Normalization benchmark of ATAC-seq datasets shows the importance of accounting for GC-content effects

Koen Van den Berge; Hsin-Jung Chou; Hector Roux de Bézieux; Kelly Street; Davide Risso; John Ngai; Sandrine Dudoit

doi:10.1016/j.crmeth.2022.100321

Normalization benchmark of ATAC-seq datasets shows the importance of accounting for GC-content effects

Cell Rep Methods. 2022 Nov 1;2(11):100321. doi: 10.1016/j.crmeth.2022.100321. eCollection 2022 Nov 21.

Authors

Koen Van den Berge^{1

2

3}, Hsin-Jung Chou⁴, Hector Roux de Bézieux^{5

6}, Kelly Street^{7

8}, Davide Risso⁹, John Ngai^{4

10}, Sandrine Dudoit^{1

5

6}

Affiliations

¹ Department of Statistics, University of California, Berkeley, Berkeley, CA, USA.
² Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.
³ Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium.
⁴ Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA.
⁵ Division of Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, CA, USA.
⁶ Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA.
⁷ Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
⁸ Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
⁹ Department of Statistical Sciences, University of Padova, Padova, Italy.
¹⁰ Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA.

Abstract

The assay for transposase-accessible chromatin using sequencing (ATAC-seq) allows the study of epigenetic regulation of gene expression by assessing chromatin configuration for an entire genome. Despite its popularity, there have been limited studies investigating the analytical challenges related to ATAC-seq data, with most studies leveraging tools developed for bulk transcriptome sequencing. 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 11 different normalization procedures on 8 public ATAC-seq datasets. Accounting for GC-content effects in the normalization is crucial for common downstream ATAC-seq data analyses, improving accuracy and interpretability. Through case studies, we show that exploratory data analysis is essential to guide the choice of an appropriate normalization method for a given dataset.

Keywords: ATAC-seq; GC-content; clustering; differential accessibility; epigenetics; normalization.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Benchmarking*
Chromatin Immunoprecipitation Sequencing*
Epigenesis, Genetic
High-Throughput Nucleotide Sequencing
Sequence Analysis, DNA / methods

Associated data

figshare/10.6084/m9.figshare.c.4436264.v1

Grants and funding

R01 DC007235/DC/NIDCD NIH HHS/United States