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Predicting tissue-specific expressions based on sequence characteristics

허철구|이도헌|Hyojung Paik|Taewoo Ryu|Hyoung-Sam Heo|Seung-Won Seo
생화학분자생물학회 (Korean Society Of Biochemistry And Molecular Biology)|2011.04|BMB Reports|제44권 제4호|pp. 250-255

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참고문헌

키워드

Random forestDomainHousekeepingTissue-specificTranscription factor binding site
주제분류화학

초록

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. [BMB reports 2011; 44(4): 250-255]

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. [BMB reports 2011; 44(4): 250-255]

참고문헌 (37)

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