Best Practices for Pre-Processing Large-Scale ATAC-Seq Data: Why Epidemiological Studies Demand Greater Stringency
- 1University of Exeter
Abstract
Assay for transposase accessible chromatin using sequencing (ATAC-seq) and related techniques provide critical insight into the epigenetic mechanisms that regulate gene expression in health and disease. Accurate identification of biologically meaningful chromatin accessibility signals remains an underexplored analytical challenge. Advances in sequencing technologies have increased both the magnitude and complexity of ATAC-seq datasets, leading to demand for scalable, reproducible and robust computational analysis frameworks. Peak calling, which identifies regions of chromatin accessibility, is a critical step that underpins all downstream quantitative analyses. However, there is a lack of consensus regarding the optimal peak calling strategy. Here, we present a novel bioinformatics pipeline PIPPA (Pipeline for Integrative Peak calling in Population-scale ATAC-seq), for quality control and peak set curation of large-scale ATAC-seq datasets. Using two independent cell-type sorted ATAC-seq datasets, we show that peak calling performance is dataset-dependent and that no single significance threshold performs optimally across all scenarios. We provide practical recommendations for parameters to generate high-confidence chromatin accessibility profiles. Our peak calling approach is applicable for other genome wide epigenomic assays such as ChIP-seq and CUT&Tag. By standardising this process, the PIPPA pipeline enhances epidemiological studies focused in identifying epigenetic dysregulation associated with development and disease.
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BibTeX
@misc{wells2026best,
title = {{Best Practices for Pre-Processing Large-Scale ATAC-Seq Data: Why Epidemiological Studies Demand Greater Stringency}},
author = {P. M. Wells and M. Flores-Payan and B. Chioza and J. Burrage and A. Babtie and S. J. Marzi and L. C. Schalkwyk and YouthGEMS Consortium and E. Dempster and J. Mill and E. J. Hannon},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.22.760939},
url = {https://www.biorxiv.org/content/10.64898/2026.10.22.760939v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Best Practices for Pre-Processing Large-Scale ATAC-Seq Data: Why Epidemiological Studies Demand Greater Stringency
AU - P. M. Wells
AU - M. Flores-Payan
AU - B. Chioza
AU - J. Burrage
AU - A. Babtie
AU - S. J. Marzi
AU - L. C. Schalkwyk
AU - YouthGEMS Consortium
AU - E. Dempster
AU - J. Mill
AU - E. J. Hannon
PY - 2026
DA - 2026/10/22
PB - bioRxiv
DO - 10.64898/2026.10.22.760939
UR - https://www.biorxiv.org/content/10.64898/2026.10.22.760939v1
AB - Assay for transposase accessible chromatin using sequencing (ATAC-seq) and related techniques provide critical insight into the epigenetic mechanisms that regulate gene expression in health and disease. Accurate identification of biologically meaningful chromatin accessibility signals remains an underexplored analytical challenge. Advances in sequencing technologies have increased both the magnitude and complexity of ATAC-seq datasets, leading to demand for scalable, reproducible and robust computational analysis frameworks. Peak calling, which identifies regions of chromatin accessibility, is a critical step that underpins all downstream quantitative analyses. However, there is a lack of consensus regarding the optimal peak calling strategy. Here, we present a novel bioinformatics pipeline PIPPA (Pipeline for Integrative Peak calling in Population-scale ATAC-seq), for quality control and peak set curation of large-scale ATAC-seq datasets. Using two independent cell-type sorted ATAC-seq datasets, we show that peak calling performance is dataset-dependent and that no single significance threshold performs optimally across all scenarios. We provide practical recommendations for parameters to generate high-confidence chromatin accessibility profiles. Our peak calling approach is applicable for other genome wide epigenomic assays such as ChIP-seq and CUT&Tag. By standardising this process, the PIPPA pipeline enhances epidemiological studies focused in identifying epigenetic dysregulation associated with development and disease.
ER -