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FlyMine is a data warehouse that addresses one of the important challenges of modern biology: how to integrate and make use of the diversity and volume of current biological data. Its main focus is genomic and proteomics data for Drosophila and other insects. It provides web access to integrated data at a number of different levels, from simple browsing to construction of complex queries, which can be executed on either single items or lists. RationaleWith the completion of increasing numbers of genome sequences has come an explosion in the development of both computational and experimental techniques for deciphering the functions of genes, molecules and their interactions. These include theoretical methods for deducing function, such as analysis of protein homologies, structural domain predictions, phylogenetic profiling and analysis of protein domain fusions, as well as experimental techniques, such as microarray-based gene expression and transcription factor binding studies, two-hybrid protein-protein interaction screens, and large-scale RNA interference (RNAi) screens. The result is a huge amount of information and a current challenge is to extract meaningful knowledge and patterns of biological significance that can lead to new experimentally testable hypotheses. Many of these broad datasets, however, are noisy and the data quality can vary significantly. While in some circumstances the data from each of these techniques are useful in their own right, the ability to combine data from different sources facilitates interpretation and potentially allows stronger inferences to be made. Currently, biological data are stored in a wide variety of formats in numerous different places, making their combined analysis difficult: when information from several different databases is required, the assembly of data into a format suitable for querying is a challenge in itself. Sophisticated analysis of diverse data requires that they are available in a form that allows questions to be asked across them and that tools for constructing the questions are available. The development of systems for the integration and combined analysis of diverse data remains a priority in bioinformatics. Avoiding the need to understand and reformat many different data sources is a major benefit for end users of a centralized data access system.A number of studies have illustrated the power of integrating data for cross-validation, functional annotation and generating testable hypotheses (reviewed in [1,2]). These studies have covered a range of data types; some looking at the overlap between two different data sets, for example, protein interaction and expression data [3][4][5][6] Another key component is the use of ontologies that provide a standardized system for naming biological entities and their relationships and this aspect is based on the approach taken by the Chado schema [28]. For example, a large part of the FlyMine data model is based on the Sequence Ontology (a controlled-vocabulary for describing biological sequences) [29...

Disrupted in Schizophrenia 1 (DISC1) is a schizophrenia risk gene associated with cognitive deficits in both schizophrenics and the normal ageing population. In this study, we have generated a network of protein-protein interactions (PPIs) around DISC1. This has been achieved by utilising iterative yeast-two hybrid (Y2H) screens, combined with detailed pathway and functional analysis. This so-called 'DISC1 interactome' contains many novel PPIs and provides a molecular framework to explore the function of DISC1. The network implicates DISC1 in processes of cytoskeletal stability and organisation, intracellular transport and cellcycle/division. In particular, DISC1 looks to have a PPI profile consistent with that of an essential synaptic protein, which fits well with the underlying molecular pathology observed at the synaptic level and the cognitive deficits seen behaviourally in schizophrenics. Utilising a similar approach with dysbindin (DTNBP1), a second schizophrenia risk gene, we show that dysbindin and DISC1 share common PPIs suggesting they may affect common biological processes and that the function of schizophrenia risk genes may converge.

Abstract:We describe a database of protein structure alignments for homologous families. The database HOMSTRAD presently contains 130 protein families and 590 aligned structures, which have been selected on the basis of quality of the X-ray analysis and accuracy of the structure. For each family, the database provides a structure-based alignment derived using COMPARER and annotated with JOY in a special format that represents the local structural environment of each amino acid residue. HOMSTRAD also provides a set of superposed atomic coordinates obtained using MNYFIT, which can be viewed with a graphical user interface or used for comparative modeling studies. The database is freely available on the World Wide Web at: http://www-cryst.bioc.cam. ac.uk/--homstrad/, with search facilities and links to other databases.