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      Provenance in bioinformatics workflows

      1 , , 1 , 2 , 2 , 1

      BMC Bioinformatics

      BioMed Central

      The Second Workshop on Data Mining of Next-Generation Sequencing in conjunction with the 2012 IEEE International Conference on Bioinformatics and Biomedicine

      4-7 October 2012

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          Abstract

          In this work, we used the PROV-DM model to manage data provenance in workflows of genome projects. This provenance model allows the storage of details of one workflow execution, e.g., raw and produced data and computational tools, their versions and parameters. Using this model, biologists can access details of one particular execution of a workflow, compare results produced by different executions, and plan new experiments more efficiently. In addition to this, a provenance simulator was created, which facilitates the inclusion of provenance data of one genome project workflow execution. Finally, we discuss one case study, which aims to identify genes involved in specific metabolic pathways of Bacillus cereus, as well as to compare this isolate with other phylogenetic related bacteria from the Bacillus group. B. cereus is an extremophilic bacteria, collected in warm water in the Midwestern Region of Brazil, its DNA samples having been sequenced with an NGS machine.

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          Taverna: a tool for building and running workflows of services

          Taverna is an application that eases the use and integration of the growing number of molecular biology tools and databases available on the web, especially web services. It allows bioinformaticians to construct workflows or pipelines of services to perform a range of different analyses, such as sequence analysis and genome annotation. These high-level workflows can integrate many different resources into a single analysis. Taverna is available freely under the terms of the GNU Lesser General Public License (LGPL) from .
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            A Chado case study: an ontology-based modular schema for representing genome-associated biological information.

            A few years ago, FlyBase undertook to design a new database schema to store Drosophila data. It would fully integrate genomic sequence and annotation data with bibliographic, genetic, phenotypic and molecular data from the literature representing a distillation of the first 100 years of research on this major animal model system. In developing this new integrated schema, FlyBase also made a commitment to ensure that its design was generic, extensible and available as open source, so that it could be employed as the core schema of any model organism data repository, thereby avoiding redundant software development and potentially increasing interoperability. Our question was whether we could create a relational database schema that would be successfully reused. Chado is a relational database schema now being used to manage biological knowledge for a wide variety of organisms, from human to pathogens, especially the classes of information that directly or indirectly can be associated with genome sequences or the primary RNA and protein products encoded by a genome. Biological databases that conform to this schema can interoperate with one another, and with application software from the Generic Model Organism Database (GMOD) toolkit. Chado is distinctive because its design is driven by ontologies. The use of ontologies (or controlled vocabularies) is ubiquitous across the schema, as they are used as a means of typing entities. The Chado schema is partitioned into integrated subschemas (modules), each encapsulating a different biological domain, and each described using representations in appropriate ontologies. To illustrate this methodology, we describe here the Chado modules used for describing genomic sequences. GMOD is a collaboration of several model organism database groups, including FlyBase, to develop a set of open-source software for managing model organism data. The Chado schema is freely distributed under the terms of the Artistic License (http://www.opensource.org/licenses/artistic-license.php) from GMOD (www.gmod.org).
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              The development and impact of 454 sequencing.

              The 454 Sequencer has dramatically increased the volume of sequencing conducted by the scientific community and expanded the range of problems that can be addressed by the direct readouts of DNA sequence. Key breakthroughs in the development of the 454 sequencing platform included higher throughput, simplified all in vitro sample preparation and the miniaturization of sequencing chemistries, enabling massively parallel sequencing reactions to be carried out at a scale and cost not previously possible. Together with other recently released next-generation technologies, the 454 platform has started to democratize sequencing, providing individual laboratories with access to capacities that rival those previously found only at a handful of large sequencing centers. Over the past 18 months, 454 sequencing has led to a better understanding of the structure of the human genome, allowed the first non-Sanger sequence of an individual human and opened up new approaches to identify small RNAs. To make next-generation technologies more widely accessible, they must become easier to use and less costly. In the longer term, the principles established by 454 sequencing might reduce cost further, potentially enabling personalized genomics.
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                Author and article information

                Conference
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central
                1471-2105
                2013
                4 November 2013
                : 14
                : Suppl 11
                : S6
                Affiliations
                [1 ]Department of Computer Science, University of Brasilia - UnB, Brasilia, Brazil
                [2 ]Department of Informatics, Pontificial Catholic University - PUC/RJ, Rio de Janeiro, Brazil
                Article
                1471-2105-14-S11-S6
                10.1186/1471-2105-14-S11-S6
                3816297
                Copyright ©2013 de Paula et al; licensee BioMed Central Ltd.

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                The Second Workshop on Data Mining of Next-Generation Sequencing in conjunction with the 2012 IEEE International Conference on Bioinformatics and Biomedicine
                Philadelphia, PA, USA
                4-7 October 2012
                Categories
                Research Article

                Bioinformatics & Computational biology

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