• DocumentCode
    2191059
  • Title

    Combined covariance model for non-coding RNA gene finding

  • Author

    Jiang, Wenbo ; Wiese, Kay C.

  • Author_Institution
    Sch. of Comput. Sci., Simon Fraser Univ., Surrey, BC, Canada
  • fYear
    2011
  • fDate
    11-15 April 2011
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    The use of covariance models in finding non-coding RNA gene members in genome sequence databases has been shown quite effective in many studies. However, it has a significant drawback, which is the very large computational burden. A combined covariance model is proposed to reduce the search complexity when a genome sequence is searched for more than one ncRNA gene family. The covariance models that are combined are selected using a hierarchical clustering algorithm. This study shows that when a small number of original covariance models are combined, the combined covariance model can find members from all original ncRNA families thus successfully reducing the search time.
  • Keywords
    biology computing; genomics; macromolecules; pattern clustering; covariance models; genome sequence databases; hierarchical clustering algorithm; ncRNA families; noncoding RNA gene finding; noncoding RNA gene members; search complexity reduction; Bioinformatics; Computational modeling; Databases; Genomics; Hidden Markov models; Periodic structures; RNA;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2011 IEEE Symposium on
  • Conference_Location
    Paris
  • Print_ISBN
    978-1-4244-9896-3
  • Type

    conf

  • DOI
    10.1109/CIBCB.2011.5948474
  • Filename
    5948474