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PLoS One. 2019 Feb 22;14(2):e0211277. doi: 10.1371/journal.pone.0211277. eCollection 2019.

BioNetApp: An interactive visual data analysis platform for molecular expressions.

Author information

1
Department of Computer Science, Gulf University for Science and Technology, Mishref, Kuwait.
2
Bindley Bioscience Center, Purdue University, West Lafayette, IN, United States of America.
3
Department of Computer Science, Purdue University, West Lafayette, IN, United States of America.
4
Qatar Computing Research Institute, Qatar Foundation, Doha, Qatar.
5
Department of Chemistry, University of Louisville, Louisville, KY, United States of America.

Abstract

MOTIVATION:

Systems biology faces two key challenges when dealing with large amounts of disparate data produced by different experiments: the integration of results across different experiments, and the extraction of meaningful information from the data produced by these experiments. An ongoing challenge is to provide better tools that can mine data patterns that could not have been discovered through simple visualization. Such mining capabilities also need to be coupled with intuitive visualization to portray those findings. We introduce a software toolbox entitled BioNetApp to mine these patterns and visualize them across all experiments.

RESULTS:

BioNetApp is an interactive visual data mining software for analyzing high-volume molecular expression data obtained from multiple 'omics experiments. By integrating visualization, statistical methods, and data mining techniques, BioNetApp can perform interactive correlative and comparative analysis along time-course studies of molecular expression data. Correlation analysis provides several visualization features such as Kamada-Kawai, Fruchterman-Reingold Spring embedding network layouts, in addition to single circle, multiple circle and heatmap layouts, whereas comparative analysis presents expression-data distributions across samples, groups, and time points with boxplot display, outlier detection, and data curve fitting. BioNetApp also provides data clustering based on molecular concentrations using Self Organizing Maps (SOM), K-Means, K-Medoids, and Farthest First algorithms.

CONCLUSION:

BioNetApp has been utilized in a metabolomics study to investigate the metabolite abundance changes in alcohol induced fatty liver, where pair-wise analyses of metabolome concentration revealed correlation networks and interesting patterns in the metabolomics dataset. This study case demonstrates the effectiveness of the BioNetApp software as an interactive visual analysis tool for molecular expression data in systems biology. The BioNetApp software is freely available under GNU GPL license and can be downloaded (including the case-study data and user-manual) at: https://doi.org/10.5281/zenodo.2563129.

PMID:
30794548
PMCID:
PMC6386483
DOI:
10.1371/journal.pone.0211277
[Indexed for MEDLINE]
Free PMC Article

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