Mining, visualizing and comparing multidimensional biomolecular data using the Genomics Data Miner (GMine) Web-Server

Genomics Data Miner (GMine) is a user-friendly online software that allows non-experts to mine, cluster and compare multidimensional biomolecular datasets. Various powerful visualization techniques are provided, generating high quality figures that can be directly incorporated into scientific public...

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Bibliographic Details
Published in:Scientific reports 2016-12, Vol.6 (1), p.38178-38178, Article 38178
Main Authors: Proietti, Carla, Zakrzewski, Martha, Watkins, Thomas S, Berger, Bernard, Hasan, Shihab, Ratnatunga, Champa N, Brion, Marie-Jo, Crompton, Peter D, Miles, John J, Doolan, Denise L, Krause, Lutz
Format: Article
Language:English
Subjects:
Antibody response
Cell activation
Computer programs
Data processing
DNA microarrays
Gene expression
Genomics
Lymphocytes T
Malaria
Plasmodium falciparum
Proteomics
Software
Statistical analysis
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Description
Summary:Genomics Data Miner (GMine) is a user-friendly online software that allows non-experts to mine, cluster and compare multidimensional biomolecular datasets. Various powerful visualization techniques are provided, generating high quality figures that can be directly incorporated into scientific publications. Robust and comprehensive analyses are provided via a broad range of data-mining techniques, including univariate and multivariate statistical analysis, supervised learning, correlation networks, clustering and multivariable regression. The software has a focus on multivariate techniques, which can attribute variance in the measurements to multiple explanatory variables and confounders. Various normalization methods are provided. Extensive help pages and a tutorial are available via a wiki server. Using GMine we reanalyzed proteome microarray data of host antibody response against Plasmodium falciparum. Our results support the hypothesis that immunity to malaria is a higher-order phenomenon related to a pattern of responses and not attributable to any single antigen. We also analyzed gene expression across resting and activated T cells, identifying many immune-related genes with differential expression. This highlights both the plasticity of T cells and the operation of a hardwired activation program. These application examples demonstrate that GMine facilitates an accurate and in-depth analysis of complex molecular datasets, including genomics, transcriptomics and proteomics data.
ISSN:2045-2322
2045-2322
DOI:10.1038/srep38178