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scDALI: modeling allelic heterogeneity in single cells reveals context-specific genetic regulation

While it is established that the functional impact of genetic variation can vary across cell types and states, capturing this diversity remains challenging. Current studies using bulk sequencing either ignore this heterogeneity or use sorted cell populations, reducing discovery and explanatory power...

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Detalles Bibliográficos
Autores principales: Heinen, Tobias, Secchia, Stefano, Reddington, James P., Zhao, Bingqing, Furlong, Eileen E. M., Stegle, Oliver
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8734213/
https://www.ncbi.nlm.nih.gov/pubmed/34991671
http://dx.doi.org/10.1186/s13059-021-02593-8
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author Heinen, Tobias
Secchia, Stefano
Reddington, James P.
Zhao, Bingqing
Furlong, Eileen E. M.
Stegle, Oliver
author_facet Heinen, Tobias
Secchia, Stefano
Reddington, James P.
Zhao, Bingqing
Furlong, Eileen E. M.
Stegle, Oliver
author_sort Heinen, Tobias
collection PubMed
description While it is established that the functional impact of genetic variation can vary across cell types and states, capturing this diversity remains challenging. Current studies using bulk sequencing either ignore this heterogeneity or use sorted cell populations, reducing discovery and explanatory power. Here, we develop scDALI, a versatile computational framework that integrates information on cellular states with allelic quantifications of single-cell sequencing data to characterize cell-state-specific genetic effects. We apply scDALI to scATAC-seq profiles from developing F1 Drosophila embryos and scRNA-seq from differentiating human iPSCs, uncovering heterogeneous genetic effects in specific lineages, developmental stages, or cell types. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02593-8.
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spelling pubmed-87342132022-01-07 scDALI: modeling allelic heterogeneity in single cells reveals context-specific genetic regulation Heinen, Tobias Secchia, Stefano Reddington, James P. Zhao, Bingqing Furlong, Eileen E. M. Stegle, Oliver Genome Biol Method While it is established that the functional impact of genetic variation can vary across cell types and states, capturing this diversity remains challenging. Current studies using bulk sequencing either ignore this heterogeneity or use sorted cell populations, reducing discovery and explanatory power. Here, we develop scDALI, a versatile computational framework that integrates information on cellular states with allelic quantifications of single-cell sequencing data to characterize cell-state-specific genetic effects. We apply scDALI to scATAC-seq profiles from developing F1 Drosophila embryos and scRNA-seq from differentiating human iPSCs, uncovering heterogeneous genetic effects in specific lineages, developmental stages, or cell types. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02593-8. BioMed Central 2022-01-06 /pmc/articles/PMC8734213/ /pubmed/34991671 http://dx.doi.org/10.1186/s13059-021-02593-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Method
Heinen, Tobias
Secchia, Stefano
Reddington, James P.
Zhao, Bingqing
Furlong, Eileen E. M.
Stegle, Oliver
scDALI: modeling allelic heterogeneity in single cells reveals context-specific genetic regulation
title scDALI: modeling allelic heterogeneity in single cells reveals context-specific genetic regulation
title_full scDALI: modeling allelic heterogeneity in single cells reveals context-specific genetic regulation
title_fullStr scDALI: modeling allelic heterogeneity in single cells reveals context-specific genetic regulation
title_full_unstemmed scDALI: modeling allelic heterogeneity in single cells reveals context-specific genetic regulation
title_short scDALI: modeling allelic heterogeneity in single cells reveals context-specific genetic regulation
title_sort scdali: modeling allelic heterogeneity in single cells reveals context-specific genetic regulation
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8734213/
https://www.ncbi.nlm.nih.gov/pubmed/34991671
http://dx.doi.org/10.1186/s13059-021-02593-8
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