MaGIC: a machine learning tool set and web application for monoallelic gene inference from chromatin
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...
| Main Authors: | , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
BioMed Central
2019
|
| Online Access: | http://hdl.handle.net/1721.1/121085 |
| _version_ | 1811096891411136512 |
|---|---|
| author | Vinogradova, Svetlana Ward, Henry N Vigneau, Sébastien Gimelbrant, Alexander A Saksena, Sachit Dinesh |
| author2 | Massachusetts Institute of Technology. Computational and Systems Biology Program |
| author_facet | Massachusetts Institute of Technology. Computational and Systems Biology Program Vinogradova, Svetlana Ward, Henry N Vigneau, Sébastien Gimelbrant, Alexander A Saksena, Sachit Dinesh |
| author_sort | Vinogradova, Svetlana |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T16:51:01Z |
| format | Article |
| id | mit-1721.1/121085 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T16:51:01Z |
| publishDate | 2019 |
| publisher | BioMed Central |
| record_format | dspace |
| spelling | mit-1721.1/1210852022-09-29T21:54:34Z MaGIC: a machine learning tool set and web application for monoallelic gene inference from chromatin Vinogradova, Svetlana Ward, Henry N Vigneau, Sébastien Gimelbrant, Alexander A Saksena, Sachit Dinesh Massachusetts Institute of Technology. Computational and Systems Biology Program Saksena, Sachit Dinesh 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. National Institutes of Health (U.S.) (award U54 HG007963) 2019-03-26T12:26:25Z 2019-03-26T12:26:25Z 2019-02 2019-03-03T04:14:08Z Article http://purl.org/eprint/type/JournalArticle 1471-2105 http://hdl.handle.net/1721.1/121085 Vinogradova, Svetlana, Sachit D. Saksena, Henry N. Ward, Sébastien Vigneau and Alexander A. Gimelbrant. "MaGIC: a machine learning tool set and web application for monoallelic gene inference from chromatin." BMC Bioinformatics (2019) 20:106. en https://doi.org/10.1186/s12859-019-2679-7 BMC Bioinformatics Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ The Author(s). application/pdf BioMed Central BioMed Central |
| spellingShingle | Vinogradova, Svetlana Ward, Henry N Vigneau, Sébastien Gimelbrant, Alexander A Saksena, Sachit Dinesh 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 |
| title_full | MaGIC: a machine learning tool set and web application for monoallelic gene inference from chromatin |
| title_fullStr | MaGIC: a machine learning tool set and web application for monoallelic gene inference from chromatin |
| title_full_unstemmed | MaGIC: a machine learning tool set and web application for monoallelic gene inference from chromatin |
| title_short | MaGIC: a machine learning tool set and web application for monoallelic gene inference from chromatin |
| title_sort | magic a machine learning tool set and web application for monoallelic gene inference from chromatin |
| url | http://hdl.handle.net/1721.1/121085 |
| work_keys_str_mv | AT vinogradovasvetlana magicamachinelearningtoolsetandwebapplicationformonoallelicgeneinferencefromchromatin AT wardhenryn magicamachinelearningtoolsetandwebapplicationformonoallelicgeneinferencefromchromatin AT vigneausebastien magicamachinelearningtoolsetandwebapplicationformonoallelicgeneinferencefromchromatin AT gimelbrantalexandera magicamachinelearningtoolsetandwebapplicationformonoallelicgeneinferencefromchromatin AT saksenasachitdinesh magicamachinelearningtoolsetandwebapplicationformonoallelicgeneinferencefromchromatin |