Structural Genomic Variation as Risk Factor for Idiopathic Recurrent Miscarriage

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MINE: Module Identification in Networks

<p>Abstract</p> <p>Background</p> <p>Graphical models of network associations are useful for both visualizing and integrating multiple types of association data. Identifying modules, or groups of functionally related gene products, is an important challenge in analyzing biological networks. However, existing tools to identify modules are insufficient when applied to dense networks of experimentally derived interaction data. To address this problem, we have developed an agglomerative clustering method that is able to identify highly modular sets of gene products within highly interconnected molecular interaction networks.</p> <p>Results</p> <p>MINE outperforms MCODE, CFinder, NEMO, SPICi, and MCL in identifying non-exclusive, high modularity clusters when applied to the <it>C. elegans </it>protein-protein interaction network. The algorithm generally achieves superior geometric accuracy and modularity for annotated functional categories. In comparison with the most closely related algorithm, MCODE, the top clusters identified by MINE are consistently of higher density and MINE is less likely to designate overlapping modules as a single unit. MINE offers a high level of granularity with a small number of adjustable parameters, enabling users to fine-tune cluster results for input networks with differing topological properties.</p> <p>Conclusions</p> <p>MINE was created in response to the challenge of discovering high quality modules of gene products within highly interconnected biological networks. The algorithm allows a high degree of flexibility and user-customisation of results with few adjustable parameters. MINE outperforms several popular clustering algorithms in identifying modules with high modularity and obtains good overall recall and precision of functional annotations in protein-protein interaction networks from both <it>S. cerevisiae </it>and <it>C. elegans</it>.</p

The new COST Action European Venom Network (EUVEN)—synergy and future perspectives of modern venomics

Venom research is a highly multidisciplinary field that involves multiple subfields of biology, informatics, pharmacology, medicine, and other areas. These different research facets are often technologically challenging and pursued by different teams lacking connection with each other. This lack of coordination hampers the full development of venom investigation and applications. The COST Action CA19144–European Venom Network was recently launched to promote synergistic interactions among different stakeholders and foster venom research at the European level

The Candida genome database incorporates multiple Candida species: multispecies search and analysis tools with curated gene and protein information for Candida albicans and Candida glabrata

The Candida Genome Database (CGD, http://www.candidagenome.org/) is an internet-based resource that provides centralized access to genomic sequence data and manually curated functional information about genes and proteins of the fungal pathogen Candida albicans and other Candida species. As the scope of Candida research, and the number of sequenced strains and related species, has grown in recent years, the need for expanded genomic resources has also grown. To answer this need, CGD has expanded beyond storing data solely for C. albicans, now integrating data from multiple species. Herein we describe the incorporation of this multispecies information, which includes curated gene information and the reference sequence for C. glabrata, as well as orthology relationships that interconnect Locus Summary pages, allowing easy navigation between genes of C. albicans and C. glabrata. These orthology relationships are also used to predict GO annotations of their products. We have also added protein information pages that display domains, structural information and physicochemical properties; bibliographic pages highlighting important topic areas in Candida biology; and a laboratory strain lineage page that describes the lineage of commonly used laboratory strains. All of these data are freely available at http://www.candidagenome.org/. We welcome feedback from the research community at [email protected]

Cohesive versus Flexible Evolution of Functional Modules in Eukaryotes

Although functionally related proteins can be reliably predicted from phylogenetic profiles, many functional modules do not seem to evolve cohesively according to case studies and systematic analyses in prokaryotes. In this study we quantify the extent of evolutionary cohesiveness of functional modules in eukaryotes and probe the biological and methodological factors influencing our estimates. We have collected various datasets of protein complexes and pathways in Saccheromyces cerevisiae. We define orthologous groups on 34 eukaryotic genomes and measure the extent of cohesive evolution of sets of orthologous groups of which members constitute a known complex or pathway. Within this framework it appears that most functional modules evolve flexibly rather than cohesively. Even after correcting for uncertain module definitions and potentially problematic orthologous groups, only 46% of pathways and complexes evolve more cohesively than random modules. This flexibility seems partly coupled to the nature of the functional module because biochemical pathways are generally more cohesively evolving than complexes

Autoantibody Repertoire in APECED Patients Targets Two Distinct Subgroups of Protiens

High titer autoantibodies produced by B lymphocytes are clinically important features of many common autoimmune diseases. APECED patients with deficient autoimmune regulator (AIRE) gene collectively display a broad repertoire of high titer autoantibodies, including some which are pathognomonic for major autoimmune diseases. AIRE deficiency severely reduces thymic expression of gene-products ordinarily restricted to discrete peripheral tissues, and developing T cells reactive to those gene-products are not inactivated during their development. However, the extent of the autoantibody repertoire in APECED and its relation to thymic expression of self-antigens are unclear. We here undertook a broad protein array approach to assess autoantibody repertoire in APECED patients. Our results show that in addition to shared autoantigen reactivities, APECED patients display high inter-individual variation in their autoantigen profiles, which collectively are enriched in evolutionarily conserved, cytosolic and nuclear phosphoproteins. The APECED autoantigens have two major origins; proteins expressed in thymic medullary epithelial cells and proteins expressed in lymphoid cells. These findings support the hypothesis that specific protein properties strongly contribute to the etiology of B cell autoimmunity.Peer reviewe