RT Journal Article
SR Electronic
T1 Normalization benchmark of ATAC-seq datasets shows the importance of accounting for GC-content effects
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.01.26.428252
DO 10.1101/2021.01.26.428252
A1 Koen Van den Berge
A1 Hsin-Jung Chou
A1 Hector Roux de Bézieux
A1 Kelly Street
A1 Davide Risso
A1 John Ngai
A1 Sandrine Dudoit
YR 2021
UL http://biorxiv.org/content/early/2021/05/20/2021.01.26.428252.abstract
AB Modern assays have enabled high-throughput studies of epigenetic regulation of gene expression using DNA sequencing. In particular, the assay for transposase-accessible chromatin using sequencing (ATAC-seq) allows the study of chromatin configuration for an entire genome. Despite the gain in popularity of the assay, there have been limited studies investigating the analytical challenges related to ATAC-seq data, and most studies leverage tools developed for bulk transcriptome sequencing (RNA-seq). Here, we show that GC-content effects are omnipresent in ATAC-seq datasets. Since the GC-content effects are sample-specific, they can bias downstream analyses such as clustering and differential accessibility analysis. We introduce a normalization method based on smooth-quantile normalization within GC-content bins, and evaluate it together with eleven different normalization procedures on eight public ATAC-seq datasets. Our work clearly shows that accounting for GC-content effects in the normalization is crucial for common downstream ATAC-seq data analyses, leading to improved accuracy and interpretability of the results. Using two case studies, we show that exploratory data analysis is essential to guide the choice of an appropriate normalization method for a given dataset.Competing Interest StatementThe authors have declared no competing interest.