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SRAMP: prediction of mammalian N(6)-methyladenosine (m(6)A) sites based on sequence-derived features

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 mo...

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Detalles Bibliográficos
Autores principales: Zhou, Yuan, Zeng, Pan, Li, Yan-Hui, Zhang, Ziding, Cui, Qinghua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4889921/
https://www.ncbi.nlm.nih.gov/pubmed/26896799
http://dx.doi.org/10.1093/nar/gkw104
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author Zhou, Yuan
Zeng, Pan
Li, Yan-Hui
Zhang, Ziding
Cui, Qinghua
author_facet Zhou, Yuan
Zeng, Pan
Li, Yan-Hui
Zhang, Ziding
Cui, Qinghua
author_sort Zhou, Yuan
collection PubMed
description 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/.
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spelling pubmed-48899212016-06-06 SRAMP: prediction of mammalian N(6)-methyladenosine (m(6)A) sites based on sequence-derived features Zhou, Yuan Zeng, Pan Li, Yan-Hui Zhang, Ziding Cui, Qinghua Nucleic Acids Res Methods Online 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/. Oxford University Press 2016-06-02 2016-02-20 /pmc/articles/PMC4889921/ /pubmed/26896799 http://dx.doi.org/10.1093/nar/gkw104 Text en © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Methods Online
Zhou, Yuan
Zeng, Pan
Li, Yan-Hui
Zhang, Ziding
Cui, Qinghua
SRAMP: prediction of mammalian N(6)-methyladenosine (m(6)A) sites based on sequence-derived features
title SRAMP: prediction of mammalian N(6)-methyladenosine (m(6)A) sites based on sequence-derived features
title_full SRAMP: prediction of mammalian N(6)-methyladenosine (m(6)A) sites based on sequence-derived features
title_fullStr SRAMP: prediction of mammalian N(6)-methyladenosine (m(6)A) sites based on sequence-derived features
title_full_unstemmed SRAMP: prediction of mammalian N(6)-methyladenosine (m(6)A) sites based on sequence-derived features
title_short SRAMP: prediction of mammalian N(6)-methyladenosine (m(6)A) sites based on sequence-derived features
title_sort sramp: prediction of mammalian n(6)-methyladenosine (m(6)a) sites based on sequence-derived features
topic Methods Online
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4889921/
https://www.ncbi.nlm.nih.gov/pubmed/26896799
http://dx.doi.org/10.1093/nar/gkw104
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