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Comprehensive analysis of single cell ATAC-seq data with SnapATAC

ABSTRACT: Comprehensive analysis of single cell ATAC-seq data with SnapATAC

PROVIDER: PRJNA523117 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR8592624_1.fastq.gz	Fastqsanger.gz
	SRR8592624_2.fastq.gz	Fastqsanger.gz
	SRR8592625_1.fastq.gz	Fastqsanger.gz
	SRR8592625_2.fastq.gz	Fastqsanger.gz
	SRR8592626_1.fastq.gz	Fastqsanger.gz

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Comprehensive analysis of single cell ATAC-seq data with SnapATAC

Project 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 volumes 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 candidate regulatory elements in 31 distinct cell populations in this brain region and inferred candidate cell-type specific transcriptional regulators.

2021-01-03 | GSE126724 | GEO

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Comprehensive analysis of single cell ATAC-seq data with SnapATAC.

Project 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.

| S-EPMC7910485 | biostudies-literature

A comprehensive comparison of differential accessibility analysis methods for ATAC-seq data

Project description:A comprehensive comparison of differential accessibility analysis methods for ATAC-seq data

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A comprehensive comparison of differential accessibility analysis methods for ATAC-seq data

Project description:Background: ATAC-seq is widely used to measure the chromatin accessibility and identify the open chromatin regions (OCRs). OCRs usually indicate the active regulatory elements in the genome and are directly associated with gene regulatory networks. Identification of differential accessibility regions (DARs) between different biological conditions is critical to measure the differential activity of regulatory elements. Differential analysis of ATAC-seq shares many similarities to differential expression analysis of RNA-seq data. However, the distribution of ATAC-seq signal is different from RNA-seq data, and higher sensitivity is desired for DARs identification. Many different tools can be used to perform differential analysis of ATAC-seq data, but a comprehensive comparison and benchmarking of these methods is still missing. Methods: Here, we used simulated datasets to systematically measure the sensitivity and specificity of 6 different methods. We further discussed the statistical and signal density cutoff in the differential analysis of ATAC-seq by applying to real data. Batch-effect is very common in high-throughput sequencing experiments. Results: We illustrated that batch-effect correction can dramatically improve the sensitivity in differential analysis of ATAC-seq data. Finally, we developed an easily usable package, BeCorrect, to perform batch-effort correction for visualizing corrected ATAC-seq signals on a genome browser. Conclusions: It is important to use PCA to check the samples distribution, and the Remove Unwanted Variation strategy can be used to correct the data to improve the sensitivity when strong batch effects are found in the data. Finally, BeCorrect can be used to correct the batch-effect of ATAC-seq data signal based on DARs analysis, and generate a proper visualization on a genome browser.

2020-06-29 | GSE131144 | GEO

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Project description:single cell ATAC-seq

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Comprehensive Human Embryogenesis Reference Tool using Single-Cell RNA-Sequencing Data

Project description:Comprehensive Human Embryogenesis Reference Tool using Single-Cell RNA-Sequencing Data

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Project description:Single cell ATAC-seq (scATAC-seq) was performed on macaque embryonic stem cell-derived cerebral organoids. scATAC-seq was performed on day 60 (2 months old cerebral organoid).

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