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 c...

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Published inBMC bioinformatics Vol. 20; no. 1; pp. 106 - 5
Main Authors Vinogradova, Svetlana, Saksena, Sachit D., Ward, Henry N., Vigneau, Sébastien, Gimelbrant, Alexander A.
Format Journal Article
LanguageEnglish
Published London BioMed Central 28.02.2019
BioMed Central Ltd
BMC
Subjects
Algorithms
Alleles
Animal models
Applications programs
Artificial intelligence
Biochemistry
Bioinformatics
Biology
Biomedical and Life Sciences
Business metrics
Cancer
Cell culture
Chromatin
Chromatin signature
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Data processing
Datasets
Epigenetic inheritance
Epigenetics
Gene expression
Generalized linear models
Genes
Genetic research
Genomes
House mouse
Inference
Internet software
Learning algorithms
Life Sciences
Machine learning
Mammals
Mapping
Methods
Microarrays
Monoallelic expression
Open source software
Pipelines
Shiny app
Software
Software pipeline
Source code
Transcriptome analysis
Web services
Web site management software
United States
United States > US
Chromatin
Chromatin signature
Software pipeline
Monoallelic expression
Shiny app
Online AccessGet full text
ISSN1471-2105
1471-2105
DOI10.1186/s12859-019-2679-7

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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.
AbstractList 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.
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.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.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 .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 .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.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.
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.
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.
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. 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. 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.
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. 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 . 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.
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 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. Keywords: Monoallelic expression, Chromatin, Chromatin signature, Software pipeline, Shiny app
ArticleNumber 106
Audience Academic
Author Ward, Henry N.
Gimelbrant, Alexander A.
Vinogradova, Svetlana
Saksena, Sachit D.
Vigneau, Sébastien
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  organization: Department of Cancer Biology, Dana-Farber Cancer Institute, Department of Genetics, Harvard Medical School, University of Minnesota-Twin Cities, Bioinformatics and Computational Biology Program
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  organization: Department of Cancer Biology, Dana-Farber Cancer Institute, Department of Genetics, Harvard Medical School
BackLink https://www.ncbi.nlm.nih.gov/pubmed/30819107$$D View this record in MEDLINE/PubMed
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CitedBy_id crossref_primary_10_1038_s41467_021_26798_4
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Issue 1
Keywords Chromatin
Chromatin signature
Software pipeline
Monoallelic expression
Shiny app
Language English
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Snippet Background A large fraction of human and mouse autosomal genes are subject to random monoallelic expression (MAE), an epigenetic mechanism characterized by...
A large fraction of human and mouse autosomal genes are subject to random monoallelic expression (MAE), an epigenetic mechanism characterized by...
Background A large fraction of human and mouse autosomal genes are subject to random monoallelic expression (MAE), an epigenetic mechanism characterized by...
Abstract Background A large fraction of human and mouse autosomal genes are subject to random monoallelic expression (MAE), an epigenetic mechanism...
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SubjectTerms Algorithms
Alleles
Animal models
Applications programs
Artificial intelligence
Biochemistry
Bioinformatics
Biology
Biomedical and Life Sciences
Business metrics
Cancer
Cell culture
Chromatin
Chromatin signature
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Data processing
Datasets
Epigenetic inheritance
Epigenetics
Gene expression
Generalized linear models
Genes
Genetic research
Genomes
House mouse
Inference
Internet software
Learning algorithms
Life Sciences
Machine learning
Mammals
Mapping
Methods
Microarrays
Monoallelic expression
Open source software
Pipelines
Shiny app
Software
Software pipeline
Source code
Transcriptome analysis
Web services
Web site management software
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Title MaGIC: a machine learning tool set and web application for monoallelic gene inference from chromatin
URI https://link.springer.com/article/10.1186/s12859-019-2679-7
https://www.ncbi.nlm.nih.gov/pubmed/30819107
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https://pubmed.ncbi.nlm.nih.gov/PMC6394031
https://doaj.org/article/aa9993ef86ab4cee88cfbaf87946b129
Volume 20
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