http://precedings.nature.com/tags/bioinformatics Recently posted documents tagged with 'bioinformatics' Nature Publishing Group en Nature Precedings http://precedings.nature.com/images/header_logo.gif http://precedings.nature.com http://precedings.nature.com/documents/3905/version/1 Context: Notwithstanding the great number of studies on the etiology and pathophysiology of schizophrenia, both issues remain far from being fully understood. Schizophrenia seems to be related to several biochemical abnormalities, which point to a multi-factor etiology and pathophysiology, as well as to the perspective that several etiologically diverse disorders might coexist within this nosographic entity. On the other hand, identical twins reveal a high concordance for schizophrenia. From that standpoint, the perspective that structurally-related proteins may play an important and yet non-deterministic role seems attractive. Among these proteins, it is suggestive that Neuregulin 1 exerts a pivotal role. Objective: This paper aims to uncover the most prominent relations that Neuregulin 1 establishes with schizophrenia. Method: Several bioinformatical methods are used in order to present: 1. A visual representation of Neuregulin 1’s main molecular pathways, associated with a discussion about their importance to schizophrenia research; 2. A new heatmap of Neuregulin 1 and its receptor’s expression in brain tissues most relevant to the understanding of schizophrenia, created after the development of new R programming scripts (described elsewhere), which facilitate the analysis of gene expression profiles in public datasets; 3. A conceptual map of the literature retrieved using the keywords ‘Neuregulin 1 and human’ in PubMed, followed by a discussion of the most relevant sub-topics. Results: Neuregulin 1 polymorphisms affect several brain tissues and contribute to the etiology and pathophysiology of schizophrenia. Suggestively, Neuregulin 1 partially bridges the ‘molecular gap’ that schizophrenia establishes in relation to bipolar disorder and Alzheimer disease, which involves genes that affect several brain networks, at the same time that they are highly dependent on noxious environmental variables to be triggered. http://precedings.nature.com/documents/3905/version/1 Wed, 28 Oct 2009 11:15:17 UTC The Role of Neuregulin 1 in Schizophrenia: A Bioinformatics Approach hdl:10101/npre.2009.3905.1 2009-10-28 Alvaro M. Dias Nature Precedings 2009-10-28T11:15:17Z Manuscript Genetics & Genomics Neuroscience Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/3773/version/1 Pandemic (H1N1) 2009 virus has been causing major concerns around the world because of its epidemic potential, rapid dissemination, rate of mutations, and the number of fatalities. One way to gain an advantage over this virus is to use existing rapid bioinformatics tools to examine easily and inexpensively generated genetic sequencing data. We have used the protein sequences deposited with the National Center for Biotechnology Information (NCBI) for data mining to study the relationship among the Pandemic (H1N1) 2009 proteins. There are 11 proteins in the Pandemic (H1N1) 2009 virus, and analysis of sequences from 65 different locations around the globe has resulted in two major clusters. These clusters illustrate the Pandemic H1N1 2009 virus is already experiencing significant genetic drift and that rapid worldwide travel is affecting the distribution of genetically distinct isolates. http://precedings.nature.com/documents/3773/version/1 Wed, 16 Sep 2009 20:35:05 UTC Pandemic (H1N1) 2009 Cluster Analysis: A Preliminary Assessment hdl:10101/npre.2009.3773.1 2009-09-16 Charles Wick Nature Precedings 2009-09-16T20:35:05Z Manuscript Genetics & Genomics Microbiology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3714.1 We present ChemAxiom as the first ontological framework for chemistry in science. ChemAxiom enables discourse about chemical objects in a computable language and is useful for the management of chemical concepts and data, the retrospective typing of resources, the identification of ambiguity and supports chemical text mining. http://dx.doi.org/10.1038/npre.2009.3714.1 Thu, 03 Sep 2009 13:08:10 UTC ChemAxiom – An Ontological Framework for Chemistry in Science doi:10.1038/npre.2009.3714.1 2009-09-03 Nico Adams Nature Precedings 2009-09-03T13:08:10Z Manuscript Chemistry Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3590.2 Lipids can be systematically classified according to functional properties, structural features, biochemical origin or biological system. However Lipid nomenclature has yet to become a robust research tool since no rigorous definitions exist for membership of specific lipid classes. Lipids need to be defined in a manner that is systematic yet at the same time semantically explicit. We report on the reuse of existing lipid nomenclature, ontology describing chemical structure and the extension of the OWL-DL Lipid Ontology to support the classification of lipid molecules. We applied definitions, DL-axioms, to describe lipids classes and illustrate suitability of the ontology for the classification of Fatty Acyl lipids and Mycolic acids. http://dx.doi.org/10.1038/npre.2009.3590.2 Wed, 12 Aug 2009 21:13:51 UTC An OWL-DL Ontology for Classification of Lipids doi:10.1038/npre.2009.3590.2 2009-08-12 Hong-Sang Low Nature Precedings 2009-08-12T21:13:51Z Presentation Chemistry Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3590.1 Lipids can be systematically classified according to functional properties, structural features, biochemical origin or biological system. However Lipid nomenclature has yet to become a robust research tool since no rigorous definitions exist for membership of specific lipid classes. Lipids need to be defined in a manner that is systematic yet at the same time semantically explicit. We report on the reuse of existing lipid nomenclature, ontology describing chemical structure and the extension of the OWL-DL Lipid Ontology to support the classification of lipid molecules. We applied definitions, DL-axioms, to describe lipids classes and illustrate suitability of the ontology for the classification of Fatty Acyl lipids and Mycolic acids. http://dx.doi.org/10.1038/npre.2009.3590.1 Tue, 11 Aug 2009 16:04:29 UTC An OWL-DL Ontology for Classification of Lipids doi:10.1038/npre.2009.3590.1 2009-08-11 Hong-Sang Low Nature Precedings 2009-08-11T16:04:29Z Presentation Chemistry Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3525.1 BackgroundAppearing in a wide variety of contexts, biochemical ‘small molecules’ are a core element of biomedical data. Chemical ontologies, which provide stable identifiers and a shared vocabulary for use in referring to such biochemical small molecules, are crucial to enable the interoperation of such data. One such chemical ontology is ChEBI (Chemical Entities of Biological Interest), a candidate member ontology of the OBO Foundry. ChEBI is a publicly available, manually annotated database of chemical entities and contains around 18000 annotated entities as of the last release (May 2009). ChEBI provides stable unique identifiers for chemical entities; a controlled vocabulary in the form of recommended names (which are unique and unambiguous), common synonyms, and systematic chemical names; cross-references to other databases; and a structural and role-based classification within the ontology. ChEBI is widely used for annotation of chemicals within biological databases, text-mining, and data integration. ChEBI can be accessed online at http://www.ebi.ac.uk/chebi/ and the full dataset is available for download in various formats including SDF and OBO.Automated ClassificationThe selection of chemical entities for inclusion in the ChEBI database is user-driven. As the use of ChEBI has grown, so too has the backlog of user-requested entries. Inevitably, the annotation backlog creates a bottleneck, and to speed up the annotation process, ChEBI has recently released a submission tool which allows community submissions of chemical entities, groups, and classes. However, classification of chemical entities within the ontology is a difficult and niche activity, and it is unlikely that the community as a whole will be able or willing to correctly and consistently classify each submitted entity, creating required classes where they are missing. As a result, it is likely that while the size of the database grows, the ontological classification will become less sophisticated, unless the classification of new entities is assisted computationally. In addition, the ChEBI database is expecting substantial size growth in the next year, so automatic classification, which has up till now not been possible, is urgently required. Automatic classification would also enable the ChEBI ontology classes to be applied to other compound databases such as PubChem. Description Logic ReasoningDescription logic based reasoning technology is a prime candidate for development of such an automatic classification system as it allows the rules of the classification system to be encoded within the knowledgebase. Already at 18000 entities, ChEBI is a fair size for a real-world application of description logic reasoning technology, and as the ontology is enhanced with a richer density of asserted relationships, the classification will become more complex and challenging. We have successfully tested a description logic-based classification of chemical entities based on specified structural properties using the hypertableaux-based HermiT reasoner, and found it to be sufficiently efficient to be feasible for use in a production environment on a database of the size that ChEBI is now. However, much work still remains to enrich the ChEBI knowledgebase itself with the properties needed to provide the formal class definitions for use in the automated classification, and to assess the efficiency of the available description logic reasoning technology on a database the size of ChEBI’s forecast future growth.AcknowledgementsChEBI is funded by the European Commission under SLING, grant agreement number 226073 (Integrating Activity) within Research Infrastructures of the FP7 Capacities Specific Programme, and by the BBSRC, grant agreement number BB/G022747/1 within the “Bioinformatics and biological resources” fund. http://dx.doi.org/10.1038/npre.2009.3525.1 Fri, 31 Jul 2009 14:24:55 UTC Towards automatic classification within the ChEBI ontology doi:10.1038/npre.2009.3525.1 2009-07-31 Janna Hastings Nature Precedings 2009-07-31T14:24:55Z Poster Chemistry Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3479.1 Over the past few years the number of bio-ontologies has rapidly increased. The evaluation of ontologies has long been a problematic issue. The growing number of ontologies makes the need for a strategy for evaluating quality more urgent. We propose a framework for evaluating the quality of bio-ontologies. This framework is inspired by a well-known software quality standard, which has been adapted to the needs of ontology evaluation. An example of how to use the framework, comparing two versions of the Open Biomedical Ontologies’ Cell Type Ontology, is included as an illustration. http://dx.doi.org/10.1038/npre.2009.3479.1 Tue, 28 Jul 2009 14:26:47 UTC A Quality Evaluation Framework for Bio-Ontologies doi:10.1038/npre.2009.3479.1 2009-07-28 Jesualdo Tomas Fernandez-Breis Nature Precedings 2009-07-28T14:26:47Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3478.1 Over the past few years the number of bio-ontologies has rapidly increased. The evaluation of ontologies has long been a problematic issue. The growing number of ontologies makes the need for a strategy for evaluating quality more urgent. We propose a framework for evaluating the quality of bio-ontologies. This framework is inspired by a well-known software quality standard, which has been adapted to the needs of ontology evaluation. An example of how to use the framework, comparing two versions of the Open Biomedical Ontologies’ Cell Type Ontology, is included as an illustration. http://dx.doi.org/10.1038/npre.2009.3478.1 Mon, 27 Jul 2009 20:25:59 UTC A Quality Evaluation Framework for Bio-Ontologies doi:10.1038/npre.2009.3478.1 2009-07-27 Jesualdo Tomas Fernandez-Breis Nature Precedings 2009-07-27T20:25:59Z Presentation Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3469.1 The Lexical Grid project is an on-going community driven initiative that provides a common terminology model to represent multiple vocabulary and ontology sources as well as a scalable and robust API for accessing such information. In order to add more powerful functionalities to the existing infrastructure and align LexGrid more closely with various Semantic Web technologies, we introduce the LexOWL project for representing the ontologies modeled within the LexGrid environment in OWL (Web Ontology Language). The crux of this effort is to create a “bridge” that functionally connects the LexBIG (a LexGrid API) and the OWL API (an interface that implements OWL) seamlessly. In this paper, we discuss the key aspects of designing and implementing the LexOWL bridge. We compared LexOWL with other OWL converting tools and conclude that LexOWL provides an OWL mapping and converting tool with well-defined interoperability for information in the biomedical domain. http://dx.doi.org/10.1038/npre.2009.3469.1 Mon, 27 Jul 2009 15:31:09 UTC LexOWL: A Bridge from LexGrid to OWL doi:10.1038/npre.2009.3469.1 2009-07-27 Cui Tao Nature Precedings 2009-07-27T15:31:09Z Presentation Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3420.1 The basic information in Reactome is provided by bench biologists who are experts on a particular pathway, the Reactome Team is always working hard to drive engagement. This engagement between experts, curators, editors and reviewers requires maintenance and improvement, and in this sense Reactome is itself a model for large biocuration projects that are driven by community engagement. This tutorial will highlight issues from the perspective of online training participants, the trainer’s and the audience’s. From the audience perspective the tutorial will introduce the concepts that drive the Reactome data model, cover the basic steps that a researcher would have to follow in order to breakdown a biological pathway into its “reaction-based” Reactome representation. Introduce the user to the tools that are used by authors, the “authortool” and the tools used by curators, the “curatortool” to move that data into the Reactome database. From the trainer perspective the tutorial will focus on the essential role that a clear explanation of a resource’s data model plays in priming the audience for the technical aspects of biocuration. Technical challenges and online delivery methods will be discussed and examples of systems used will be presented with discussion of the negative and positive aspects. Pedagogical models for enhancing audience participation will be briefly presented. The Reactome project is a collaboration among Cold Spring Harbor Laboratory, The European Bioinformatics Institute, and The Gene Ontology Consortium to develop a curated resource of core pathways and reactions in human biology. The information in this database is authored by biological researchers with expertise in their fields, maintained by the Reactome editorial staff, and cross referenced with the sequence databases at NCBI, Ensembl and UniProt, the UCSC Genome Browser , KEGG (Gene and Compound ), ChEBI, PubMed and GO. The information is then managed by groups of curators at CSHL and EBI, peer-reviewed by other researchers and published on the web. While Reactome is targeted at human pathways, it also includes many individual biochemical reactions from non-human systems such as rat, mouse, pufferfish and zebrafish. This makes the database relevant to the many researchers who work on model organisms. All the information in Reactome is backed up by its provenance: either a literature citation or an electronic inference based on sequence similarity. Reactome is a free on-line resource, and Reactome software is open-source. http://dx.doi.org/10.1038/npre.2009.3420.1 Mon, 13 Jul 2009 09:06:34 UTC Online Training of New Curators doi:10.1038/npre.2009.3420.1 2009-07-13 Marc E. Gillespie Nature Precedings 2009-07-13T09:06:34Z Presentation Bioinformatics http://creativecommons.org/licenses/by/3.0/