Gene Ontology Terms Visualization with Dynamic Distance-Graph and Similarity Measures

Abstract

In the biological field, having a visual and interactive representation of data is useful, particularly when there is a need to investigate a large amount of multilevel data. It is advantageous to communicate this knowledge intuitively because it helps the users to perceive the dynamic structure in which the correct connections are present and can be extrapolated. In this work, we propose a human-interaction system to view similarity data based on the functions of the Gene Ontology (Cellular Component, Molecular Function, and Biological Process) of the proteins/genes for Alzheimer disease and Parkinson disease. The similarity data was built with the Lin and Wang measures for all three areas of Gene Ontology. We clustered data with the K-means algorithm in order to demonstrate how information derived from data can only be partial when using traditional display methods. Then, we have suggested a dynamic and interactive view based on SigmaJS with the aim of allowing customization in the interactive mode of the analysis workflow by users. To this aim, we have developed a first prototype to obtained a more immediate visualization to capture the most relevant information within the three vocabularies of Gene Ontology. This facilitates the creation of an omic view and the ability to perform a multilevel analysis with more details which is much more valuable for the understanding of knowledge by the end users.