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SPOT: a web-tool enabling swift profiling of transcriptomes.

Elias B Farr1, Julia M Sattler1,2, Friedrich Frischknecht1,2

  • 1Integrative Parasitology, Center for Infectious Diseases, Heidelberg University Medical School, Heidelberg 69120, Germany.

Bioinformatics (Oxford, England)
|July 21, 2021
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Summary
This summary is machine-generated.

Swift Profiling Of Transcriptomes (SPOT) is a web tool for analyzing gene expression datasets. It enables rapid comparative analysis and ranking of genes based on user-defined profiles, aiding non-bioinformaticians.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • The proliferation of single-cell and bulk RNA sequencing datasets necessitates efficient tools for comparative gene expression analysis across diverse biological contexts.
  • Analyzing complex gene expression patterns in different organisms, organs, and cell types presents a significant challenge for researchers.

Purpose of the Study:

  • To introduce Swift Profiling Of Transcriptomes (SPOT), an intuitive web tool designed for rapid comparative analysis of RNA sequencing data.
  • To provide a user-friendly platform for both differential gene expression analysis and the identification of genes matching specific transcriptional profiles.

Main Methods:

  • SPOT employs a heuristic algorithm to rank genes based on their proximity to user-defined expression profiles of interest.
  • The tool facilitates the visualization of top-ranking genes through tables, bar charts, and dot plots, with exportable results in Excel format.
  • The methodology was validated using RNA sequencing data from malaria parasites, human organ development, and SARS-CoV-2 infected cell lines.

Main Results:

  • SPOT successfully ranks genes according to user-specified expression profiles, enabling rapid identification of relevant genes.
  • The tool demonstrated applicability across various complex biological datasets, including parasite life cycles and human development.
  • Visualizations and export options enhance the interpretability and utility of the analysis results.

Conclusions:

  • SPOT offers a valuable and accessible solution for comparative transcriptomic analysis, particularly for researchers without extensive bioinformatics expertise.
  • The web tool empowers non-bioinformaticians to analyze their own and publicly available RNA sequencing datasets effectively.
  • SPOT facilitates deeper insights into gene expression patterns across diverse biological systems and disease models.