NmSEER V2.0: a prediction tool for 2'-O-methylation sites based on random forest and multi-encoding combination.
BMC Bioinformatics
; 20(Suppl 25): 690, 2019 Dec 24.
Article
en En
| MEDLINE
| ID: mdl-31874624
ABSTRACT
BACKGROUND:
2'-O-methylation (2'-O-me or Nm) is a post-transcriptional RNA methylation modified at 2'-hydroxy, which is common in mRNAs and various non-coding RNAs. Previous studies revealed the significance of Nm in multiple biological processes. With Nm getting more and more attention, a revolutionary technique termed Nm-seq, was developed to profile Nm sites mainly in mRNA with single nucleotide resolution and high sensitivity. In a recent work, supported by the Nm-seq data, we have reported a method in silico for predicting Nm sites, which relies on nucleotide sequence information, and established an online server named NmSEER. More recently, a more confident dataset produced by refined Nm-seq was available. Therefore, in this work, we redesigned the prediction model to achieve a more robust performance on the new data.RESULTS:
We redesigned the prediction model from two perspectives, including machine learning algorithm and multi-encoding scheme combination. With optimization by 5-fold cross-validation tests and evaluation by independent test respectively, random forest was selected as the most robust algorithm. Meanwhile, one-hot encoding, together with position-specific dinucleotide sequence profile and K-nucleotide frequency encoding were collectively applied to build the final predictor.CONCLUSIONS:
The predictor of updated version, named NmSEER V2.0, achieves an accurate prediction performance (AUROC = 0.862) and has been settled into a brand-new server, which is available at http//www.rnanut.net/nmseer-v2/ for free.Palabras clave
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Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Interfaz Usuario-Computador
/
ARN
Tipo de estudio:
Clinical_trials
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Prognostic_studies
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Risk_factors_studies
Idioma:
En
Revista:
BMC Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2019
Tipo del documento:
Article
País de afiliación:
China