miREM: an expectation-maximization approach for prioritizing miRNAs associated with gene-set.
BMC Bioinformatics
; 19(1): 299, 2018 08 10.
Article
en En
| MEDLINE
| ID: mdl-30097004
ABSTRACT
BACKGROUND:
The knowledge of miRNAs regulating the expression of sets of mRNAs has led to novel insights into numerous and diverse cellular mechanisms. While a single miRNA may regulate many genes, one gene can be regulated by multiple miRNAs, presenting a complex relationship to model for accurate predictions.RESULTS:
Here, we introduce miREM, a program that couples an expectation-maximization (EM) algorithm to the common approach of hypergeometric probability (HP), which improves the prediction and prioritization of miRNAs from gene-sets of interest. miREM has been made available through a web-server ( https//bioinfo-csi.nus.edu.sg/mirem2/ ) that can be accessed through an intuitive graphical user interface. The program incorporates a large compendium of human/mouse miRNA-target prediction databases to enhance prediction. Users may upload their genes of interest in various formats as an input and select whether to consider non-conserved miRNAs, amongst filtering options. Results are reported in a rich graphical interface that allows users to (i) prioritize predicted miRNAs through a scatterplot of HP p-values and EM scores; (ii) visualize the predicted miRNAs and corresponding genes through a heatmap; and (iii) identify and filter homologous or duplicated predictions by clustering them according to their seed sequences.CONCLUSION:
We tested miREM using RNAseq datasets from two single "spiked" knock-in miRNA experiments and two double knock-out miRNA experiments. miREM predicted these manipulated miRNAs as having high EM scores from the gene set signatures (i.e. top predictions for single knock-in and double knock-out miRNA experiments). Finally, we have demonstrated that miREM predictions are either similar or better than results provided by existing programs.Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Algoritmos
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Biología Computacional
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Bases de Datos de Ácidos Nucleicos
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MicroARNs
Tipo de estudio:
Prognostic_studies
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Risk_factors_studies
Límite:
Animals
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Humans
Idioma:
En
Revista:
BMC Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2018
Tipo del documento:
Article
País de afiliación:
Singapur