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MOLGENIS research: advanced bioinformatics data software for non-bioinformaticians.

K Joeri van der Velde1,2, Floris Imhann2,3, Bart Charbon1

  • 1Genomics Coordination Center, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands.

Bioinformatics (Oxford, England)
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Summary

MOLGENIS Research is an open-source web application designed to help researchers manage and share large biomedical datasets. This tool empowers users without extensive bioinformatics expertise to handle complex biological data effectively.

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Area of Science:

  • Bioinformatics
  • Biomedical Research
  • Data Science

Background:

  • The rapid increase in biological data volume and complexity presents challenges for researchers.
  • Many clinical professionals and biomedical researchers lack the necessary bioinformatics tools to manage, process, and share big '-omics' data.

Purpose of the Study:

  • To introduce MOLGENIS Research, an open-source web application.
  • To provide a user-friendly platform for managing and analyzing large biomedical datasets.
  • To enable researchers without advanced bioinformatics skills to handle complex biological data.

Main Methods:

  • Development of an open-source web application.
  • Implementation of features for data collection, management, analysis, visualization, and sharing.
  • Ensuring accessibility through various installation options (source code, WAR file, Docker, SaaS).

Main Results:

  • MOLGENIS Research offers a comprehensive solution for handling large and complex biomedical datasets.
  • The application is designed for users with limited bioinformatics background.
  • It facilitates data processing, analysis, visualization, and public sharing.

Conclusions:

  • MOLGENIS Research addresses the growing need for accessible tools in biomedical data management.
  • The open-source nature and flexible deployment options enhance its utility for the research community.
  • It empowers a broader range of researchers to effectively utilize and share their big '-omics' data.