Development of network analysis tool BioLayout Express3D

点击翻译按钮获取中文摘要

Enormous amounts of data pertaining to the functions of genes and proteins and their interactions in the cell, have now been generated by a range of techniques including but not limited to: expression profiling, mass spectrometry, RNAi and Y2H assays. Such functional genomics and proteomics approaches, when combined with computational biology and the emerging discipline of systems biology, finally allow us to begin comprehensive mapping of cellular and molecular networks and pathways. One of the main difficulties we currently face is how best to integrate these disparate data sources and use them to better understand biological systems. Visualisation and analysis of biological data as networks is becoming an increasingly important approach to explore a variety of biological relationships. Such approaches have already been used successfully in the study of sequence similarity, protein structure, protein interactions and evolution. Shifting biological data into a graph/network paradigm allows one to utilise algorithms, techniques, ideas and statistics previously developed in graph theory, engineering, computer science and computational systems biology. In networks derived from biological data, nodes are usually genes, transcripts or proteins, while edges tend to represent experimentally determined similarities or functional linkages between them. While network analysis of biological data has shown great promise, little attention has been paid to microarray data. These data are now abundant, generally of high quality and consist of the type of high-dimensional data for which such approaches are well suited. We have developed a new program called BioLayout Express3D that constructs networks out of microarray expression data. This is achieved by measuring the similarity between individual gene expression profiles and where similar i.e. above a defined threshold, a line is used to connect them. In circumstances where there are groups of co-expressed genes within a given dataset, these nodes form a clique of interconnected nodes. Given the complexity of the data from modern array platforms tools that provide a means of visualising and analysing large amounts of data are very much needed. The current version of BioLayout Express3D can construct graphs composing of over 10K nodes and 1M edges. Visual representation of the graphs is enhanced by a unique layout algorithm combined with an OpenGL graphics engine that renders the network graphs in 3-D space. The layout data in this manner has a number of distinct advantages. The position of each node (gene) within the network can be determined relative to its immediate neighbours i.e. genes that are closest in expression (share edges) to that selected. This visualisation also allows the user to quickly identify structures and features in the graph by eye that would not have been obvious previously. Definition of these structures has also been enhanced by a graph-based clustering algorithm (MCL). Using this approach, large graphs can be divided in groups of highly connected nodes or expression data clusters of co-expressed genes. Having now looked at numerous 1- and 2-colour microarray expression datasets varying in size from less than 20 chips to over 200, we are very happy with the basic performance of the tool. However, we urgently need to add features that will extend its analytical capabilities. The other area in which this Biolayout Express3D is likely to play an important role is in modelling other types of biological relationships. In particular we have begun to use this tool construct graphs based on relationships in protein similarities and in particular networks based on large-scale interaction and pathway datasets. In this respect the tool is showing great promise over other available software packages, but again the tool is in need of further development to enhance its capabilities in this area.