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Comprehensive analysis of single cell ATAC-seq data with SnapATAC

Identification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding the origin of cellular diversity. Conventional assays to map regulatory elements via open chromatin analysis of primary tissues is hindered by sample heterogeneity. Si...

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Autores principales: Fang, Rongxin, Preissl, Sebastian, Li, Yang, Hou, Xiaomeng, Lucero, Jacinta, Wang, Xinxin, Motamedi, Amir, Shiau, Andrew K., Zhou, Xinzhu, Xie, Fangming, Mukamel, Eran A., Zhang, Kai, Zhang, Yanxiao, Behrens, M. Margarita, Ecker, Joseph R., Ren, Bing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7910485/
https://www.ncbi.nlm.nih.gov/pubmed/33637727
http://dx.doi.org/10.1038/s41467-021-21583-9
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author Fang, Rongxin
Preissl, Sebastian
Li, Yang
Hou, Xiaomeng
Lucero, Jacinta
Wang, Xinxin
Motamedi, Amir
Shiau, Andrew K.
Zhou, Xinzhu
Xie, Fangming
Mukamel, Eran A.
Zhang, Kai
Zhang, Yanxiao
Behrens, M. Margarita
Ecker, Joseph R.
Ren, Bing
author_facet Fang, Rongxin
Preissl, Sebastian
Li, Yang
Hou, Xiaomeng
Lucero, Jacinta
Wang, Xinxin
Motamedi, Amir
Shiau, Andrew K.
Zhou, Xinzhu
Xie, Fangming
Mukamel, Eran A.
Zhang, Kai
Zhang, Yanxiao
Behrens, M. Margarita
Ecker, Joseph R.
Ren, Bing
author_sort Fang, Rongxin
collection PubMed
description Identification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding the origin of cellular diversity. Conventional assays to map regulatory elements via open chromatin analysis of primary tissues is hindered by sample heterogeneity. Single cell analysis of accessible chromatin (scATAC-seq) can overcome this limitation. However, the high-level noise of each single cell profile and the large volume of data pose unique computational challenges. Here, we introduce SnapATAC, a software package for analyzing scATAC-seq datasets. SnapATAC dissects cellular heterogeneity in an unbiased manner and map the trajectories of cellular states. Using the Nyström method, SnapATAC can process data from up to a million cells. Furthermore, SnapATAC incorporates existing tools into a comprehensive package for analyzing single cell ATAC-seq dataset. As demonstration of its utility, SnapATAC is applied to 55,592 single-nucleus ATAC-seq profiles from the mouse secondary motor cortex. The analysis reveals ~370,000 candidate regulatory elements in 31 distinct cell populations in this brain region and inferred candidate cell-type specific transcriptional regulators.
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spelling pubmed-79104852021-03-04 Comprehensive analysis of single cell ATAC-seq data with SnapATAC Fang, Rongxin Preissl, Sebastian Li, Yang Hou, Xiaomeng Lucero, Jacinta Wang, Xinxin Motamedi, Amir Shiau, Andrew K. Zhou, Xinzhu Xie, Fangming Mukamel, Eran A. Zhang, Kai Zhang, Yanxiao Behrens, M. Margarita Ecker, Joseph R. Ren, Bing Nat Commun Article Identification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding the origin of cellular diversity. Conventional assays to map regulatory elements via open chromatin analysis of primary tissues is hindered by sample heterogeneity. Single cell analysis of accessible chromatin (scATAC-seq) can overcome this limitation. However, the high-level noise of each single cell profile and the large volume of data pose unique computational challenges. Here, we introduce SnapATAC, a software package for analyzing scATAC-seq datasets. SnapATAC dissects cellular heterogeneity in an unbiased manner and map the trajectories of cellular states. Using the Nyström method, SnapATAC can process data from up to a million cells. Furthermore, SnapATAC incorporates existing tools into a comprehensive package for analyzing single cell ATAC-seq dataset. As demonstration of its utility, SnapATAC is applied to 55,592 single-nucleus ATAC-seq profiles from the mouse secondary motor cortex. The analysis reveals ~370,000 candidate regulatory elements in 31 distinct cell populations in this brain region and inferred candidate cell-type specific transcriptional regulators. Nature Publishing Group UK 2021-02-26 /pmc/articles/PMC7910485/ /pubmed/33637727 http://dx.doi.org/10.1038/s41467-021-21583-9 Text en © The Author(s) 2021 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Fang, Rongxin
Preissl, Sebastian
Li, Yang
Hou, Xiaomeng
Lucero, Jacinta
Wang, Xinxin
Motamedi, Amir
Shiau, Andrew K.
Zhou, Xinzhu
Xie, Fangming
Mukamel, Eran A.
Zhang, Kai
Zhang, Yanxiao
Behrens, M. Margarita
Ecker, Joseph R.
Ren, Bing
Comprehensive analysis of single cell ATAC-seq data with SnapATAC
title Comprehensive analysis of single cell ATAC-seq data with SnapATAC
title_full Comprehensive analysis of single cell ATAC-seq data with SnapATAC
title_fullStr Comprehensive analysis of single cell ATAC-seq data with SnapATAC
title_full_unstemmed Comprehensive analysis of single cell ATAC-seq data with SnapATAC
title_short Comprehensive analysis of single cell ATAC-seq data with SnapATAC
title_sort comprehensive analysis of single cell atac-seq data with snapatac
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7910485/
https://www.ncbi.nlm.nih.gov/pubmed/33637727
http://dx.doi.org/10.1038/s41467-021-21583-9
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