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Mapping Genome-wide Accessible Chromatin in Primary Human T Lymphocytes by ATAC-Seq
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ATAC-seq normalization method can significantly affect differential accessibility analysis and interpretation.

Jake J Reske1, Mike R Wilson1, Ronald L Chandler2,3

  • 1Department of Obstetrics, Gynecology and Reproductive Biology, College of Human Medicine, Michigan State University, Grand Rapids, MI, 49503, USA.

Epigenetics & Chromatin
|April 24, 2020
PubMed
Summary

Normalization method choice significantly impacts Assay for Transposase-Accessible Chromatin (ATAC-seq) analysis results. Researchers must compare methods to ensure accurate differential chromatin accessibility findings.

Keywords:
ATAC-seqBioinformaticsChromatin accessibilityDifferential accessibilityGenomicsNormalization

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Area of Science:

  • Genomics
  • Molecular Biology
  • Epigenetics

Background:

  • Chromatin dysregulation is linked to developmental disorders and cancer.
  • Genome-wide chromatin accessibility assays, like ATAC-seq, are crucial for studying chromatin regulators.
  • Differential ATAC-seq analysis can produce conflicting results, with normalization methods often overlooked.

Purpose of the Study:

  • To investigate the impact of normalization methods on differential ATAC-seq analysis.
  • To propose a generalized workflow for robust differential accessibility analysis.
  • To highlight the importance of normalization method selection in ATAC-seq studies.

Main Methods:

  • Utilized an in vivo ATAC-seq dataset and published yeast ATAC-seq data.
  • Developed and applied a generalized workflow for differential accessibility analysis.
  • Correlated differential accessibility sites with gene expression data and used negative controls.

Main Results:

  • Observed variations in chromatin accessibility patterns based on the normalization method used.
  • Verified normalization method impact on both in vivo and published ATAC-seq data.
  • Demonstrated that the proposed workflow identifies differential chromatin accessibility sites linked to gene expression.

Conclusions:

  • Systematic comparison of multiple normalization methods is essential before differential ATAC-seq analysis.
  • Researchers must understand potential biases and assumptions associated with chosen normalization methods.
  • Awareness of normalization method implications is critical for accurate interpretation of ATAC-seq experimental data.