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Updated: May 31, 2025

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Exploring RNA-Seq Data Analysis Through Visualization Techniques and Tools: A Systematic Review of Opportunities and

Farhana Manzoor1, Cyruss A Tsurgeon2, Vibhuti Gupta1

  • 1Department of Computer Science and Data Science, School of Applied Computational Sciences, Meharry Medical College, Nashville, TN 37208, USA.

Bioengineering (Basel, Switzerland)
|January 24, 2025
PubMed
Summary

This review surveys RNA sequencing (RNA-seq) visualization tools for clinical applications. It highlights single-cell RNA-seq as the most visualized data type, aiding disease diagnosis and prognosis.

Keywords:
RNA-seqsequencingvisualization

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Area of Science:

  • Genomics and Bioinformatics
  • Biomedical Data Visualization
  • Clinical Transcriptomics

Background:

  • RNA sequencing (RNA-seq) is crucial for gene expression analysis in clinical studies.
  • Effective visualization tools are needed to interpret complex RNA-seq data for clinical insights.
  • Understanding gene expression patterns aids in diagnosing and predicting disease outcomes.

Purpose of the Study:

  • To review current data visualization techniques and tools for RNA sequencing (RNA-seq) analysis in clinical settings.
  • To assess the benefits, applications, and limitations of existing visualization methods.
  • To provide a resource for researchers and clinicians utilizing RNA-seq data.

Main Methods:

  • Systematic literature review of studies published between 2017 and 2024.
  • Searches conducted in PubMed, Scopus, Web of Science, and IEEE Xplore databases.
  • Inclusion of 33 studies meeting PRISMA guidelines from an initial 126 identified.

Main Results:

  • Single-cell RNA-seq data visualization was most prevalent (56% of studies).
  • Bulk RNA-seq data (23%) and circular RNA-seq data (18%) were also analyzed.
  • Long non-coding RNA-seq data visualization was least common (3% of studies).

Conclusions:

  • A variety of visualization tools are available for RNA-seq data analysis.
  • Single-cell RNA-seq visualization is a key area for clinical applications.
  • This review offers a comprehensive overview to guide the use of RNA-seq visualization in clinical research.