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VolcaNoseR is a web app for creating, exploring, labeling and sharing volcano plots.

Joachim Goedhart1, Martijn S Luijsterburg2

  • 1Swammerdam Institute for Life Sciences, Section of Molecular Cytology, van Leeuwenhoek Centre for Advanced Microscopy, University of Amsterdam, P.O. Box 94215, 1090 GE, Amsterdam, The Netherlands. j.goedhart@uva.nl.

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Summary

Volcano plots visualize large genomic and proteomic datasets, but unannotated data is inaccessible. VolcaNoseR is an open-source web tool that enhances volcano plot accessibility and reusability for scientific research.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Proteomics

Background:

  • Comparative genome- and proteome-wide screens generate vast datasets.
  • Volcano plots are commonly used to visualize these datasets, highlighting significant findings.
  • Current volcano plots limit access to unannotated data, hindering further analysis and reuse.

Purpose of the Study:

  • To develop an open-source, online tool for creating, exploring, and sharing interactive volcano plots.
  • To improve accessibility and reusability of large-scale genomic and proteomic data.
  • To enhance the transparency of comparative biological datasets.

Main Methods:

  • Development of a web application using R/Shiny, named VolcaNoseR.
  • Integration with online data repositories for data retrieval.
  • Implementation of interactive features for plot customization and data sharing.

Main Results:

  • VolcaNoseR enables the creation of customized, interactive volcano plots.
  • Users can explore, label, and share plots along with their underlying data.
  • The tool facilitates access to and re-use of previously inaccessible data points.

Conclusions:

  • VolcaNoseR significantly increases the transparency and reusability of large comparative biological datasets.
  • The web tool simplifies the identification and exploration of significant findings in genomic and proteomic studies.
  • VolcaNoseR promotes collaborative research by enabling easy sharing of interactive data visualizations.