Abstract
BackgroundA 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.ResultsWe 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.ConclusionThe 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.
Highlights
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
As an alternative to direct measurement, we have identified a chromatin signature of monoallelic expression, which can be applied to detect MAE in polyclonal samples [10]
The first chromatin mark is associated with active transcription and the second one is associated with silencing; MAE genes are enriched among genes displaying a characteristic chromatin signature: the two marks simultaneously occurring in the gene body
Summary
The MaGIC pipeline begins with ChIP-seq data processing and concludes with the prediction of MAE genes based on this data (see Implementation for more details). We process ChIP-seq files into gene-body or promoter enrichment normalized to control data This processed signal is used to classify genes into MAE and BAE using existing or user-generated models. The precision score is superior if the user wants to have the lowest number of false positives possible e.g., in identifying high-confidence MAE genes These gene lists can be further used to design experiments aimed at studying MAE genes’ properties. Using more histone marks data originating from high-quality ChIP-seq experiments and getting matching training data from a bigger number of clones would potentially increase the performance of the classifiers and allow for more precise predictions of MAE genes in the mouse
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