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MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets.

Urminder Singh1,2,3, Manhoi Hur2, Karin Dorman1,3,4

  • 1Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50011, USA.

Nucleic Acids Research
|January 21, 2020
PubMed
Summary
This summary is machine-generated.

MetaOmGraph (MOG) is a free software enabling researchers to explore large omics datasets without coding. It facilitates interactive visualization and statistical analysis, uncovering hidden knowledge in biological data.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Vast amounts of omics data are publicly available but largely underutilized.
  • Extracting knowledge from these large datasets presents significant challenges for researchers.
  • Existing tools may require coding expertise or lack comprehensive exploratory features.

Purpose of the Study:

  • To introduce MetaOmGraph (MOG), a free, open-source software for the exploratory analysis of massive omics datasets.
  • To enable researchers, regardless of coding ability, to interactively visualize and analyze complex biological data.
  • To facilitate the discovery of novel insights and potential biomarkers from diverse omics data.

Main Methods:

  • Development of a standalone software application (MetaOmGraph) for big data analysis.
  • Implementation of interactive visualizations (line charts, box plots, scatter plots, histograms, volcano plots).
  • Integration of statistical analyses including co-expression, differential expression, and differential correlation analyses.
  • Support for metadata exploration, ontology annotations, and data subset export to R.
  • Utilization of multithreading and indexing for efficient processing of large datasets.

Main Results:

  • Demonstrated MOG's capability in analyzing large curated datasets, including human cancer RNA-Seq and Arabidopsis thaliana omics data.
  • Successfully identified novel putative biomarker genes in human tumors.
  • Showcased the software's utility for exploring microarray and metabolomics data.
  • Validated MOG's efficiency in handling big data through multithreading and indexing.

Conclusions:

  • MetaOmGraph (MOG) provides a powerful, user-friendly platform for exploring large-scale omics data.
  • The software empowers researchers to uncover hidden biological knowledge and potential biomarkers.
  • MOG supports reproducible research by allowing projects, including exploration history, to be saved and shared.