Artículo
Genome-wide hairpins datasets of animals and plants for novel miRNA prediction
Bugnon, Leandro Ariel
; Yones, Cristian Ariel
; Raad, Jonathan
; Milone, Diego Humberto
; Stegmayer, Georgina
Fecha de publicación:
08/2019
Editorial:
Elsevier
Revista:
Data in Brief
ISSN:
2352-3409
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
This article makes available several genome-wide datasets, which can be used for training microRNA (miRNA) classifiers. The hairpin sequences available are from the genomes of: Homo sapiens, Arabidopsis thaliana, Anopheles gambiae, Caenorhabditis elegans and Drosophila melanogaster. Each dataset provides the genome data divided into sequences and a set of computed features for predictions. Each sequence has one label: i) ?positive?: meaning that it is a well-known pre-miRNA, according to miRBase v21; or ii) ?unlabeled?: indicating that the sequence has not (yet) a known function and could be a possible candidate to novel pre-miRNA. Due to the fact that selecting an informative feature set is very important for a good pre-miRNA classifier, a representative feature set with large discriminative power has been calculated and it is provided, as well, for each genome. This feature set contains typical information about sequence, topology and structure. Dataset was publically shared in https://sourceforge.net/projects/sourcesinc/files/mirdata/.
Palabras clave:
MicroRNA
,
Bioinformatics
,
miRNA features
,
Genome-wide data
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Articulos(SINC(I))
Articulos de INST. DE INVESTIGACION EN SEÑALES, SISTEMAS E INTELIGENCIA COMPUTACIONAL
Articulos de INST. DE INVESTIGACION EN SEÑALES, SISTEMAS E INTELIGENCIA COMPUTACIONAL
Citación
Bugnon, Leandro Ariel; Yones, Cristian Ariel; Raad, Jonathan; Milone, Diego Humberto; Stegmayer, Georgina; Genome-wide hairpins datasets of animals and plants for novel miRNA prediction; Elsevier; Data in Brief; 25; 8-2019
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