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Best practices for differential accessibility analysis in single-cell epigenomics

ABSTRACT: Best practices for differential accessibility analysis in single-cell epigenomics

PROVIDER: PRJNA962539 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR24326672_1.fastq.gz	Fastqsanger.gz
	SRR24326672_2.fastq.gz	Fastqsanger.gz
	SRR24326673_1.fastq.gz	Fastqsanger.gz
	SRR24326673_2.fastq.gz	Fastqsanger.gz
	SRR24326674_1.fastq.gz	Fastqsanger.gz

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Best practices for differential accessibility analysis in single-cell epigenomics.

Project description:Differential accessibility (DA) analysis of single-cell epigenomics data enables the discovery of regulatory programs that establish cell type identity and steer responses to physiological and pathophysiological perturbations. While many statistical methods to identify DA regions have been developed, the principles that determine the performance of these methods remain unclear. As a result, there is no consensus on the most appropriate statistical methods for DA analysis of single-cell epigenomics data. Here, we present a systematic evaluation of statistical methods that have been applied to identify DA regions in single-cell ATAC-seq (scATAC-seq) data. We leverage a compendium of scATAC-seq experiments with matching bulk ATAC-seq or scRNA-seq in order to assess the accuracy, bias, robustness, and scalability of each statistical method. The structure of our experiments also provides the opportunity to define best practices for the analysis of scATAC-seq data beyond DA itself. We leverage this understanding to develop an R package implementing these best practices.

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Best practices for the visualization, mapping, and manipulation of R-loops

Project description:R-loops represent an abundant class of large non-B DNA structures in genomes. Even though they form transiently and at modest frequencies, interfering with R-loop formation or dissolution significantly impacts genome stability. Addressing the mechanism(s) of R-loop-mediated genome destabilization requires a precise characterization of their distribution in genomes. A number of independent methods have been developed to visualize and map R-loops, but their results are at times discordant, leading to confusion. Here we review the main existing methodologies underlying R-loop mapping and assess their limitations and the robustness of existing datasets. We offer a set of best practices to improve the reproducibility of maps, hoping that such guidelines could be useful for authors and referees alike. Finally, we offer a possible resolution to the apparent contradictions in R-loop mapping outcomes between antibody-based and RNase H1-based mapping approaches.

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