Comparison of differential accessibility analysis strategies for ATAC-seq data

• Published in: Scientific reports 2020-06, Vol.10 (1), p.10150-10150, Article 10150
• Main Authors: Gontarz, Paul, Fu, Shuhua, Xing, Xiaoyun, Liu, Shaopeng, Miao, Benpeng, Bazylianska, Viktoriia, Sharma, Akhil, Madden, Pamela, Cates, Kitra, Yoo, Andrew, Moszczynska, Anna, Wang, Ting, Zhang, Bo
• Format: Article
• Language: English
• Subjects: 45; 45/23; 631/114/794; 631/1647/48; Chromatin; Chromatin - genetics; Chromatin Immunoprecipitation Sequencing - methods; Databases, Nucleic Acid; Datasets as Topic; Gene Regulatory Networks - genetics; Genomes; High-Throughput Nucleotide Sequencing; Humanities and Social Sciences; Humans; multidisciplinary; Next-generation sequencing; Regulatory sequences; Ribonucleic acid; RNA; Science; Science (multidisciplinary); Sensitivity and Specificity
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-020-66998-4

ATAC-seq is widely used to measure chromatin accessibility and identify open chromatin regions (OCRs). OCRs usually indicate active regulatory elements in the genome and are directly associated with the gene regulatory network. The identification of differential accessibility regions (DARs) between different biological conditions is critical in determining the differential activity of regulatory elements. Differential analysis of ATAC-seq shares many similarities with differential expression analysis of RNA-seq data. However, the distribution of ATAC-seq signal intensity is different from that of RNA-seq data, and higher sensitivity is required 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 lacking. Here, we used simulated datasets to systematically measure the sensitivity and specificity of six different methods. We further discussed the statistical and signal density cut-offs in the differential analysis of ATAC-seq by applying them to real data. Batch effects are very common in high-throughput sequencing experiments. We illustrated that batch-effect correction can dramatically improve sensitivity in the differential analysis of ATAC-seq data. Finally, we developed a user-friendly package, BeCorrect, to perform batch effect correction and visualization of corrected ATAC-seq signals in a genome browser.