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CNIT: a fast and accurate web tool for identifying protein-coding and long non-coding transcripts based on intrinsic sequence composition.
Guo, Jin-Cheng; Fang, Shuang-Sang; Wu, Yang; Zhang, Jian-Hua; Chen, Yang; Liu, Jing; Wu, Bo; Wu, Jia-Rui; Li, En-Min; Xu, Li-Yan; Sun, Liang; Zhao, Yi.
Afiliação
  • Guo JC; Beijing University of Chinese Medicine, Chaoyang District, Beijing 100029, China.
  • Fang SS; Key Laboratory of Molecular Biology in High Cancer Incidence Coastal Chaoshan Area of Guangdong Higher Education Institutes, Shantou University Medical College, Shantou 515041, China.
  • Wu Y; Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.
  • Zhang JH; Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.
  • Chen Y; University of Chinese Academy of Sciences, Beijing 100049, China.
  • Liu J; Beijing University of Chinese Medicine, Chaoyang District, Beijing 100029, China.
  • Wu B; Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.
  • Wu JR; Department of Blood Transfusion, Peking University People's Hospital, Beijing 100000, China.
  • Li EM; Key Laboratory of Molecular Biology in High Cancer Incidence Coastal Chaoshan Area of Guangdong Higher Education Institutes, Shantou University Medical College, Shantou 515041, China.
  • Xu LY; The College of Life Sciences, Northwest University, Xi'an 710069, China.
  • Sun L; Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.
  • Zhao Y; Beijing University of Chinese Medicine, Chaoyang District, Beijing 100029, China.
Nucleic Acids Res ; 47(W1): W516-W522, 2019 07 02.
Article em En | MEDLINE | ID: mdl-31147700
ABSTRACT
As more and more high-throughput data has been produced by next-generation sequencing, it is still a challenge to classify RNA transcripts into protein-coding or non-coding, especially for poorly annotated species. We upgraded our original coding potential calculator, CNCI (Coding-Non-Coding Index), to CNIT (Coding-Non-Coding Identifying Tool), which provides faster and more accurate evaluation of the coding ability of RNA transcripts. CNIT runs âˆ¼200 times faster than CNCI and exhibits more accuracy compared with CNCI (0.98 versus 0.94 for human, 0.95 versus 0.93 for mouse, 0.93 versus 0.92 for zebrafish, 0.93 versus 0.92 for fruit fly, 0.92 versus 0.88 for worm, and 0.98 versus 0.85 for Arabidopsis transcripts). Moreover, the AUC values of 11 animal species and 27 plant species showed that CNIT was capable of obtaining relatively accurate identification results for almost all eukaryotic transcripts. In addition, a mobile-friendly web server is now freely available at http//cnit.noncode.org/CNIT.
Assuntos

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Software / Proteínas / Análise de Sequência de RNA / RNA Longo não Codificante Tipo de estudo: Prognostic_studies Limite: Animals / Humans Idioma: En Revista: Nucleic Acids Res Ano de publicação: 2019 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Software / Proteínas / Análise de Sequência de RNA / RNA Longo não Codificante Tipo de estudo: Prognostic_studies Limite: Animals / Humans Idioma: En Revista: Nucleic Acids Res Ano de publicação: 2019 Tipo de documento: Article País de afiliação: China