Information Visualization Techniques in Bioinformatics during the Postgenomic Era

Abstract

Information visualization techniques, which take advantage of the bandwidth of human vision, are powerful tools for organizing and analyzing a large amount of data. In the postgenomic era, information visualization tools are indispensable for biomedical research. This paper aims to present an overview of current applications of information visualization techniques in bioinformatics for visualizing different types of biological data, such as from genomics, proteomics, expression profiling and structural studies. Finally, we discuss the challenges of information visualization in bioinformatics related to dealing with more complex biological information in the emerging fields of systems biology and systems medicine.

Figure 1

Microarray gene expression profiles visualized as colored mosaic and clustered by dendrogram. Similar profiles are arranged close to one another and are linked together in a tree structure. However, the cluster is solely based on mathematical similarity of gene expressions and no biological knowledge is used. Produced using Spotfire software (http://www.spotfire.com/).

Figure 2

Microarray data visualized in Treemap in the context of Gene Ontology. This novel hybrid system is representative of a ‘semantic enhancement’ trend in visualization: it consists of a visualization method exploiting ontology-anchoring (preprocessing and organizing data with GO) followed by a conventional clustering (analogous to the one presented in Figure 1). Larger boxes of molecular functions representing higher level GO terms contain smaller ones representing lower level terms. Microarray expression data are represented as colored boxes with gene identities distributed in this hierarchy with their sizes representing expression levels and their colors representing p-values. Thus the overview of expression pattern is presented in the context of GO within only one screen. Corresponding biological interpretations can be easily perceived by identifying the GO boxes that contain brighter and larger gene boxes. Produced using TreeMap 4.1 [68] (http://www.cs.umd.edu/hcil/treemap/) and data from [69] (http://www.umbi.umd.edu/∼cbr/baehrecke/sdata2.htm).