MaGIC: a machine learning tool set and web application for monoallelic gene inference from chromatin
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12859_2019_Article_2679.pdf
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Author(s)
Vinogradova, Svetlana
Ward, Henry N
Vigneau, Sébastien
Gimelbrant, Alexander A
Saksena, Sachit Dinesh
Date Issued
February 2019
Journal
BMC Bioinformatics
Publisher
BioMed Central
Citation
Vinogradova, Svetlana, Sachit D. Saksena, Henry N. Ward, Sébastien Vigneau and Alexander A. Gimelbrant. "MaGIC: a machine learning tool set and web application for monoallelic gene inference from chromatin." BMC Bioinformatics (2019) 20:106.
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Computational and Systems Biology Program
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DOI of Published Version
https://doi.org/10.1186/s12859-019-2679-7
