Research Article: BioNetApp: An interactive visual data analysis platform for molecular expressions

Date Published: February 22, 2019

Publisher: Public Library of Science

Author(s): Ali M. Roumani, Amgad Madkour, Mourad Ouzzani, Thomas McGrew, Esraa Omran, Xiang Zhang, Gideon Schreiber.

http://doi.org/10.1371/journal.pone.0211277

Abstract

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.

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.

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.

Partial Text

Experiments conducted in the omics arena generate a substantial amount of data. This information is the key to analyze biological behavior through a systems level understanding in which groups of component biomolecules and pathways are connected and operate interdependently. Representation of relationships among biomolecules is also an intensive field of research in systems biology [1]. Correlation and comparative analyses as well as clustering are key elements in understanding such relationships across experiments. Correlation analysis measures the strength of any relationship between the variables. This analysis is useful in testing hypotheses about cause-effect relationships between molecules. Comparative analysis compares the results between experiments. Finally, clustering helps understand the structure of the data and detect anomalies.

The BioNetApp software package went through two main development stages. At first, a prototype initial system called “SysNet” [17] was developed using C++ language, which demonstrated how the core functionalities could be integrated into one system. It also showed how it could be used in the process of hypothesis generation for ionomics data. The latest development stage produced the current BioNetApp software package, a platform for visualizing and mining various relationships between molecular expression data. BioNetApp includes significant additions and improvements (Demonstrated in the Results Section) over its predecessor, such as:

BioNetApp provides interactive analysis and graphic visualization of molecular expression data. It provides three main functionalities to explore molecular expression data and aid in the hypothesis generation process: molecular correlation analysis, comparative and data distribution analysis, and data clustering, with time-course study based on molecular concentrations.

The objective of our work is to enhance the experience of users when performing data analysis with a large amount of data, including time-points, by providing powerful visualization capabilities that are integrated with data mining and statistical techniques. The BioNetApp software takes data from high volume molecular expression experiments as its input and enables interactive visual data mining of molecular correlations, comparative, and clustering analysis with time-course study. It provides a project management GUI interface that enables users to easily manage different projects, import experiment data, and manipulate the meta-data associated with the project and experiment samples.

 

Source:

http://doi.org/10.1371/journal.pone.0211277

 

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