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SRAMP: prediction of mammalian N6-methyladenosine (m6A) sites based on sequence-derived features.
Zhou, Yuan; Zeng, Pan; Li, Yan-Hui; Zhang, Ziding; Cui, Qinghua.
Afiliação
  • Zhou Y; Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing 100191, China MOE Key Lab of Molecular Cardiovascular Sciences, Peking University, Beijing 100191, China Center for Noncoding RNA Medicine, Peking University Health Science Center, Beijing 100191, Chin
  • Zeng P; Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing 100191, China MOE Key Lab of Molecular Cardiovascular Sciences, Peking University, Beijing 100191, China Center for Noncoding RNA Medicine, Peking University Health Science Center, Beijing 100191, Chin
  • Li YH; Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing 100191, China MOE Key Lab of Molecular Cardiovascular Sciences, Peking University, Beijing 100191, China Center for Noncoding RNA Medicine, Peking University Health Science Center, Beijing 100191, Chin
  • Zhang Z; State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China.
  • Cui Q; Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing 100191, China MOE Key Lab of Molecular Cardiovascular Sciences, Peking University, Beijing 100191, China Center for Noncoding RNA Medicine, Peking University Health Science Center, Beijing 100191, Chin
Nucleic Acids Res ; 44(10): e91, 2016 06 02.
Article em En | MEDLINE | ID: mdl-26896799
N(6)-methyladenosine (m(6)A) is a prevalent RNA methylation modification involved in the regulation of degradation, subcellular localization, splicing and local conformation changes of RNA transcripts. High-throughput experiments have demonstrated that only a small fraction of the m(6)A consensus motifs in mammalian transcriptomes are modified. Therefore, accurate identification of RNA m(6)A sites becomes emergently important. For the above purpose, here a computational predictor of mammalian m(6)A site named SRAMP is established. To depict the sequence context around m(6)A sites, SRAMP combines three random forest classifiers that exploit the positional nucleotide sequence pattern, the K-nearest neighbor information and the position-independent nucleotide pair spectrum features, respectively. SRAMP uses either genomic sequences or cDNA sequences as its input. With either kind of input sequence, SRAMP achieves competitive performance in both cross-validation tests and rigorous independent benchmarking tests. Analyses of the informative features and overrepresented rules extracted from the random forest classifiers demonstrate that nucleotide usage preferences at the distal positions, in addition to those at the proximal positions, contribute to the classification. As a public prediction server, SRAMP is freely available at http://www.cuilab.cn/sramp/.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: RNA / Adenosina / Biologia Computacional / Mamíferos Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Animals Idioma: En Ano de publicação: 2016 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: RNA / Adenosina / Biologia Computacional / Mamíferos Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Animals Idioma: En Ano de publicação: 2016 Tipo de documento: Article