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Related Experiment Video

Updated: Apr 23, 2026

Identification and Analysis of Mouse Erythroid Progenitors using the CD71/TER119 Flow-cytometric Assay
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RAMONA: a Web application for gene set analysis on multilevel omics data.

Steffen Sass1, Florian Buettner1, Nikola S Mueller1

  • 1Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany and Department of Mathematics, Technische Universität München, Boltzmannstraße 3, 85747 Garching, Germany.

Bioinformatics (Oxford, England)
|September 20, 2014
PubMed
Summary

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This summary is machine-generated.

Modern multi-omics experiments generate complex data. The Remotely Accessible Multilevel Ontology analysis (RAMONA) tool simplifies interpreting this data, offering functional insights into biological processes.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput experiments enable multi-omics data generation.
  • Analyzing multi-omics data is challenging for non-bioinformaticians due to data complexity.

Purpose of the Study:

  • To present RAMONA, a user-friendly web application for analyzing combined omics datasets.
  • To extend the MONA approach for enhanced multilevel ontology analysis.

Main Methods:

  • RAMONA utilizes a Bayesian enrichment method for gene set analysis.
  • It infers overrepresented biological processes and quantifies them using term probabilities.
  • The approach handles data from various molecular levels and addresses redundancy and multiple testing issues.

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Related Experiment Videos

Last Updated: Apr 23, 2026

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15:32

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Published on: August 5, 2011

32.9K
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Main Results:

  • RAMONA provides an easy-to-interpret output for functional insights.
  • It efficiently processes large ontologies (thousands of terms) within seconds.
  • The tool facilitates simultaneous gene set analysis of combined omics data.

Conclusions:

  • RAMONA offers an accessible solution for interpreting complex multi-omics data.
  • It empowers researchers to gain functional insights into biological processes.
  • The tool supports efficient and interpretable analysis of integrated omics datasets.