MaGIC: a machine learning tool set and web application for monoallelic gene inference from chromatin.
BMC Bioinformatics
; 20(1): 106, 2019 Feb 28.
Article
in 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.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Chromatin
/
Internet
/
Alleles
/
Machine Learning
/
Genes
Type of study:
Prognostic_studies
Limits:
Animals
/
Humans
Language:
En
Journal:
BMC Bioinformatics
Year:
2019
Document type:
Article