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조회 373  인쇄하기 주소복사 트위터 공유 페이스북 공유 
Human microRNA prediction through a probabilistic co-learning model of sequence and structure

Jin-Wu Nam1,2, Ki-Roo Shin3, Jinju Han4, Yoontae Lee4, V. Narry Kim4 and Byoung-Tak Zhang1,2,3,*

1Graduate Program in Bioinformatics, Seoul National University Seoul 151-744, Korea 
2Center for Bioinformation Technology (CBIT), Seoul National University Seoul 151-744, Korea 
3Biointelligence Laboratory, School of Computer Science and Engineering, Seoul National University Seoul 151-744, Korea 
4Department of Biological Sciences, Seoul National University Seoul 151-744, Korea 

*To whom correspondence should be addressed.

MicroRNAs (miRNAs) are small regulatory RNAs of ∼22 nt. Although hundreds of miRNAs have been identified through experimental complementary DNA cloning methods and computational efforts, previous approaches could detect only abundantly expressed miRNAs or close homologs of previously identified miRNAs. Here, we introduce a probabilistic co-learning model for miRNA gene finding, ProMiR, which simultaneously considers the structure and sequence of miRNA precursors (pre-miRNAs). On 5-fold cross-validation with 136 referenced human datasets, the efficiency of the classification shows 73% sensitivity and 96% specificity. When applied to genome screening for novel miRNAs on human chromosomes 16, 17, 18 and 19, ProMiR effectively searches distantly homologous patterns over diverse pre-miRNAs, detecting at least 23 novel miRNA gene candidates. Importantly, the miRNA gene candidates do not demonstrate clear sequence similarity to the known miRNA genes. By quantitative PCR followed by RNA interference against Drosha, we experimentally confirmed that 9 of the 23 representative candidate genes express transcripts that are processed by the miRNA biogenesis enzyme Drosha in HeLa cells, indicating that ProMiR may successfully predict miRNA genes with at least 40% accuracy. Our study suggests that the miRNA gene family may be more abundant than previously anticipated, and confer highly extensive regulatory networks on eukaryotic cells.

- 형식: Research article
- 게재일: 2005년 06월 (BRIC 등록일 2014-11-21)
- 연구진: 국내연구진태극기
- 분야: Biochemistry, Genetics
- 피인용횟수: 120회 이상 인용된 논문
광유전학의 과거, 현재와 미래[Neuron]
발표: 김윤석 (Stanford University)
일자: 2020년 7월 30일 (목) 오후 02시 (한국시간)
언어: 한국어
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모집인원: 50명
모집기간: ~7/15
신청조건: BRIC 회원
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