Abstract
BackgroundHeterogeneous biological data such as sequence matches, gene expression correlations, protein-protein interactions, and biochemical pathways can be merged and analyzed via graphs, or networks. Existing software for network analysis has limited scalability to large data sets or is only accessible to software developers as libraries. In addition, the polymorphic nature of the data sets requires a more standardized method for integration and exploration.ResultsMango facilitates large network analyses with its Graph Exploration Language, automatic graph attribute handling, and real-time 3-dimensional visualization. On a personal computer Mango can load, merge, and analyze networks with millions of links and can connect to online databases to fetch and merge biological pathways.ConclusionsMango is written in C++ and runs on Mac OS, Windows, and Linux. The stand-alone distributions, including the Graph Exploration Language integrated development environment, are freely available for download from http://www.complex.iastate.edu/download/Mango. The Mango User Guide listing all features can be found at http://www.gitbook.com/book/j23414/mango-user-guide.
Highlights
Heterogeneous biological data such as sequence matches, gene expression correlations, protein-protein interactions, and biochemical pathways can be merged and analyzed via graphs, or networks
We present a few network analysis examples to illustrate the use of Mango
The path biological pathways of E. coli were downloaded from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and combined into a single pathway network
Summary
Heterogeneous biological data such as sequence matches, gene expression correlations, protein-protein interactions, and biochemical pathways can be merged and analyzed via graphs, or networks. On a personal computer Mango can load, merge, and analyze networks with millions of links and can connect to online databases to fetch and merge biological pathways. The stand-alone distributions, including the Graph Exploration Language integrated development environment, are freely available for download from http://www. Modern biological research produces large and heterogeneous data sets, and there are many ways to categorize or display each type of data. The 2014 Nucleic Acids Research Database Special Issue counted 1552 online biological databases [1]. It is often illuminating, even essential, to examine important biological problems using different types of data. A common method to analyze related data relies on graphs, or networks, where data of various types are linked and key network features or subsets are identified [3,4,5]
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