Finding a Common Motif of RNA Sequences Using Genetic Programming: The GeRNAMo System

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@Article{Shahar:2007:tCBB,
  author =       "Shahar Michal and Tor Ivry and Omer Schalit-Cohen and 
                 Moshe Sipper and Danny Barash",
  title =        "Finding a Common Motif of RNA Sequences Using Genetic
                 Programming: The GeRNAMo System",
  journal =      "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  year =         "2007",
  volume =       "4",
  number =       "4",
  pages =        "596--610",
  month =        oct # "-" # dec,
  keywords =     "genetic algorithms, genetic programming, RNA, common
                 motif, microRNA, STGP",
  DOI =          "doi:10.1109/tcbb.2007.1045",
  size =         "15 pages",
  abstract =     "We focus on finding a consensus motif of a set of
                 homologous or functionally related RNA molecules.
                 Recent approaches to this problem have been limited to
                 simple motifs, require sequence alignment, and make
                 prior assumptions concerning the data set. We use
                 genetic programming to predict RNA consensus motifs
                 based solely on the data set. Our system -- dubbed
                 GeRNAMo (Genetic programming of RNA Motifs) -- predicts
                 the most common motifs without sequence alignment and
                 is capable of dealing with any motif size. Our program
                 only requires the maximum number of stems in the motif,
                 and if prior knowledge is available the user can
                 specify other attributes of the motif (e.g., the range
                 of the motif's minimum and maximum sizes), thereby
                 increasing both sensitivity and speed. We describe
                 several experiments using either ferritin iron response
                 element (IRE); signal recognition particle (SRP); or
                 microRNA sequences, showing that the most common motif
                 is found repeatedly, and that our system offers
                 substantial advantages over previous methods.",
  notes =        "ECJ, STGP, mutation

                 evolved motif uses special functions h5,h3, ss and
                 length(min length, max length). Favourable comparison
                 with GPRM \cite{Yuh-JyhHu:2003:NAR}.",
}

Genetic Programming entries for Shahar Michal Tor Ivry Omer Schalit-Cohen Moshe Sipper Danny Barash

Citations