A detection method for intronic snoRNA genes using extended-weight-updating SOM with appearance probability of bases

  • Authors:

  • Kunihito Yamamori;Takuro Matsuo;Junichi Iwakiri;Naoya Kenmochi;Ikuo Yoshihara

  • Affiliations:

  • Faculty of Engineering, University of Miyazaki, Miyazaki, Japan 889-2192;NEC Corp., Tokyo, Japan 108-8001;Department of Computational Biology, Graduate School of Frontier Science, The University of Tokyo, Kashiwa, Japan 277-8568;Frontier Science Research Center, University of Miyazaki, Miyazaki, Japan 889-1692;Faculty of Engineering, University of Miyazaki, Miyazaki, Japan 889-2192

  • Venue:
  • Artificial Life and Robotics
  • Year:
  • 2013

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Abstract

Small nucleolar RNAs (snoRNAs) are known that they will participate with RNA modification. However, detail functions of snoRNAs have not been clear still yet. In order to make clear functions of snoRNA, finding more snoRNAs and studying their works in cells are required. In this paper, we propose a method to detect snoRNA genes using extended-weight-updating self-organizing map (eSOM). An input vector to eSOM consists of a feature vector and a target vector. Different from a conventional SOM, a winner node for an input vector is decided by the feature vector only, and all the weights around the winner node are updated to be close to the input vector. We employ bases appearance probabilities and complementary base pair ratio for a feature vector. A target vector is a flag which is 1.0 or 0.0 for a positive or a negative sample, respectively. Experimental results showed our method achieved 91 and 93 % detection ratio for boxC/D and boxH/ACA type snoRNA genes, respectively.