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Comparison of differential accessibility analysis strategies for ATAC-seq data
Paul Gontarz1, Shuhua Fu1, Xiaoyun Xing2
1Department of Developmental Biology, Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA.
Scientific Reports
|June 25, 2020
Summary
This study benchmarks tools for identifying differential chromatin accessibility regions (DARs) from ATAC-seq data. Batch effect correction significantly improves DAR identification sensitivity, leading to the development of the BeCorrect package.
Area of Science:
- Genomics
- Epigenetics
- Bioinformatics
Background:
- Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) identifies open chromatin regions (OCRs), crucial for understanding gene regulation.
- Differential analysis of ATAC-seq data is key to identifying regulatory element activity changes between conditions.
- Existing tools for differential accessibility regions (DARs) identification lack comprehensive benchmarking.
Purpose of the Study:
- To systematically compare the sensitivity and specificity of six different DAR identification methods using simulated ATAC-seq data.
- To investigate the impact of statistical and signal density cut-offs on DAR analysis.
- To evaluate the effectiveness of batch-effect correction in improving DAR identification and introduce a user-friendly package for this purpose.
Main Methods:
- Utilized simulated ATAC-seq datasets to benchmark six differential analysis tools.
- Applied methods to real ATAC-seq data to assess statistical and signal density cut-offs.
- Evaluated the impact of batch-effect correction on DAR identification sensitivity.
- Developed the BeCorrect R package for batch-effect correction and visualization.
Main Results:
- Systematic benchmarking revealed performance variations among six tested DAR identification methods.
- Analysis highlighted the importance of appropriate statistical and signal density cut-offs for accurate DAR identification.
- Batch-effect correction demonstrated a significant improvement in the sensitivity of DAR detection.
- The BeCorrect package provides a streamlined approach for correcting batch effects and visualizing results.
Conclusions:
- No single tool universally outperforms others for DAR identification; method selection depends on specific experimental contexts.
- Careful consideration of statistical thresholds and signal density is crucial for robust DAR analysis.
- Batch effects can substantially hinder ATAC-seq differential analysis, and correction is essential for maximizing sensitivity.
- The BeCorrect package offers a valuable resource for improving the reliability and interpretability of ATAC-seq differential accessibility studies.
