miRCat2: accurate prediction of plant and animal microRNAs from next-generation sequencing datasets.
Bioinformatics
; 33(16): 2446-2454, 2017 Aug 15.
Article
em En
| MEDLINE
| ID: mdl-28407097
ABSTRACT
MOTIVATION MicroRNAs are a class of â¼21-22 nt small RNAs which are excised from a stable hairpin-like secondary structure. They have important gene regulatory functions and are involved in many pathways including developmental timing, organogenesis and development in eukaryotes. There are several computational tools for miRNA detection from next-generation sequencing datasets. However, many of these tools suffer from high false positive and false negative rates. Here we present a novel miRNA prediction algorithm, miRCat2. miRCat2 incorporates a new entropy-based approach to detect miRNA loci, which is designed to cope with the high sequencing depth of current next-generation sequencing datasets. It has a user-friendly interface and produces graphical representations of the hairpin structure and plots depicting the alignment of sequences on the secondary structure. RESULTS:
We test miRCat2 on a number of animal and plant datasets and present a comparative analysis with miRCat, miRDeep2, miRPlant and miReap. We also use mutants in the miRNA biogenesis pathway to evaluate the predictions of these tools. Results indicate that miRCat2 has an improved accuracy compared with other methods tested. Moreover, miRCat2 predicts several new miRNAs that are differentially expressed in wild-type versus mutants in the miRNA biogenesis pathway. AVAILABILITY AND IMPLEMENTATION miRCat2 is part of the UEA small RNA Workbench and is freely available from http//srna-workbench.cmp.uea.ac.uk/. CONTACT v.moulton@uea.ac.uk or s.moxon@uea.ac.uk. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Software
/
Biologia Computacional
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MicroRNAs
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Loci Gênicos
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Sequenciamento de Nucleotídeos em Larga Escala
Tipo de estudo:
Prognostic_studies
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Risk_factors_studies
Limite:
Animals
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2017
Tipo de documento:
Article
País de afiliação:
Reino Unido