BMC Genomics 2008, 9:461 http://www.biomedcentral.com/1471-2164/9/461 Background Many of the important properties of biological systems emerge as a result of the interactions among genes and among their protein products. Genes and the proteins they encode participate in gene-gene, gene-protein, and protein-protein interactions to mediate a wide variety of biological processes. An increasing appreciation for the importance of charting these interactions has lead to many large-scale efforts to identify gene and protein inter- actions for a number of systems [1]. As this data continues to accumulate from a variety of sources there is an increas- ing need for comprehensive databases and analysis tools that allow biologists to make use of it. Genes and proteins that function in the same pathway, for example, interact directly or indirectly, and their functions can only be fully understood in the context of the interaction networks to which they belong. Gene and protein interaction data have come from a vari- ety of sources. To detect protein-protein interactions, for example, high throughput yeast two-hybrid [2-5] and co- affinity purification [6,7] screens have been developed and applied to proteins from humans and several model organisms. To generate large networks of gene-protein interactions, high throughput techniques are being devel- oped for detecting transcription factors and other proteins bound to DNA [8-11]. Finally, gene-gene interactions that suggest functional relationships between pairs of genes are being revealed by large-scale assays for genetic interac- tions [12,13]. While each type of interaction data has proven useful for understanding how genes and their products work together in biological systems, the large amount of disparate data can be difficult to access and interpret. Combining data from different sources has become important because no single screen or technique is free from false positives and false negatives. Many stud- ies have shown, for example, that interactions detected in multiple screens or by multiple techniques are less likely to be false positives (e.g., [14]), so that combining data- sets can provide a simple way to gain confidence in any particular set of interactions. Likewise, the inability of any one technique or particular screen to detect all biologi- cally relevant interactions suggests that combining data- sets increases coverage. A number of centralized databases have been imple- mented to store gene and protein interaction data and to make it publicly available [15-21]. While most of the data are from large-scale screens, several of these databases have also begun to include data from small-scale 'low throughput' experiments collected by manual curation of the literature. Despite the ideal of central databases to be comprehensive, a surprising number of interactions can be found in one database but not another [22,23]. Thus, biologists have been well advised to consult multiple databases to get a complete picture of the available data. Most of the large interaction databases include data for many different species. Such multi-species databases, however, are rarely fully comprehensive for any one organism; for example, organism-specific gene informa- tion, such as gene expression and phenotype data is not available for searching and filtering the interaction data. Multi-organism databases also have difficulty represent- ing potentially conserved interactions for any given spe- cies. Finding conserved interactions requires looking up the orthologous proteins and conducting searches for interaction data in each of several different organisms. Recently, a few public databases have addressed these issues in efforts to generate comprehensive resources for a particular species; e.g., HomoMINT [24] and UniHI for humans [25]. DroID is designed to be a comprehensive interactions database dedicated to the important model organism, Drosophila melanogaster. Construction and content Database overview We developed DroID with several guiding principles in mind. First, we set out to combine all available gene and protein interaction data for Drosophila into one place where it could be frequently updated. DroID also contains searchable gene information from Flybase, the central repository for Drosophila gene information [26], enabling users to find or filter interactions based on Drosophila-spe- cific gene attributes. Second, DroID strives to include all original data when available. For example, the database tries to obtain and store even technique-specific or exper- iment-specific details. These details, which are often miss- ing from centralized databases, can facilitate a wider range of downstream analyses. Third, DroID tracks primary sources and secondary sources, providing links to refer- ences where available, so that users can trace the prove- nance of each interaction. Fourth, DroID strives to eliminate redundancy. If an interaction derived from a single primary reference is found in slightly different forms in multiple databases, a single instance with the appropriate reference appears in DroID. Fifth, DroID includes interactions predicted from experimental data for other major model organisms and humans. Because interactions are often conserved, data from other organ- isms can be used to infer likely interactions between orthologous proteins in Drosophila. Such predicted inter- actions, which have been called interologs [27], essen- tially enable researchers to use humans and other organisms as 'model organisms' for Drosophila studies. Sixth, every interaction in DroID is annotated with a con- fidence score providing a measure of the likelihood that it is a biologically relevant interaction, and a separate score indicating the level of co-expression of the two genes involved. Finally, we set out to provide complete access to DroID with three user-friendly interfaces that include Page 2 of 9 (page number not for citation purposes)