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Venn-diaNet : venn diagram based network propagation analysis framework for comparing multiple biological

Benjamin Hur1, Dongwon Kang2, Sangseon Lee2

  • 1Interdisciplinary Program in Bioinformatics, Seoul National University, 1 Gwanak-ro, Seoul, Korea.

BMC Bioinformatics
|December 29, 2019
PubMed
Summary

This study introduces Venn-diaNet, a novel framework for comparing multiple biological experiments using transcriptome data. It prioritizes differentially expressed genes (DEGs) by integrating Venn diagrams with network propagation for enhanced biological insights.

Keywords:
Differentially expressed genesGene prioritizationNetwork propagationVenn diagram

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Comparing multiple biological experiments using transcriptome data often relies on analyzing differentially expressed genes (DEGs).
  • Traditional Venn diagram approaches for DEG analysis face limitations in prioritizing genes and elucidating complex phenotypic differences, especially with combined treatments.
  • Existing methods struggle to systematically rank genes within Venn diagram segments and interpret results from intricate experimental designs.

Purpose of the Study:

  • To develop an advanced analysis framework for comparing multiple biological experiments based on transcriptome data.
  • To address the limitations of conventional Venn diagram tools in prioritizing genes and interpreting complex experimental outcomes.
  • To provide a systematic method for ranking differentially expressed genes (DEGs) and facilitating the understanding of phenotypic differences.

Main Methods:

  • Introduction of Venn-diaNet, a web-based framework integrating Venn diagrams with network propagation on protein-protein interaction networks.
  • Utilizing network propagation to prioritize genes from multiple lists of differentially expressed genes (DEGs).
  • Enabling users to select seed genes within Venn diagram segments for network analysis and gene influence measurement.

Main Results:

  • Venn-diaNet effectively prioritizes genes from multiple DEG lists by ranking them based on their position within Venn diagram segments.
  • The framework provides a user-friendly interface for comparing DEG lists and performing network propagation analysis.
  • Demonstrated ability to reproduce findings from published studies involving two, three, and eight experiments.

Conclusions:

  • Venn-diaNet offers a logical approach to seed gene selection for network propagation, reducing reliance on prior knowledge.
  • The system enhances the analysis of multiple transcriptome experiments, improving the interpretation of DEG data.
  • Venn-diaNet is freely available as a web tool, facilitating its adoption in biological research.