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Semantic web data warehousing for caGrid

BMC BIOINFORMATICS 2009年第10-Sup期10卷 1-10页

作者： McCusker, James P. Phillips, Joshua A. Beltran, Alejandra Gonzalez Finkelstein, Anthony Krauthammer, Michael Yale Univ Sch Med Dept Pathol New Haven CT 06510 USA Semant Bits LLC Reston VA USA UCL Dept Comp Sci London England

The National Cancer Institute (NCI) is developing caGrid as a means for sharing cancer-related data and services. As more data sets become available on caGrid, we need effective ways of accessing and integrating this information. Although the data models exposed on caGrid are semantically well annotated, it is currently up to the caGrid client to infer relationships between the different models and their classes. In this paper, we present a Semantic Web-based data warehouse (Corvus) for creating relationships among caGrid models. This is accomplished through the transformation of semantically-annotated caBIG(R) Unified modeling language (UML) information models into Web Ontology language (OWL) ontologies that preserve those semantics. We demonstrate the validity of the approach by Semantic Extraction, Transformation and Loading (SETL) of data from two caGrid data sources, caTissue and caArray, as well as alignment and query of those sources in Corvus. We argue that semantic integration is necessary for integration of data from distributed web services and that Corvus is a useful way of accomplishing this. Our approach is generalizable and of broad utility to researchers facing similar integration challenges.

关键词： unify modeling language Resource Description Framework Grid Service unify modeling language Model Service Metadata

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Integrated Bio-Search: challenges and trends for the integration, search and comprehensive processing of biological information

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BMC BIOINFORMATICS 2014年第Sup1期15卷 1-15页

作者： Masseroli, Marco Mons, Barend Bongcam-Rudloff, Erik Ceri, Stefano Kel, Alexander Rechenmann, Francois Lisacek, Frederique Romano, Paolo Politecn Milan Dipartimento Elettron Informaz & Bioingn I-20133 Milan Italy Leiden Univ Med Ctr NL-2333 ZA Leiden Netherlands Netherlands Bioinformat Ctr NL-6500 HB Nijmegen Netherlands Swedish Univ Agr Sci SLU Global Bioinformat Ctr Dept Anim Breeding & Genet S-75124 Uppsala Sweden Uppsala Univ Dept Immunol Genet & Pathol S-75108 Uppsala Sweden GeneXplain GmbH D-38302 Wolfenbuttel Germany Inst Chem Biol & Fundamental Med SBRAS Novosibirsk 630090 Russia Inria Grenoble Rhone Alpes F-38334 Saint Ismier France SIB Swiss Inst Bioinformat Proteome Informat Grp CH-1211 Geneva 4 Switzerland Univ Geneva Biol Sect CH-1211 Geneva 4 Switzerland IRCCS AOU San Martino IST I-16132 Genoa Italy

Many efforts exist to design and implement approaches and tools for data capture, integration and analysis in the life sciences. Challenges are not only the heterogeneity, size and distribution of information sources, but also the danger of producing too many solutions for the same problem. Methodological, technological, infrastructural and social aspects appear to be essential for the development of a new generation of best practices and tools. In this paper, we analyse and discuss these aspects from different perspectives, by extending some of the ideas that arose during the NETTAB 2012 Workshop, making reference especially to the European context. First, relevance of using data and software models for the management and analysis of biological data is stressed. Second, some of the most relevant community achievements of the recent years, which should be taken as a starting point for future efforts in this research domain, are presented. Third, some of the main outstanding issues, challenges and trends are analysed. The challenges related to the tendency to fund and create large scale international research infrastructures and public-private partnerships in order to address the complex challenges of data intensive science are especially discussed. The needs and opportunities of Genomic Computing (the integration, search and display of genomic information at a very specific level, e.g. at the level of a single DNA region) are then considered. In the current data and network-driven era, social aspects can become crucial bottlenecks. How these may best be tackled to unleash the technical abilities for effective data integration and validation efforts is then discussed. Especially the apparent lack of incentives for already overwhelmed researchers appears to be a limitation for sharing information and knowledge with other scientists. We point out as well how the bioinformatics market is growing at an unprecedented speed due to the impact that new powerful in silico ana

关键词： unify modeling language Resource Description Framework Text Mining Research Infrastructure Gene Interaction Network

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Digital imaging of root traits (DIRT): a high-throughput computing and collaboration platform for field-based root phenomics

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PLANT METHODS 2015年第1期11卷 1-12页

作者： Das, Abhiram Schneider, Hannah Burridge, James Ascanio, Ana Karine Martinez Wojciechowski, Tobias Topp, Christopher N. Lynch, Jonathan P. Weitz, Joshua S. Bucksch, Alexander Georgia Inst Technol Sch Biol Atlanta GA 30332 USA Penn State Univ Dept Plant Sci State Coll PA USA Univ Bologna Sch Agr & Vet Med Bologna Italy Forschungszentrum Julich IBG Pflanzenwissensch 2 D-52425 Julich Germany Donald Danforth Plant Sci Ctr St Louis MO USA Georgia Inst Technol Sch Phys Atlanta GA 30332 USA Georgia Inst Technol Sch Interact Comp Atlanta GA 30332 USA

Background: Plant root systems are key drivers of plant function and yield. They are also under-explored targets to meet global food and energy demands. Many new technologies have been developed to characterize crop root system architecture (CRSA). These technologies have the potential to accelerate the progress in understanding the genetic control and environmental response of CRSA. Putting this potential into practice requires new methods and algorithms to analyze CRSA in digital images. Most prior approaches have solely focused on the estimation of root traits from images, yet no integrated platform exists that allows easy and intuitive access to trait extraction and analysis methods from images combined with storage solutions linked to metadata. Automated high-throughput phenotyping methods are increasingly used in laboratory-based efforts to link plant genotype with phenotype, whereas similar field-based studies remain predominantly manual low-throughput. Description: Here, we present an open-source phenomics platform "DIRT", as a means to integrate scalable supercomputing architectures into field experiments and analysis pipelines. DIRT is an online platform that enables researchers to store images of plant roots, measure dicot and monocot root traits under field conditions, and share data and results within collaborative teams and the broader community. The DIRT platform seamlessly connects end-users with large-scale compute "commons" enabling the estimation and analysis of root phenotypes from field experiments of unprecedented size. Conclusion: DIRT is an automated high-throughput computing and collaboration platform for field based crop root phenomics. The platform is accessible at http://***/ and hosted on the iPlant cyber-infrastructure using high-throughput grid computing resources of the Texas Advanced Computing Center (TACC). DIRT is a high volume central depository and high-throughput RSA trait computation platform for pl

关键词： unify modeling language Root Trait Content Type Root System Architecture Image Mask

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Design and implementation of microarray gene expression markup language (MAGE-ML)

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GENOME BIOLOGY 2002年第9期3卷 research0046.1-research0046.9页

作者： Spellman, Paul T. Miller, Michael Stewart, Jason Troup, Charles Sarkans, Ugis Chervitz, Steve Bernhart, Derek Sherlock, Gavin Ball, Catherine Lepage, Marc Swiatek, Marcin Marks, W. L. Goncalves, Jason Markel, Scott Iordan, Daniel Shojatalab, Mohammadreza Pizarro, Angel White, Joe Hubley, Robert Deutsch, Eric Senger, Martin Aronow, Bruce J. Robinson, Alan Bassett, Doug Stoeckert, Christian J., Jr. Brazma, Alvis Univ Calif Berkeley Dept Cell & Mol Biol Berkeley CA 94720 USA Rosetta Biosoftware Kirkland WA 98034 USA Open Informat Albuquerque NM 87108 USA Biosci Res Agilent Technol Palo Alto CA 94304 USA EMBL Hinxton Outstn European Bioinformat Inst Cambridge CB10 1SD England Affymetrix Inc Emeryville CA 94608 USA Stanford Univ Dept Genet Sch Med Stanford CA 94305 USA Mol Min Corp Kingston ON K7K 2Z8 Canada Imaging Res Inc St Catharines ON L2S 3A1 Canada Iob Informat LLC La Jolla CA 92037 USA LION Biosci Inc San Diego CA 92121 USA Univ Penn Ctr Bioinformat Philadelphia PA 19104 USA Inst Genom Res Rockville MD 20850 USA Inst Syst Biol Seattle WA 98103 USA Univ Cincinnati Coll Med CHRF Cincinnati OH 45229 USA

Background: Meaningful exchange of microarray data is currently difficult because it is rare that published data provide sufficient information depth or are even in the same format from one publication to another. Only when data can be easily exchanged will the entire biological community be able to derive the full benefit from such microarray studies. Results: To this end we have developed three key ingredients towards standardizing the storage and exchange of microarray data. First, we have created a minimal information for the annotation of a microarray experiment (MIAME)-compliant conceptualization of microarray experiments modeled using the unified modeling language (UML) named MAGE-OM (microarray gene expression object model). Second, we have translated MAGE-OM into an XML-based data format, MAGE-ML, to facilitate the exchange of data. Third, some of us are now using MAGE (or its progenitors) in data production settings. Finally, we have developed a freely available software tool kit (MAGE-STK) that eases the integration of MAGE-ML into end users' systems. Conclusions: MAGE will help microarray data producers and users to exchange information by providing a common platform for data exchange, and MAGE-STK will make the adoption of MAGE easier.

关键词： unify modeling language Microarray Experiment Application Programmer Interface Microarray Gene Expression Database Schema

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Semantic integration of gene expression analysis tools and data sources using software connectors

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BMC GENOMICS 2013年第6-Sup期14卷 1-23页

作者： Miyazaki, Flavia A. Guardia, Gabriela D. A. Vencio, Ricardo Z. N. de Farias, Clever R. G. Univ Sao Paulo Dept Comp Sci & Math DCM FFCLRP BR-14040901 Ribeirao Preto SP Brazil

Background: The study and analysis of gene expression measurements is the primary focus of functional genomics. Once expression data is available, biologists are faced with the task of extracting (new) knowledge associated to the underlying biological phenomenon. Most often, in order to perform this task, biologists execute a number of analysis activities on the available gene expression dataset rather than a single analysis activity. The integration of heteregeneous tools and data sources to create an integrated analysis environment represents a challenging and error-prone task. Semantic integration enables the assignment of unambiguous meanings to data shared among different applications in an integrated environment, allowing the exchange of data in a semantically consistent and meaningful way. This work aims at developing an ontology-based methodology for the semantic integration of gene expression analysis tools and data sources. The proposed methodology relies on software connectors to support not only the access to heterogeneous data sources but also the definition of transformation rules on exchanged data. Results: We have studied the different challenges involved in the integration of computer systems and the role software connectors play in this task. We have also studied a number of gene expression technologies, analysis tools and related ontologies in order to devise basic integration scenarios and propose a reference ontology for the gene expression domain. Then, we have defined a number of activities and associated guidelines to prescribe how the development of connectors should be carried out. Finally, we have applied the proposed methodology in the construction of three different integration scenarios involving the use of different tools for the analysis of different types of gene expression data. Conclusions: The proposed methodology facilitates the development of connectors capable of semantically integrating different gene expression analysis tools

关键词： unify modeling language Semantic Integration Reference Ontology unify modeling language Class Diagram Sequence Ontology

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Accessing and distributing EMBL data using CORBA (common object request broker architecture)

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GENOME BIOLOGY 2000年第5期1卷 1-10页

作者： Wang, Lichun Rodriguez-Tome, Patricia Redaschi, Nicole McNeil, Phil Robinson, Alan Lijnzaad, Philip European Bioinformat Inst EMBL Outstn Hinxton Cambridge CB10 1SD England

Background: The EMBL Nucleotide Sequence Database is a comprehensive database of DNA and RNA sequences and related information traditionally made available in flat-file format. Queries through tools such as SRS (Sequence Retrieval System) also return data in flat-file format. Flat files have a number of shortcomings, however, and the resources therefore currently lack a flexible environment to meet individual researchers' needs. The Object Management Group's common object request broker architecture (CORBA) is an industry standard that provides platform-independent programming interfaces and models for portable distributed object-oriented computing applications. Its independence from programming languages, computing platforms and network protocols makes it attractive for developing new applications for querying and distributing biological data. Results: A CORBA infrastructure developed by EMBL-EBI provides an efficient means of accessing and distributing EMBL data. The EMBL object model is defined such that it provides a basis for specifying interfaces in interface definition language (IDL) and thus for developing the CORBA servers. The mapping from the object model to the relational schema in the underlying Oracle database uses the facilities provided by Persistence (TM), an object/relational tool. The techniques of developing loaders and 'live object caching' with persistent objects achieve a smart live object cache where objects are created on demand. The objects are managed by an evictor pattern mechanism. Conclusions: The CORBA interfaces to the EMBL database address some of the problems of traditional flat-file formats and provide an efficient means for accessing and distributing EMBL data. CORBA also provides a flexible environment for users to develop their applications by building clients to our CORBA servers, which can be integrated into existing systems.

关键词： unify modeling language Object Management Group EMBL Database Common Object Request Broker Architecture Live Object

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The CAP cancer protocols - A case study of caCORE based data standards implementation to integrate with the Cancer Biomedical Informatics Grid

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BMC Medical Informatics and Decision Making 2006年第1期6卷 1-19页

作者： Tobias, Jonathan Chilukuri, Ram Komatsoulis, George A. Mohanty, Sambit Sioutos, Nicholas Warzel, Denise B. Wright, Lawrence W. Crowley, Rebecca S. Department of Pathology University of California at Los Angeles School of Medicine Los Angeles CA United States SemanticBits LLC Reston VA United States National Cancer Institute Center for Bioinformatics National Institutes of Health United States Department of Health and Human Services Rockville MD United States Department of Pathology University of Pittsburgh School of Medicine Pittsburgh PA United States Lockheed Martin Information Technology Rockville MD United States Office of Communications National Cancer Institute Rockville MD United States University of Pittsburgh Cancer Institute Pittsburgh PA United States Center for Biomedical Informatics University of Pittsburgh School of Medicine Pittsburgh PA United States

Background: The Cancer Biomedical Informatics Grid (caBIG™) is a network of individuals and institutions, creating a world wide web of cancer research. An important aspect of this informatics effort is the development of consistent practices for data standards development, using a multi-tier approach that facilitates semantic interoperability of systems. The semantic tiers include (1) information models, (2) common data elements, and (3) controlled terminologies and ontologies. The College of American Pathologists (CAP) cancer protocols and checklists are an important reporting standard in pathology, for which no complete electronic data standard is currently available. Methods: In this manuscript, we provide a case study of Cancer Common Ontologic Representation Environment (caCORE) data standard implementation of the CAP cancer protocols and checklists model - an existing and complex paper based standard. We illustrate the basic principles, goals and methodology for developing caBIG™ models. Results: Using this example, we describe the process required to develop the model, the technologies and data standards on which the process and models are based, and the results of the modeling effort. We address difficulties we encountered and modifications to caCORE that will address these problems. In addition, we describe four ongoing development projects that will use the emerging CAP data standards to achieve integration of tissue banking and laboratory information systems. Conclusion: The CAP cancer checklists can be used as the basis for an electronic data standard in pathology using the caBIG™ semantic modeling methodology. © 2006 Tobias et al;licensee BioMed Central Ltd.

关键词： unify modeling language Cutaneous Melanoma Semantic Annotation unify modeling language Model Semantic Interoperability

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The caCORE Software Development Kit: Streamlining construction of interoperable biomedical information services

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BMC Medical Informatics and Decision Making 2006年第1期6卷 1-16页

作者： Phillips, Joshua Chilukuri, Ram Fragoso, Gilberto Warzel, Denise Covitz, Peter A. Science Applications International Corporation Annapolis MD United States Oracle Corporation Reston VA United States National Cancer Institute Center for Bioinformatics Rockville MD 20852 6116 Executive Blvd. United States SemanticBits LLC Baltimore MD United States

Background: Robust, programmatically accessible biomedical information services that syntactically and semantically interoperate with other resources are challenging to construct. Such systems require the adoption of common information models, data representations and terminology standards as well as documented application programming interfaces (APIs). The National Cancer Institute (NCI) developed the cancer common ontologic representation environment (caCORE) to provide the infrastructure necessary to achieve interoperability across the systems it develops or sponsors. The caCORE Software Development Kit (SDK) was designed to provide developers both within and outside the NCI with the tools needed to construct such interoperable software systems. Results: The caCORE SDK requires a Unified modeling language (UML) tool to begin the development workflow with the construction of a domain information model in the form of a UML Class Diagram. Models are annotated with concepts and definitions from a description logic terminology source using the Semantic Connector component. The annotated model is registered in the Cancer Data Standards Repository (caDSR) using the UML Loader component. System software is automatically generated using the Codegen component, which produces middleware that runs on an application server. The caCORE SDK was initially tested and validated using a seven-class UML model, and has been used to generate the caCORE production system, which includes models with dozens of classes. The deployed system supports access through object-oriented APIs with consistent syntax for retrieval of any type of data object across all classes in the original UML model. The caCORE SDK is currently being used by several development teams, including by participants in the cancer biomedical informatics grid (caBIG) program, to create compatible data services. caBIG compatibility standards are based upon caCORE resources, and thus the caCORE SDK has emerged as a key enab

关键词： unify modeling language unify modeling language Model unify modeling language Class Diagram unify modeling language Diagram Common Data Element

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An analysis of extensible modelling for functional genomics data

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BMC BIOINFORMATICS 2005年第1期6卷 235-235页

作者： Jones, AR Paton, NW Univ Manchester Sch Comp Sci Manchester Lancs England

Background: Several data formats have been developed for large scale biological experiments, using a variety of methodologies. Most data formats contain a mechanism for allowing extensions to encode unanticipated data types. Extensions to data formats are important because the experimental methodologies tend to be fairly diverse and rapidly evolving, which hinders the creation of formats that will be stable over time. Results: In this paper we review the data formats that exist in functional genomics, some of which have become de facto or de jure standards, with a particular focus on how each domain has been modelled, and how each format allows extensions. We describe the tasks that are frequently performed over data formats and analyse how well each task is supported by a particular modelling structure. Conclusion: From our analysis, we make recommendations as to the types of modelling structure that are most suitable for particular types of experimental annotation. There are several standards currently under development that we believe could benefit from systematically following a set of guidelines.

关键词： unify modeling language Control Vocabulary Core Schema Geography Markup language Proprietary Format

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QPCR: Application for real-time PCR data management and analysis

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BMC BIOINFORMATICS 2009年第1期10卷 268-268页

作者： Pabinger, Stephan Thallinger, Gerhard G. Snajder, Rene Eichhorn, Heiko Rader, Robert Trajanoski, Zlatko Graz Univ Technol Inst Genom & Bioinformat A-8010 Graz Austria Christian Doppler Lab Genom & Bioinformat A-8010 Graz Austria Sandoz GmbH Dev Antiinfect Microbiol A-6250 Kundl Austria

Background: Since its introduction quantitative real-time polymerase chain reaction (qPCR) has become the standard method for quantification of gene expression. Its high sensitivity, large dynamic range, and accuracy led to the development of numerous applications with an increasing number of samples to be analyzed. Data analysis consists of a number of steps, which have to be carried out in several different applications. Currently, no single tool is available which incorporates storage, management, and multiple methods covering the complete analysis pipeline. Results: QPCR is a versatile web-based Java application that allows to store, manage, and analyze data from relative quantification qPCR experiments. It comprises a parser to import generated data from qPCR instruments and includes a variety of analysis methods to calculate cycle-threshold and amplification efficiency values. The analysis pipeline includes technical and biological replicate handling, incorporation of sample or gene specific efficiency, normalization using single or multiple reference genes, inter-run calibration, and fold change calculation. Moreover, the application supports assessment of error propagation throughout all analysis steps and allows conducting statistical tests on biological replicates. Results can be visualized in customizable charts and exported for further investigation. Conclusion: We have developed a web-based system designed to enhance and facilitate the analysis of qPCR experiments. It covers the complete analysis workflow combining parsing, analysis, and generation of charts into one single application. The system is freely available at http://***/QPCR

关键词： unify modeling language Amplification Efficiency unify modeling language Model qPCR Experiment Quality Control Parameter

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