MaGIC: a machine learning tool set and web application for monoallelic gene inference from chromatin.
In: BMC Bioinformatics, Jg. 20 (2019-02-28), Heft 1, S. 1-5
Online
academicJournal
Zugriff:
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. [ABSTRACT FROM AUTHOR]
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Titel: |
MaGIC: a machine learning tool set and web application for monoallelic gene inference from chromatin.
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Autor/in / Beteiligte Person: | Vinogradova, Svetlana ; Saksena, Sachit D. ; Ward, Henry N. ; Vigneau, Sébastien ; Gimelbrant, Alexander A. |
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Zeitschrift: | BMC Bioinformatics, Jg. 20 (2019-02-28), Heft 1, S. 1-5 |
Veröffentlichung: | 2019 |
Medientyp: | academicJournal |
ISSN: | 1471-2105 (print) |
DOI: | 10.1186/s12859-019-2679-7 |
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