microRPM: a microRNA prediction model based only on plant small RNA sequencing data.
Bioinformatics
; 34(7): 1108-1115, 2018 04 01.
Article
em En
| MEDLINE
| ID: mdl-29136092
ABSTRACT
Motivation MicroRNAs (miRNAs) are endogenous non-coding small RNAs (of about 22 nucleotides), which play an important role in the post-transcriptional regulation of gene expression via either mRNA cleavage or translation inhibition. Several machine learning-based approaches have been developed to identify novel miRNAs from next generation sequencing (NGS) data. Typically, precursor/genomic sequences are required as references for most methods. However, the non-availability of genomic sequences is often a limitation in miRNA discovery in non-model plants. A systematic approach to determine novel miRNAs without reference sequences is thus necessary. Results:
In this study, an effective method was developed to identify miRNAs from non-model plants based only on NGS datasets. The miRNA prediction model was trained with several duplex structure-related features of mature miRNAs and their passenger strands using a support vector machine algorithm. The accuracy of the independent test reached 96.61% and 93.04% for dicots (Arabidopsis) and monocots (rice), respectively. Furthermore, true small RNA sequencing data from orchids was tested in this study. Twenty-one predicted orchid miRNAs were selected and experimentally validated. Significantly, 18 of them were confirmed in the qRT-PCR experiment. This novel approach was also compiled as a user-friendly program called microRPM (miRNA Prediction Model). Availability and implementation This resource is freely available at http//microRPM.itps.ncku.edu.tw. Contact nslin@sinica.edu.tw or sarah321@mail.ncku.edu.tw. Supplementary information Supplementary data are available at Bioinformatics online.
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Base de dados:
MEDLINE
Assunto principal:
Análise de Sequência de RNA
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Genoma de Planta
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MicroRNAs
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Sequenciamento de Nucleotídeos em Larga Escala
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Máquina de Vetores de Suporte
Idioma:
En
Ano de publicação:
2018
Tipo de documento:
Article