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    Using growing self-organising maps to improve the binning process in environmental whole-genome shotgun sequencing

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    Author
    Chan, C-KK; Hsu, AL; Tang, S-L; Halgamuge, SK
    Date
    2008-01-01
    Source Title
    JOURNAL OF BIOMEDICINE AND BIOTECHNOLOGY
    Publisher
    HINDAWI LTD
    University of Melbourne Author/s
    Hsu, Arthur; Halgamuge, Saman; CHAN, CHON KIT KENNETH
    Affiliation
    Mechanical Engineering
    Metadata
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    Document Type
    Journal Article
    Citations
    Chan, C. -K. K., Hsu, A. L., Tang, S. -L. & Halgamuge, S. K. (2008). Using growing self-organising maps to improve the binning process in environmental whole-genome shotgun sequencing. JOURNAL OF BIOMEDICINE AND BIOTECHNOLOGY, 2008 (1), https://doi.org/10.1155/2008/513701.
    Access Status
    Access this item via the Open Access location
    URI
    http://hdl.handle.net/11343/29303
    DOI
    10.1155/2008/513701
    Open Access at PMC
    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2235928
    Abstract
    Metagenomic projects using whole-genome shotgun (WGS) sequencing produces many unassembled DNA sequences and small contigs. The step of clustering these sequences, based on biological and molecular features, is called binning. A reported strategy for binning that combines oligonucleotide frequency and self-organising maps (SOM) shows high potential. We improve this strategy by identifying suitable training features, implementing a better clustering algorithm, and defining quantitative measures for assessing results. We investigated the suitability of each of di-, tri-, tetra-, and pentanucleotide frequencies. The results show that dinucleotide frequency is not a sufficiently strong signature for binning 10 kb long DNA sequences, compared to the other three. Furthermore, we observed that increased order of oligonucleotide frequency may deteriorate the assignment result in some cases, which indicates the possible existence of optimal species-specific oligonucleotide frequency. We replaced SOM with growing self-organising map (GSOM) where comparable results are obtained while gaining 7%-15% speed improvement.
    Keywords
    Artificial Intelligence and Image Processing

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