Abstract
BackgroundIn the functional genomics analysis domain, various methodologies are available for interpreting the results produced by high-throughput biological experiments. These methods commonly use a list of genes as an analysis input, and most of them produce a more complicated list of genes or pathways as the results of the analysis. Although there are several network-based methods, which detect key nodes in the network, the results tend to include well-studied, major hub genes.ResultsTo mine the molecules that have biological meaning but to fewer degrees than major hubs, we propose, in this study, a new network-based method for selecting these hidden key molecules based on virtual information flows circulating among the input list of genes. The human biomolecular network was constructed from the Pathway Commons database, and a calculation method based on betweenness centrality was newly developed. We validated the method with the ErbB pathway and applied it to practical cancer research data. We were able to confirm that the output genes, despite having fewer edges than major hubs, have biological meanings that were able to be invoked by the input list of genes.ConclusionsThe developed method, named NetHiKe (Network-based Hidden Key molecule miner), was able to detect potential key molecules by utilizing the human biomolecular network as a knowledge base. Thus, it is hoped that this method will enhance the progress of biological data analysis in the whole-genome research era.
Highlights
In the functional genomics analysis domain, various methodologies are available for interpreting the results produced by high-throughput biological experiments
Over-representation analysis (ORA) is frequently implemented as a web application, such as the NCI-Nature Pathway Interaction Database [1,2] and the DAVID bioinformatics resources [3], that receive an input list of genes and calculate the p-values based on the frequency of the appearance of the input genes in each precompiled gene set
We call this method Network-based Hidden Key Molecule Miner (NetHiKe), and a detailed description is provided in the “Methods” section
Summary
In the functional genomics analysis domain, various methodologies are available for interpreting the results produced by high-throughput biological experiments These methods commonly use a list of genes as an analysis input, and most of them produce a more complicated list of genes or pathways as the results of the analysis. It is absolutely indispensable to use biological knowledge-based analysis methods to translate the results of these experiments into a better understanding of the underlying phenomena and to plan the stages of research. Biological knowledge, such as pathways or gene sets, is compiled in various databases. Researchers can rarely discover something new related to their input
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