Seahawk: moving beyond HTML in Web-based bioinformatics analysis

<p>Abstract</p> <p>Background</p> <p>Traditional HTML interfaces for input to and output from Bioinformatics analysis on the Web are highly variable in style, content and data formats. Combining multiple analyses can therfore be an onerous task for biologists. Semantic Web Services allow automated discovery of conceptual links between remote data analysis servers. A shared data ontology and service discovery/execution framework is particularly attractive in Bioinformatics, where data and services are often both disparate and distributed. Instead of biologists copying, pasting and reformatting data between various Web sites, Semantic Web Service protocols such as MOBY-S hold out the promise of seamlessly integrating multi-step analysis.</p> <p>Results</p> <p>We have developed a program (Seahawk) that allows biologists to intuitively and seamlessly chain together Web Services using a data-centric, rather than the customary service-centric approach. The approach is illustrated with a ferredoxin mutation analysis. Seahawk concentrates on lowering entry barriers for biologists: no prior knowledge of the data ontology, or relevant services is required. In stark contrast to other MOBY-S clients, in Seahawk users simply load Web pages and text files they already work with. Underlying the familiar Web-browser interaction is an XML data engine based on extensible XSLT style sheets, regular expressions, and XPath statements which import existing user data into the MOBY-S format.</p> <p>Conclusion</p> <p>As an easily accessible applet, Seahawk moves beyond standard Web browser interaction, providing mechanisms for the biologist to concentrate on the analytical task rather than on the technical details of data formats and Web forms. As the MOBY-S protocol nears a 1.0 specification, we expect more biologists to adopt these new semantic-oriented ways of doing Web-based analysis, which empower them to do more complicated, <it>ad hoc </it>analysis workflow creation without the assistance of a programmer.</p

Efficient, Distributed and Interactive Neuroimaging Data Analysis Using the LONI Pipeline

The LONI Pipeline is a graphical environment for construction, validation and execution of advanced neuroimaging data analysis protocols (Rex et al., 2003). It enables automated data format conversion, allows Grid utilization, facilitates data provenance, and provides a significant library of computational tools. There are two main advantages of the LONI Pipeline over other graphical analysis workflow architectures. It is built as a distributed Grid computing environment and permits efficient tool integration, protocol validation and broad resource distribution. To integrate existing data and computational tools within the LONI Pipeline environment, no modification of the resources themselves is required. The LONI Pipeline provides several types of process submissions based on the underlying server hardware infrastructure. Only workflow instructions and references to data, executable scripts and binary instructions are stored within the LONI Pipeline environment. This makes it portable, computationally efficient, distributed and independent of the individual binary processes involved in pipeline data-analysis workflows. We have expanded the LONI Pipeline (V.4.2) to include server-to-server (peer-to-peer) communication and a 3-tier failover infrastructure (Grid hardware, Sun Grid Engine/Distributed Resource Management Application API middleware, and the Pipeline server). Additionally, the LONI Pipeline provides three layers of background-server executions for all users/sites/systems. These new LONI Pipeline features facilitate resource-interoperability, decentralized computing, construction and validation of efficient and robust neuroimaging data-analysis workflows. Using brain imaging data from the Alzheimer's Disease Neuroimaging Initiative (Mueller et al., 2005), we demonstrate integration of disparate resources, graphical construction of complex neuroimaging analysis protocols and distributed parallel computing. The LONI Pipeline, its features, specifications, documentation and usage are available online (http://Pipeline.loni.ucla.edu)

Neuroimaging study designs, computational analyses and data provenance using the LONI pipeline.

Modern computational neuroscience employs diverse software tools and multidisciplinary expertise to analyze heterogeneous brain data. The classical problems of gathering meaningful data, fitting specific models, and discovering appropriate analysis and visualization tools give way to a new class of computational challenges--management of large and incongruous data, integration and interoperability of computational resources, and data provenance. We designed, implemented and validated a new paradigm for addressing these challenges in the neuroimaging field. Our solution is based on the LONI Pipeline environment [3], [4], a graphical workflow environment for constructing and executing complex data processing protocols. We developed study-design, database and visual language programming functionalities within the LONI Pipeline that enable the construction of complete, elaborate and robust graphical workflows for analyzing neuroimaging and other data. These workflows facilitate open sharing and communication of data and metadata, concrete processing protocols, result validation, and study replication among different investigators and research groups. The LONI Pipeline features include distributed grid-enabled infrastructure, virtualized execution environment, efficient integration, data provenance, validation and distribution of new computational tools, automated data format conversion, and an intuitive graphical user interface. We demonstrate the new LONI Pipeline features using large scale neuroimaging studies based on data from the International Consortium for Brain Mapping [5] and the Alzheimer's Disease Neuroimaging Initiative [6]. User guides, forums, instructions and downloads of the LONI Pipeline environment are available at http://pipeline.loni.ucla.edu

CoryneCenter – An online resource for the integrated analysis of corynebacterial genome and transcriptome data

Neuweger H, Baumbach J, Albaum S, et al. CoryneCenter: an online resource for the integrated analysis of corynebacterial genome and transcriptome data. BMC Systems Biology. 2007;1(1): 55.Background: The introduction of high-throughput genome sequencing and post-genome analysis technologies, e.g. DNA microarray approaches, has created the potential to unravel and scrutinize complex gene-regulatory networks on a large scale. The discovery of transcriptional regulatory interactions has become a major topic in modern functional genomics. Results: To facilitate the analysis of gene-regulatory networks, we have developed CoryneCenter, a web-based resource for the systematic integration and analysis of genome, transcriptome, and gene regulatory information for prokaryotes, especially corynebacteria. For this purpose, we extended and combined the following systems into a common platform: (1) GenDB, an open source genome annotation system, (2) EMMA, a MAGE compliant application for high-throughput transcriptome data storage and analysis, and (3) CoryneRegNet, an ontology-based data warehouse designed to facilitate the reconstruction and analysis of gene regulatory interactions. We demonstrate the potential of CoryneCenter by means of an application example. Using microarray hybridization data, we compare the gene expression of Corynebacterium glutamicum under acetate and glucose feeding conditions: Known regulatory networks are confirmed, but moreover CoryneCenter points out additional regulatory interactions. Conclusion: CoryneCenter provides more than the sum of its parts. Its novel analysis and visualization features significantly simplify the process of obtaining new biological insights into complex regulatory systems. Although the platform currently focusses on corynebacteria, the integrated tools are by no means restricted to these species, and the presented approach offers a general strategy for the analysis and verification of gene regulatory networks. CoryneCenter provides freely accessible projects with the underlying genome annotation, gene expression, and gene regulation data. The system is publicly available at http://www.CoryneCenter.d

Biological data integration using Semantic Web technologies

International audienceCurrent research in biology heavily depends on the availability and efficient use of information. In order to build new knowledge, various sources of biological data must often be combined. Semantic Web technologies, which provide a common framework allowing data to be shared and reused between applications, can be applied to the management of disseminated biological data. However, due to some specificities of biological data, the application of these technologies to life science constitutes a real challenge. Through a use case of biological data integration, we show in this paper that current Semantic Web technologies start to become mature and can be applied for the development of large applications. However, in order to get the best from these technologies, improvements are needed both at the level of tool performance and knowledge modeling

FReDoWS: a method to automate molecular docking simulations with explicit receptor flexibility and snapshots selection

<p>Abstract</p> <p>Background</p> <p><it>In silico</it> molecular docking is an essential step in modern drug discovery when driven by a well defined macromolecular target. Hence, the process is called structure-based or rational drug design (RDD). In the docking step of RDD the macromolecule or receptor is usually considered a rigid body. However, we know from biology that macromolecules such as enzymes and membrane receptors are inherently flexible. Accounting for this flexibility in molecular docking experiments is not trivial. One possibility, which we call a fully-flexible receptor model, is to use a molecular dynamics simulation trajectory of the receptor to simulate its explicit flexibility. To benefit from this concept, which has been known since 2000, it is essential to develop and improve new tools that enable molecular docking simulations of fully-flexible receptor models.</p> <p>Results</p> <p>We have developed a Flexible-Receptor Docking Workflow System (FReDoWS) to automate molecular docking simulations using a fully-flexible receptor model. In addition, it includes a snapshot selection feature to facilitate acceleration the virtual screening of ligands for well defined disease targets. FReDoWS usefulness is demonstrated by investigating the docking of four different ligands to flexible models of <it>Mycobacterium tuberculosis’</it> wild type InhA enzyme and mutants I21V and I16T. We find that all four ligands bind effectively to this receptor as expected from the literature on similar, but wet experiments.</p> <p>Conclusions</p> <p>A work that would usually need the manual execution of many computer programs, and the manipulation of thousands of files, was efficiently and automatically performed by FReDoWS. Its friendly interface allows the user to change the docking and execution parameters. Besides, the snapshot selection feature allowed the acceleration of docking simulations. We expect FReDoWS to help us explore more of the role flexibility plays in receptor-ligand interactions. FReDoWS can be made available upon request to the authors.</p

Taverna :un ambiente para el desarrollo de experimentos científicos

Taverna es una potente herramienta para el diseño, desarrollo y ejecución de experimentos biológicos y componente central de la plataforma myGrid. Una de sus cualidades más importantes, es su capacidad de mediación entre usuarios y la computadora, simplificando de forma notable varias tareas computacionales avanzadas. En este reporte, se presenta el resultado de un relevamiento de la literatura, con el propósito de describir las principales características y funcionalidades de Taverna, así como la arquitectura, integración con otros proyectos, presentando también algunas limitaciones y debilidades

Interoperability With Moby 1.0 - It&#x27;s Better Than Sharing Your Toothbrush!

The BioMoby project was initiated in 2001 from within the model organism database community. It aimed to standardize methodologies to facilitate information exchange and access to analytical resources, using a consensus driven approach. Six years later, the BioMoby development community is pleased to announce the release of the 1.0 version of the interoperability framework, registry API, and supporting Perl and Java code-bases. Together, these provide interoperable access to over 1400 bioinformatics resources worldwide through the BioMoby platform, and this number continues to grow. Here we highlight and discuss the features of BioMoby that make it distinct from other Semantic Web Service and interoperability initiatives, and that have been instrumental to its deployment and use by a wide community of bioinformatics service providers. The standard, client software, and supporting code libraries are all freely available at http://www.biomoby.org

BioMoby extensions to the Taverna workflow management and enactment software

Background: As biology becomes an increasingly computational science, it is critical that we develop software tools that support not only bioinformaticians, but also bench biologists in their exploration of the vast and complex data-sets that continue to build from international genomic, proteomic, and systems-biology projects. The BioMoby interoperability system was created with the goal of facilitating the movement of data from one Web-based resource to another to fulfill the requirements of non-expert bioinformaticians. In parallel with the development of BioMoby, the European myGrid project was designing Taverna, a bioinformatics workflow design and enactment tool. Here we describe the marriage of these two projects in the form of a Taverna plug-in that provides access to many of BioMoby's features through the Taverna interface. Results: The exposed BioMoby functionality aids in the design of "sensible" BioMoby workflows, aids in pipelining BioMoby and non-BioMoby-based resources, and ensures that end-users need only a minimal understanding of both BioMoby, and the Taverna interface itself. Users are guided through the construction of syntactically and semantically correct workflows through plug-in calls to the Moby Central registry. Moby Central provides a menu of only those BioMoby services capable of operating on the data-type(s) that exist at any given position in the workflow. Moreover, the plug-in automatically and correctly connects a selected service into the workflow such that users are not required to understand the nature of the inputs or outputs for any service, leaving them to focus on the biological meaning of the workflow they are constructing, rather than the technical details of how the services will interoperate. Conclusion: With the availability of the BioMoby plug-in to Taverna, we believe that BioMoby-based Web Services are now significantly more useful and accessible to bench scientists than are more traditional Web Services.Medical Genetics, Department ofOther UBCNon UBCMedicine, Faculty ofReviewedFacult