Differential ATAC-seq and ChIP-seq peak detection using ROTS. (2nd July 2021)

• Record Type: Journal Article
• Authors: Faux, Thomas; Rytkönen, Kalle T; Mahmoudian, Mehrad; Paulin, Niklas; Junttila, Sini; Laiho, Asta; Elo, Laura L
• Is Part Of: NAR genomics and bioinformatics. Volume 3:issue 3(2021)
• Publication Date: 2021-07-02
• Subjects: Genomics -- Periodicals; Bioinformatics -- Periodicals; 572.8
• DOI: 10.1093/nargab/lqab059
• Languages: English
• ISSNs: 2631-9268

Abstract

Abstract: Changes in cellular chromatin states fine-tune transcriptional output and ultimately lead to phenotypic changes. Here we propose a novel application of our reproducibility-optimized test statistics (ROTS) to detect differential chromatin states (ATAC-seq) or differential chromatin modification states (ChIP-seq) between conditions. We compare the performance of ROTS to existing and widely used methods for ATAC-seq and ChIP-seq data using both synthetic and real datasets. Our results show that ROTS outperformed other commonly used methods when analyzing ATAC-seq data. ROTS also displayed the most accurate detection of small differences when modeling with synthetic data. We observed that two-step methods that require the use of a separate peak caller often more accurately called enrichment borders, whereas one-step methods without a separate peak calling step were more versatile in calling sub-peaks. The top ranked differential regions detected by the methods had marked correlation with transcriptional differences of the closest genes. Overall, our study provides evidence that ROTS is a useful addition to the available differential peak detection methods to study chromatin and performs especially well when applied to study differential chromatin states in ATAC-seq data.