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Korean Society for Biochemistry and Molecular Biology (생화학분자생물학회)
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Predicting tissue-specific expressions based on sequence characteristics

  • Paik, Hyo-Jung (Plant Systems Engineering Center, KRIBB) ;
  • Ryu, Tae-Woo (Red Sea Laboratory for Integrative Systems Biology, Computational Biosciences Research Center, Division of Chemical & Life Sciences and Engineering, King Abdullah University of Science and Technology (KAUST)) ;
  • Heo, Hyoung-Sam (Plant Systems Engineering Center, KRIBB) ;
  • Seo, Seung-Won (Plant Systems Engineering Center, KRIBB) ;
  • Lee, Do-Heon (Department of Bio and Brain Engineering, KAIST) ;
  • Hur, Cheol-Goo (Plant Systems Engineering Center, KRIBB)
  • Received : 2010.12.29
  • Accepted : 2011.01.20
  • Published : 2011.04.30
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Abstract

In multicellular organisms, including humans, understanding expression specificity at the tissue level is essential for interpreting protein function, such as tissue differentiation. We developed a prediction approach via generated sequence features from overrepresented patterns in housekeeping (HK) and tissue-specific (TS) genes to classify TS expression in humans. Using TS domains and transcriptional factor binding sites (TFBSs), sequence characteristics were used as indices of expressed tissues in a Random Forest algorithm by scoring exclusive patterns considering the biological intuition; TFBSs regulate gene expression, and the domains reflect the functional specificity of a TS gene. Our proposed approach displayed better performance than previous attempts and was validated using computational and experimental methods.

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References

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