflows through modular functions.
    • Integrated visualization aids in confirming data and process integrity.

    Conclusions:

    • TRAPR enhances the efficiency and accessibility of RNA-Seq data analysis.
    • The integrated approach simplifies complex bioinformatics workflows.
    • TRAPR empowers researchers with robust tools for gene expression studies.