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

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 m ono a llelic g ene i nference from c hromatin (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. Electronic supplementary material The online version of this article (10.1186/s12859-019-2679-7) contains supplementary material, which is available to authorized users.

Introns control stochastic allele expression bias

“…It has been observed independently in yeast 32 , 33 , worms 34 , 35 , flies 36 , mice 6 , 37 and humans 4 , 5 , with each group developing their own terminology for the phenomenon. Scientists and clinicians have referred to stochastic autosomal allele bias as allele-specific expression (ASE) 12 , 38 – 41 , random autosomal monoallelic expression (RAME/RMAE) 3 , 37 , 42 , monoallelic expression (MAE) 5 , 6 , 43 , 44 , allelic imbalance 29 , or allele differential expression (ADE) 33 . Scientists using fluorescent reporters to study expression bias in bacteria (which do not have alleles per se) 45 and yeast 32 referred to and measured the relative degree of bias as intrinsic noise.…”

Foreign RNA spike-ins enable accurate allele-specific expression analysis at scale

“…AI analysis has been increasingly used for understanding regulatory variation (Uechi et al, 2020;GTEx_Consortium, 2017;Pirinen et al, 2015;Moyerbrailean et al, 2016;Mohammadi et al, 2019;Vierstra et al, 2020). Besides uncovering genetic effects, allele-specific analysis can reveal epigenetic gene regulation in cis, including imprinting (Tucci et al, 2019), X-chromosome inactivation (Galupa and Heard, 2018), and autosomal monoallelic expression (Gimelbrant et al, 2007;Vinogradova et al, 2019;Chess, 2016;Zwemer et al, 2012;Gendrel et al, 2014).…”