title: SICOP: identifying significant co-interaction patterns.
abstract: CONCLUSIONS
  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.
  
  
  BACKGROUND
  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).
authors.affiliation: Graph Theory and Network Analysis Group, Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, 69115 Heidelberg, Germany and Department of Computer Science, University of Science and Technology Kaiserslautern, 67663 Kaiserslautern, Germany.
authors.forename: Andreas
authors.initials: A
authors.lastname: Spitz
authors.forename: Katharina A
authors.initials: KA
authors.lastname: Zweig
authors.forename: Emoke-Ágnes
authors.initials: EÁ
authors.lastname: Horvát
mesh_terms: D000465:Algorithms; D004247:DNA / Q000378:metabolism; D015233:Models, Statistical; D012313:RNA / Q000378:metabolism; D011897:Random Allocation; D012985:Software Design
chemical_list: D012313:RNA; D004247:DNA