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MaGIC: a machine learning tool set and web application for monoallelic gene inference from chromatin

By Svetlana Vinogradova, Sachit D. Saksena, Henry N. Ward, S├ębastien Vigneau, Alexander A. Gimelbrant

Posted 22 Jun 2018
bioRxiv DOI: 10.1101/353359 (published DOI: 10.1186/s12859-019-2679-7)

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. 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). Availability and implementation: The source code for the MaGIC pipeline and the Shiny app is available at https://github.com/gimelbrantlab/magic.

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