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Bioconductor: open software development for computational biology and bioinformatics

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      A detailed description of the aims and methods of the Bioconductor project, an initiative for the collaborative creation of extensible software for computational biology and bioinformatics.


      The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples.

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      Most cited references 24

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      The Bioperl toolkit: Perl modules for the life sciences.

      The Bioperl project is an international open-source collaboration of biologists, bioinformaticians, and computer scientists that has evolved over the past 7 yr into the most comprehensive library of Perl modules available for managing and manipulating life-science information. Bioperl provides an easy-to-use, stable, and consistent programming interface for bioinformatics application programmers. The Bioperl modules have been successfully and repeatedly used to reduce otherwise complex tasks to only a few lines of code. The Bioperl object model has been proven to be flexible enough to support enterprise-level applications such as EnsEMBL, while maintaining an easy learning curve for novice Perl programmers. Bioperl is capable of executing analyses and processing results from programs such as BLAST, ClustalW, or the EMBOSS suite. Interoperation with modules written in Python and Java is supported through the evolving BioCORBA bridge. Bioperl provides access to data stores such as GenBank and SwissProt via a flexible series of sequence input/output modules, and to the emerging common sequence data storage format of the Open Bioinformatics Database Access project. This study describes the overall architecture of the toolkit, the problem domains that it addresses, and gives specific examples of how the toolkit can be used to solve common life-sciences problems. We conclude with a discussion of how the open-source nature of the project has contributed to the development effort.
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        Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection.

        Recent advances in cDNA and oligonucleotide DNA arrays have made it possible to measure the abundance of mRNA transcripts for many genes simultaneously. The analysis of such experiments is nontrivial because of large data size and many levels of variation introduced at different stages of the experiments. The analysis is further complicated by the large differences that may exist among different probes used to interrogate the same gene. However, an attractive feature of high-density oligonucleotide arrays such as those produced by photolithography and inkjet technology is the standardization of chip manufacturing and hybridization process. As a result, probe-specific biases, although significant, are highly reproducible and predictable, and their adverse effect can be reduced by proper modeling and analysis methods. Here, we propose a statistical model for the probe-level data, and develop model-based estimates for gene expression indexes. We also present model-based methods for identifying and handling cross-hybridizing probes and contaminating array regions. Applications of these results will be presented elsewhere.
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          Creating a bioinformatics nation.

           Lincoln Stein (2002)

            Author and article information

            [1 ]Department of Biostatistical Science, Dana-Farber Cancer Institute, 44 Binney St, Boston, MA 02115, USA
            [2 ]Channing Laboratory, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA
            [3 ]Department of Statistics, University of Wisconsin-Madison, 1210 W Dayton St, Madison, WI 53706, USA
            [4 ]Division of Biostatistics, University of California, Berkeley, 140 Warren Hall, Berkeley, CA 94720-7360, USA
            [5 ]Seminar for Statistics LEO C16, ETH Zentrum, Zürich CH-8092, Switzerl
            [6 ]Department of Statistics, Harvard University, 1 Oxford St, Cambridge, MA 02138, USA
            [7 ]Center for Biological Sequence Analysis, Technical University of Denmark, Building 208, Lyngby 2800, Denmark
            [8 ]Department of Biomathematical Sciences, Mount Sinai School of Medicine, 1 Gustave Levy Place, Box 1023, New York, NY 10029, USA
            [9 ]Institut für Statistik und Wahrscheinlichkeitstheorie, TU Wien, Wiedner Hauptstrasse 8-10/1071, Wien 1040, Austria
            [10 ]Institut für Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universität Erlangen-Nürnberg, Waldstraße6, D-91054 Erlangen, Germany
            [11 ]Division of Molecular Genome Analysis, DKFZ (German Cancer Research Center), 69120 Heidelberg, Germany
            [12 ]Department of Economics, University of Milan, 23 Via Mercalli, I-20123 Milan, Italy
            [13 ]Department of Biostatistics, Johns Hopkins University, 615 N Wolfe St E3035, Baltimore, MD 21205, USA
            [14 ]Department of Medical Education and Biomedical Informatics, University of Washington, Box 357240, 1959 NE Pacific, Seattle, WA 98195, USA
            [15 ]Statistisches Labor, Institut für Angewandte Mathematik, Im Neuenheimer Feld 294, D 69120, Heidelberg, Germany
            [16 ]Department of Molecular Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, TPC-28, La Jolla, CA 92037, USA
            [17 ]Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3050, Australia
            [18 ]Department of Statistics and Actuarial Science, University of Iowa, 241 Schaeffer Hall, Iowa City, IA 52242, USA
            [19 ]Center for Bioinformatics and Molecular Biostatistics, Univerisity of California, San Francisco, 500 Parnassus Ave, San Francisco 94143-0560, USA
            Genome Biol
            Genome Biology
            BioMed Central (London )
            15 September 2004
            : 5
            : 10
            : R80
            Copyright © 2004 Gentleman et al.; licensee BioMed Central Ltd.

            This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.




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