Academic Journal
MaGIC: a machine learning tool set and web application for monoallelic gene inference from chromatin
| Title: | MaGIC: a machine learning tool set and web application for monoallelic gene inference from chromatin |
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| Authors: | Svetlana Vinogradova, Sachit D. Saksena, Henry N. Ward, Sébastien Vigneau, Alexander A. Gimelbrant |
| Source: | BMC Bioinformatics, Vol 20, Iss 1, Pp 1-5 (2019) |
| Publisher Information: | BMC, 2019. |
| Publication Year: | 2019 |
| Collection: | LCC:Computer applications to medicine. Medical informatics LCC:Biology (General) |
| Subject Terms: | Monoallelic expression, Chromatin, Chromatin signature, Software pipeline, Shiny app, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5 |
| More Details: | 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. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1471-2105 |
| Relation: | http://link.springer.com/article/10.1186/s12859-019-2679-7; https://doaj.org/toc/1471-2105 |
| DOI: | 10.1186/s12859-019-2679-7 |
| Access URL: | https://doaj.org/article/aa9993ef86ab4cee88cfbaf87946b129 |
| Accession Number: | edsdoj.9993ef86ab4cee88cfbaf87946b129 |
| Database: | Directory of Open Access Journals |
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| ISSN: | 14712105 |
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| DOI: | 10.1186/s12859-019-2679-7 |
| Published in: | BMC Bioinformatics |
| Language: | English |