MaGIC: a machine learning tool set and web application for monoallelic gene inference from chromatin.
BMC Bioinformatics
; 20(1): 106, 2019 Feb 28.
Article
en En
| MEDLINE
| ID: mdl-30819107
ABSTRACT
BACKGROUND:
A large fraction of human and mouse autosomal genes are subject to random monoallelic expression (MAE), an epigenetic mechanism characterized by allele-specific gene expression that varies between clonal cell lineages. MAE is highly cell-type specific and mapping it in a large number of cell and tissue types can provide insight into its biological function. Its detection, however, remains challenging.RESULTS:
We previously reported that a sequence-independent chromatin signature identifies, with high sensitivity and specificity, genes subject to MAE in multiple tissue types using readily available ChIP-seq data. Here we present an implementation of this method as a user-friendly, open-source software pipeline for monoallelic gene inference from chromatin (MaGIC). The source code for the MaGIC pipeline and the Shiny app is available at https//github.com/gimelbrantlab/magic .CONCLUSION:
The pipeline can be used by researchers to map monoallelic expression in a variety of cell types using existing models and to train new models with additional sets of chromatin marks.Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Cromatina
/
Internet
/
Alelos
/
Aprendizaje Automático
/
Genes
Tipo de estudio:
Prognostic_studies
Límite:
Animals
/
Humans
Idioma:
En
Revista:
BMC Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2019
Tipo del documento:
Article
País de afiliación:
Estados Unidos