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Graph-based clustering and characterization of repetitive sequences in next-generation sequencing data

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    0347465 - BC 2011 RIV GB eng J - Journal Article
    Novák, Petr - Neumann, Pavel - Macas, Jiří
    Graph-based clustering and characterization of repetitive sequences in next-generation sequencing data.
    BMC Bioinformatics. Roč. 11, č. 1 (2010), s. 378-389. ISSN 1471-2105. E-ISSN 1471-2105
    R&D Projects: GA MŠMT(CZ) OC10037; GA MŠMT(CZ) LC06004
    Institutional research plan: CEZ:AV0Z50510513
    Keywords : repetitive DNA * plant genome * next generation sequencing
    Subject RIV: EB - Genetics ; Molecular Biology
    Impact factor: 3.028, year: 2010

    We adapted a graph-based approach for similarity-based partitioning of whole genome 454 sequence reads in order to build clusters made of the reads derived from individual repeat families. The information about cluster sizes was utilized for assessing the proportion and composition of repeats in the genomes of two model species,differing in genome size and 454 sequencing coverage. Moreover, statistical analysis and visual inspection of the topology of the cluster graphs using a newly developed program tool, SeqGrapheR, were shown to be helpful in distinguishing basic types of repeats and investigating sequence variability within repeat families. Repetitive regions of plant genomes can be efficiently characterized by the presented graph-based analysis and the graph representation of repeats can be further used to assess the variability and evolutionary divergence of repeat families, discover and characterize novel elements, and aid in subsequent assembly of their consensus sequences
    Permanent Link: http://hdl.handle.net/11104/0188237

     
     
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