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

BMC Bioinformatics

Sachit D. Saksena¹  Alexander A. Gimelbrant²  Svetlana Vinogradova²  Sébastien Vigneau²  Henry N. Ward² 

[1] Computational and Systems Biology, Massachusetts Institute of Technology;Department of Cancer Biology, Dana-Farber Cancer Institute;

关键词: Monoallelic expression;    Chromatin;    Chromatin signature;    Software pipeline;    Shiny app;   

DOI  :  10.1186/s12859-019-2679-7

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown