D632–D636 Nucleic Acids Research, 2008, Vol. 36, Database issue doi:10.1093/nar/gkm807 Published online 16 October 2007 Bacteriome.org—an integrated protein interaction database for E. coli Chong Su 1 , Jose M. Peregrin-Alvarez1,2, Gareth Butland3,4, Sadhna Phanse3, Vincent Fong 3 , Andrew Emili3,5 and John Parkinson1,5,6,* 1Molecular Structure and Function, Hospital for Sick Children, 555 University Avenue, Toronto, ON M5G 1X8, Canada, 2Department of Molecular Biology and Biochemistry, University of Malaga, 29071 Malaga, Spain, 3Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada, 4Life Science Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road MS84R0171, Berkeley, CA 94720, USA, 5Department of Molecular and Medical Genetics and 6Department of Biochemistry, University of Toronto, Toronto, ON M5S 1A1, Canada Received August 15, 2007; Revised September 14, 2007; Accepted September 17, 2007 ABSTRACT High throughput methods are increasingly being used to examine the functions and interactions of gene products on a genome-scale. These include systematic large-scale proteomic studies of protein complexes and protein–protein interaction net- works, functional genomic studies examining pat- terns of gene expression and comparative genomics studies examining patterns of conservation. Since these datasets offer different yet highly comple- mentary perspectives on cell behavior it is expected that integration of these datasets will lead to conceptual advances in our understanding of the fundamental design and evolutionary principles that underlie the organization and function of proteins within biochemical pathways. Here we present Bacteriome.org, a resource that combines locally generated interaction and evolutionary datasets with a previously generated knowledgebase, to provide an integrated view of the Escherichia coli interactome. Tools are provided which allow the user to select and visualize functional, evolutionary and structural relationships between groups of interacting proteins and to focus on genes of interest. Currently the database contains three interaction datasets: a functional dataset consisting of 3989 interactions between 1927 proteins; a ‘core’ high quality experimental dataset of 4863 interac- tions between 1100 proteins and an ‘extended’ experimental dataset of 9860 interactions between 2131 proteins. Bacteriome.org is available online at http://www.bacteriome.org. INTRODUCTION From a historic perspective Escherichia coli has played a central role in the elucidation of the mechanisms underlying core cellular processes such as metabolism, signaling, gene expression and genome replication. A key feature of many of these processes is the tendency of their component proteins to physically associate via stable protein–protein interactions (PPI) to form larger macro- molecular assemblies or complexes. These complexes are often linked together by extended networks of more transient PPI such that the cell is increasingly viewed as an assembly of interconnected functional modules—the ‘interactome’—which integrates and coordinates the cell’s biochemical activities, behavior and responses to external and intrinsic signals. Systematic large-scale proteomics studies and sophisticated computational ana- lyses are increasingly being applied to reveal the extent and complexity of these interconnections in E. coli (1–4). In addition to these interaction datasets, a large body of research has resulted in the generation of comprehensive knowledgebases providing functional and structural details of each E. coli gene product (5,6). Together with other high throughput ‘omic’ type studies measuring, for example, global patterns of gene expression (7) or the impact of evolutionary constraints (8), these complementary resources are paving the way for an exciting new era of ‘integrative biology’ where, for the ﬁrst time, entire systems of interacting biomolecular components can be studied at several levels of biological abstraction. Although each dataset may be exploited for its own purposes, it is widely anticipated that close integration of these datasets will reveal a host of hitherto unknown biological relationships. For example, combining comparative genomic, pathway, structural *To whom correspondence should be addressed. Tel: +1 416 813 5746; Fax: +1 416 813 4931; Email: jparkin@sickkids.ca 2007 The Author(s) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.