Abstract
Systems biology analysis of protein modules is important for understanding the functional relationships between proteins in the interactome. Here, we present a comprehensive database named AIM for Arabidopsis (Arabidopsis thaliana) interactome modules. The database contains almost 250 000 modules that were generated using multiple analysis methods and integration of microarray expression data. All the modules in AIM are well annotated using multiple gene function knowledge databases. AIM provides a user-friendly interface for different types of searches and offers a powerful graphical viewer for displaying module networks linked to the enrichment annotation terms. Both interactive Venn diagram and power graph viewer are integrated into the database for easy comparison of modules. In addition, predicted interologs from other plant species (homologous proteins from different species that share a conserved interaction module) are available for each Arabidopsis module. AIM is a powerful systems biology platform for obtaining valuable insights into the function of proteins in Arabidopsis and other plants using the modules of the Arabidopsis interactome.Database URL:http://probes.pw.usda.gov/AIM
Highlights
The interactome represents the complete set of molecular interactions in a particular cell [1]
Interactome analysis is important for understanding the functions of proteins in the biological processes of living organisms
The example section shown demonstrates that the AIM database provides a comprehensive tool enabling users to gain complex biological information associated with specific protein(s), thereby facilitating the design of research approaches for system biology studies in Arabidopsis
Summary
The interactome represents the complete set of molecular interactions in a particular cell [1]. Because cellular processes are not typically carried out by individual proteins, interactomes play a very important role for understanding the biological regulatory mechanisms. Several powerful bioinformatics methods, such as co-expression analysis, text mining and ortholog interaction prediction, have been developed to assist in the global interactome analysis, making the interactome data more massive and complicated. It is well established that densely interconnected regions of an interactome often correspond to functionally related groups of proteins that can be identified as modules [2]. Despite the increasing number of interactions in the interactome, it is difficult to gain an understanding of a given protein’s function without knowing the module construction and function. To study specific biological processes, the interactome is frequently studied through protein modules [3]
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