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Stroke is a common disorder with significant morbidity and mortality, and complex aetiology involving both environmental and genetic risk factors. Although some of the major risk factors for stoke, such as smoking and hypertension, are well-documented, the underlying genetic and detailed molecular mechanisms remain elusive. Exploring the relevant biochemical pathways may contribute to the clinical diagnosis of stroke and shed light on its aetiology. A comparative proteomic analysis of blood serum of a pair of monozygotic (MZ) twins discordant for ischaemic stroke (IS) was performed using a label-free quantitative proteomics approach. To overcome the limit of reproducibility in the serum preparation, two separate runs were performed, each consisting of three technical replicates per sample. Biological processes associated with proteins differentially expressed between the twins were explored with gene ontology (GO) classification using the functional analysis tool g:Profiler. ANOVA test performed in Progenesis LC-MS identified 179 (run 1) and 209 (run 2) proteins as differentially expressed between the affected and unaffected twin (p

The International Molecular Exchange (IMEx) Consortium provides scientists with a single body of experimentally verified protein interactions curated in rich contextual detail to an internationally agreed standard. In this update to the work of the IMEx Consortium, we discuss how this initiative has been working in practice, how it has ensured database sustainability, and how it is meeting emerging annotation challenges through the introduction of new interactor types and data formats. Additionally, we provide examples of how IMEx data are being used by biomedical researchers and integrated in other bioinformatic tools and resources., The IMEx consortium provides one of the largest resources of curated, experimentally verified molecular interaction data. Here, the authors review how IMEx evolved into a fundamental resource for life scientists and describe how IMEx data can support biomedical research.

The major histocompatibility complex (MHC) is the most important region in the vertebrate genome with respect to infection and autoimmunity, and is crucial in adaptive and innate immunity. Decades of biomedical research have revealed many MHC genes that are duplicated, polymorphic and associated with more diseases than any other region of the human genome. The recent completion of several large-scale studies offers the opportunity to assimilate the latest data into an integrated gene map of the extended human MHC. Here, we present this map and review its content in relation to paralogy, polymorphism, immune function and disease.

BACKGROUND: The Gene Ontology project integrates data about the function of gene products across a diverse range of organisms, allowing the transfer of knowledge from model organisms to humans, and enabling computational analyses for interpretation of high-throughput experimental and clinical data. The core data structure is the annotation, an association between a gene product and a term from one of the three ontologies comprising the GO. Historically, it has not been possible to provide additional information about the context of a GO term, such as the target gene or the location of a molecular function. This has limited the specificity of knowledge that can be expressed by GO annotations. RESULTS: The GO Consortium has introduced annotation extensions that enable manually curated GO annotations to capture additional contextual details. Extensions represent effector-target relationships such as localization dependencies, substrates of protein modifiers and regulation targets of signaling pathways and transcription factors as well as spatial and temporal aspects of processes such as cell or tissue type or developmental stage. We describe the content and structure of annotation extensions, provide examples, and summarize the current usage of annotation extensions. CONCLUSIONS: The additional contextual information captured by annotation extensions improves the utility of functional annotation by representing dependencies between annotations to terms in the different ontologies of GO, external ontologies, or an organism's gene products. These enhanced annotations can also support sophisticated queries and reasoning, and will provide curated, directional links between many gene products to support pathway and network reconstruction.

Gene Ontology (GO) provides a controlled vocabulary, which is used by several groups around the world to provide functional annotation to proteins across a wide range of species ("www.geneontology.org":http://www.geneontology.org). The BHF-UCL team is funded by the British Heath Foundation to supply GO annotation specifically for human proteins involved in cardiovascular (CV) processes. This is the first time that a physiological process-centered approach has been used for human protein GO annotation. Experienced GO curators from the BHF-UCL team work alongside the bench scientists from the CV genetics group at University College London. By working in a CV dedicated environment the GO curators are able to build up their expertise in the CV field. Over 4000 proteins have been identified as being cardiovascular related and over the last 14 months, the BHF-UCL team has added 6840 Gene Ontology annotations to 603 human CV related proteins, including CDKN2A, CDKN2B, EDN2, GH1 and TERF1. In order to provide detailed annotations the BHF-UCL GO curators also initiate the development of new GO terms related to cardiovascular processes. The development of these terms, such as lipoprotein-mediated signalling, regulation of cardiac muscle hypertrophy and cardiac epithelial to mesenchymal transition, are made in collaboration with the GO editorial office and CV scientists. We will present details of the progress made in the first year of the BHF-UCL GO annotation project and the impact on microarray analysis.

The dinucleotide repeat sequences at the DXS454 and DXS458 loci have been mapped genetically to Xq22, to the interval between DXS3 and DXS17. We have now mapped them with respect to XLA and five other loci, to within the DXS3 to XLA interval. The more precise localisation of these polymorphic loci will be useful for the fine-mapping of disease loci on the long arm of the X chromosome and enable these probes to be used for prenatal diagnosis and carrier status determination in families with XLA.