Databases and tools for constructing signal transduction networks in cancer

Abstract

Traditionally, biologists have devoted their careers to studying individual biological entities of their own interest, partly due to lack of available data regarding that entity. Large, highthroughput data, too complex for conventional processing methods (i.e., "big data"), has accumulated in cancer biology, which is freely available in public data repositories. Such challenges urge biologists to inspect their biological entities of interest using novel approaches, firstly including repository data retrieval. Essentially, these revolutionary changes demand new interpretations of huge datasets at a systems-level, by so called "systems biology". One of the representative applications of systems biology is to generate a biological network from high-throughput big data, providing a global map of molecular events associated with specific phenotype changes. In this review, we introduce the repositories of cancer big data and cutting-edge systems biology tools for network generation, and improved identification of therapeutic targets. [BMB Reports 2017; 50(1): 12-19].

Fig. 1

Systems biology, databases, and network generation. (A) The diversity of types of high-throughput data (genomics, epigenomics, transcriptomics, proteomics, metabolomics) available. The relationships among the data types are connected by edges. (B) The flow (represented by “edges”) of genetic information from DNA to protein is aligned with the diverse data types. Public repositories corresponding to each data type are listed (further description in Table 1). (C) Network differences between correlation-based approaches and Bayesian networks approaches. The correlation (or mutual information) oriented tools, ARACNE (39) and WGCNA (36), do not report directions of edges in networks. Bayesian-driven networks naturally reveal directed edges among the network entries. In other words, the undirected network (in left of the grey-shaded triangular) having G1, G2, and G3 entries by ARACNE and WGCNA can be differentiated into directed networks (in the right of the grey-shaded triangular), using Bayesian networks tools (–51).