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

      , 1 , 2 , 3 , 4 , 5 , 1 , 6 , 7 , 8 , 9 , 10 , 11

      BMC Bioinformatics

      BioMed Central

      Integrated Bio-Search: 12th International Workshop on Network Tools and Applications in Biology (NETTAB 2012)

      14-16 November 2012

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          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 analysis promises to have on better diagnosis, prognosis, drug discovery and treatment, towards personalized medicine. An open business model for bioinformatics, which appears to be able to reduce undue duplication of efforts and support the increased reuse of valuable data sets, tools and platforms, is finally discussed.

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          Most cited references 25

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          The meaning and use of the area under a receiver operating characteristic (ROC) curve.

          A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.
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            The Proteomics Identifications (PRIDE) database and associated tools: status in 2013

            The PRoteomics IDEntifications (PRIDE, http://www.ebi.ac.uk/pride) database at the European Bioinformatics Institute is one of the most prominent data repositories of mass spectrometry (MS)-based proteomics data. Here, we summarize recent developments in the PRIDE database and related tools. First, we provide up-to-date statistics in data content, splitting the figures by groups of organisms and species, including peptide and protein identifications, and post-translational modifications. We then describe the tools that are part of the PRIDE submission pipeline, especially the recently developed PRIDE Converter 2 (new submission tool) and PRIDE Inspector (visualization and analysis tool). We also give an update about the integration of PRIDE with other MS proteomics resources in the context of the ProteomeXchange consortium. Finally, we briefly review the quality control efforts that are ongoing at present and outline our future plans.
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              The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration.

              The value of any kind of data is greatly enhanced when it exists in a form that allows it to be integrated with other data. One approach to integration is through the annotation of multiple bodies of data using common controlled vocabularies or 'ontologies'. Unfortunately, the very success of this approach has led to a proliferation of ontologies, which itself creates obstacles to integration. The Open Biomedical Ontologies (OBO) consortium is pursuing a strategy to overcome this problem. Existing OBO ontologies, including the Gene Ontology, are undergoing coordinated reform, and new ontologies are being created on the basis of an evolving set of shared principles governing ontology development. The result is an expanding family of ontologies designed to be interoperable and logically well formed and to incorporate accurate representations of biological reality. We describe this OBO Foundry initiative and provide guidelines for those who might wish to become involved.
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                Author and article information

                Affiliations
                [1 ]Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, 20133, Italy
                [2 ]Leiden University Medical Center, Leiden, 2333 ZA, The Netherlands
                [3 ]Netherlands Bioinformatics Center, Nijmegen, 6500 HB, The Netherlands
                [4 ]Department of Animal Breeding and Genetics, SLU-Global Bioinformatics Centre, Swedish University of Agricultural Sciences, Uppsala, 75124, Sweden
                [5 ]Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, 75108, Sweden
                [6 ]GeneXplain GmbH, Wolfenbüttel, 38302, Germany
                [7 ]Institute of Chemical Biology and Fundamental Medicine SBRAS, Novosibirsk, 630090, Russia
                [8 ]Inria Grenoble Rhône-Alpes, Saint-Ismier cedex, 38334 France
                [9 ]Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, 1211 Geneva 4, Switzerland
                [10 ]Section of Biology, University of Geneva, 1211 Geneva 4, Switzerland
                [11 ]Biopolymers and Proteomics, IRCCS AOU San Martino IST, Genoa, 16132, Italy
                Contributors
                Conference
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central
                1471-2105
                2014
                10 January 2014
                : 15
                : Suppl 1
                : S2
                1471-2105-15-S1-S2
                10.1186/1471-2105-15-S1-S2
                4015876
                24564249
                Copyright © 2013 Masseroli 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.

                Integrated Bio-Search: 12th International Workshop on Network Tools and Applications in Biology (NETTAB 2012)
                Como, Italy
                14-16 November 2012
                Categories
                Review

                Bioinformatics & Computational biology

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