A comprehensive comparison of differential accessibility analysis methods for ATAC-seq data
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ABSTRACT:
PROVIDER: PRJNA542717 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR9050830_1.fastq.gz | Fastqsanger.gz | |||
| SRR9050830_2.fastq.gz | Fastqsanger.gz | |||
| SRR9050823_1.fastq.gz | Fastqsanger.gz | |||
| SRR9050823_2.fastq.gz | Fastqsanger.gz | |||
| SRR9050824_1.fastq.gz | Fastqsanger.gz |
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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.
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Project description:ATAC-seq is a frequently used assay to study chromatin accessibility levels. Differential chromatin accessibility level analyses between biological groups and functional interpretation of these differential regions are essential in ATAC-seq data analyses. Although distinct methods and analyses pipelines are developed for this purpose, we are missing a stand-along R package that combines state-of-the art differential analyses and functional enrichment analyses. To fill this gap, we developed cinaR, which is a single wrapper function and provides users with various configurable plots. To show-case this pipeline we provide these bulk ATAC-seq samples from bone-marrow of 3 & 18 months old New Zelland Obese (NZO) mice and show the activation of pro-inflammatory pathways.
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Project description:chromatin accessibility (ATAC-seq) experiment. HeLa cells were primed with IFNγ for 24 hours, followed by IFNγ washout. After 48h, naïve and primed cells were induced by IFNγ for 1h and 3h. Cells were harvested at indicated time points and processed for ATAC-seq.
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