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      Googling DNA sequences on the World Wide Web

      , 1 , 1

      BMC Bioinformatics

      BioMed Central

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          Abstract

          Background

          New web-based technologies provide an excellent opportunity for sharing and accessing information and using web as a platform for interaction and collaboration. Although several specialized tools are available for analyzing DNA sequence information, conventional web-based tools have not been utilized for bioinformatics applications. We have developed a novel algorithm and implemented it for searching species-specific genomic sequences, DNA barcodes, by using popular web-based methods such as Google.

          Results

          We developed an alignment independent character based algorithm based on dividing a sequence library (DNA barcodes) and query sequence to words. The actual search is conducted by conventional search tools such as freely available Google Desktop Search. We implemented our algorithm in two exemplar packages. We developed pre and post-processing software to provide customized input and output services, respectively. Our analysis of all publicly available DNA barcode sequences shows a high accuracy as well as rapid results.

          Conclusion

          Our method makes use of conventional web-based technologies for specialized genetic data. It provides a robust and efficient solution for sequence search on the web. The integration of our search method for large-scale sequence libraries such as DNA barcodes provides an excellent web-based tool for accessing this information and linking it to other available categories of information on the web.

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          Most cited references 1

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          Alignment-free genome comparison with feature frequency profiles (FFP) and optimal resolutions.

          For comparison of whole-genome (genic + nongenic) sequences, multiple sequence alignment of a few selected genes is not appropriate. One approach is to use an alignment-free method in which feature (or l-mer) frequency profiles (FFP) of whole genomes are used for comparison-a variation of a text or book comparison method, using word frequency profiles. In this approach it is critical to identify the optimal resolution range of l-mers for the given set of genomes compared. The optimum FFP method is applicable for comparing whole genomes or large genomic regions even when there are no common genes with high homology. We outline the method in 3 stages: (i) We first show how the optimal resolution range can be determined with English books which have been transformed into long character strings by removing all punctuation and spaces. (ii) Next, we test the robustness of the optimized FFP method at the nucleotide level, using a mutation model with a wide range of base substitutions and rearrangements. (iii) Finally, to illustrate the utility of the method, phylogenies are reconstructed from concatenated mammalian intronic genomes; the FFP derived intronic genome topologies for each l within the optimal range are all very similar. The topology agrees with the established mammalian phylogeny revealing that intron regions contain a similar level of phylogenic signal as do coding regions.
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            Author and article information

            Journal
            BMC Bioinformatics
            BMC Bioinformatics
            BioMed Central
            1471-2105
            2009
            10 November 2009
            : 10
            : Suppl 14
            : S4
            Affiliations
            [1 ]Biodiversity Institute of Ontario, Department of Integrative Biology, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
            Article
            1471-2105-10-S14-S4
            10.1186/1471-2105-10-S14-S4
            2775150
            19900300
            Copyright © 2009 Hajibabaei and Singer; 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 th original work is properly cited.

            Categories
            Research

            Bioinformatics & Computational biology

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