ArticleBMC bioinformatics2019
MaGIC: a machine learning tool set and web application for monoallelic gene inference from chromatin.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 8 citations in OpenAlex.
- Article
- Foreign RNA spike-ins enable accurate allele-specific expression analysis at scale.Bioinformatics (Oxford, England) · 2023Article
- Foreign RNA spike-ins enable accurate allele-specific expression analysis at scale.bioRxiv : the preprint server for biology · 2023Article
- Replicate sequencing libraries are important for quantification of allelic imbalance.Nature communications · 2021Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
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.
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