Abstract
Interactions between various types of molecules that regulate crucial cellular processes are extensively investigated by high-throughput experiments and require dedicated computational methods for the analysis of the resulting data. In many cases, these data can be represented as a bipartite graph because it describes interactions between elements of two different types such as the influence of different experimental conditions on cellular variables or the direct interaction between receptors and their activators/inhibitors. One of the major challenges in the analysis of such noisy datasets is the statistical evaluation of the relationship between any two elements of the same type. Here, we present SICOP (significant co-interaction patterns), an implementation of a method that provides such an evaluation based on the number of their common interaction partners, their so-called co-interaction. This general network analytic method, proved successful in diverse fields, provides a framework for assessing the significance of this relationship by comparison with the expected co-interaction in a suitable null model of the same bipartite graph. SICOP takes into consideration up to two distinct types of interactions such as up- or downregulation. The tool is written in Java and accepts several common input formats and supports different output formats, facilitating further analysis and visualization. Its key features include a user-friendly interface, easy installation and platform independence. The software is open source and available at cna.cs.uni-kl.de/SICOP under the terms of the GNU General Public Licence (version 3 or later).
Highlights
High-throughput experiments resulting in interaction data require computational methods that enable the identification of their key interaction patterns
If the interaction is observed between elements of two different types, the data can be modelled as a bipartite graph in which the elements are represented by nodes and their interaction by edges
The extensive null model approach behind SICOP enabled the identification of key microRNAs that were shown in subsequent experiments to inhibit cell-cycle progression and proliferation
Summary
High-throughput experiments resulting in interaction data require computational methods that enable the identification of their key interaction patterns. If the interaction is observed between elements of two different types, the data can be modelled as a bipartite graph in which the elements are represented by nodes and their interaction by edges. A recently suggested method deals with these problems by using a network analytic approach (Uhlmann et al, 2012; Malumbres, 2012). In this procedure, the number of common interaction partners of two elements is compared with its expected value in a randomized null model that maintains the number of interaction partners of all elements. The underlying assumption is that two elements with a statistically significant number of common interaction partners share the same functional role according to the so-called guilt-byassociation principle (Quackenbush, 2003). It is able to detect significant mild co-interaction effects by taking into account up to two different types of interactions
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