• DocumentCode
    2466233
  • Title

    Accelerated Non-coding RNA Searches with Covariance Model Approximations

  • Author

    Smith, Scott F.

  • Author_Institution
    Boise State Univ., Boise
  • fYear
    0
  • fDate
    0-0 0
  • Firstpage
    2728
  • Lastpage
    2733
  • Abstract
    Covariance models (CMs) are a very sensitive tool for finding non-coding RNA (ncRNA) genes in DNA sequence data. However, CMs are extremely slow. One reason why CMs are so slow is that they allow all possible combinations of insertions and deletions relative to the consensus model even though the vast majority of these are never seen in practice. In this paper we examine reduction in the number of states in covariance models. A simplified CM with reduced states which can be scored much faster is introduced. A comparison of the results of a full CM versus a reduced-state model found using a genetic algorithm is given for the let7 ncRNA family.
  • Keywords
    approximation theory; biology computing; covariance analysis; macromolecules; DNA sequence data; covariance model approximation; noncoding RNA gene; Acceleration; Biological system modeling; Collision mitigation; Databases; Frequency estimation; Genetic algorithms; Hidden Markov models; Proteins; RNA; Sequences;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation, 2006. CEC 2006. IEEE Congress on
  • Conference_Location
    Vancouver, BC
  • Print_ISBN
    0-7803-9487-9
  • Type

    conf

  • DOI
    10.1109/CEC.2006.1688650
  • Filename
    1688650