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Quantification, Dynamic Visualization, and Validation of Bias in ATAC-Seq Data with ataqv.
Peter Orchard1, Yasuhiro Kyono2, John Hensley1
1Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
Cell Systems
|March 28, 2020
Summary
We developed ataqv, a toolkit for assessing the quality of transposase-accessible chromatin sequencing (ATAC-seq) data. This tool helps identify technical biases in ATAC-seq experiments, improving data reliability.
Area of Science:
- Genomics
- Molecular Biology
- Bioinformatics
Background:
- Transposase-accessible chromatin using sequencing (ATAC-seq) is a widely used method for mapping chromatin accessibility.
- Evaluating ATAC-seq data quality and identifying technical biases across samples can be challenging.
Purpose of the Study:
- To present ataqv, a computational toolkit for quality control (QC) of ATAC-seq data.
- To enable efficient measurement, visualization, and comparison of QC metrics across samples and experiments.
Main Methods:
- Development of the ataqv computational toolkit.
- Analysis of 2,009 public ATAC-seq datasets using ataqv.
- Tn5 dosage experiments and statistical modeling to identify sources of bias.
Main Results:
- atqav effectively measures and visualizes ATAC-seq QC metrics, revealing a 10-fold range in quality across datasets.
- Technical variations in Tn5 transposase to nuclei ratio and sequencing flowcell density introduce systematic bias in ATAC-seq data.
- Bias affects enrichment across genomic regions like promoters and enhancers, but not CTCF-bound regions.
Conclusions:
- atqav is a valuable tool for assessing ATAC-seq data quality and identifying technical biases.
- Understanding and mitigating Tn5 dosage and flowcell density biases is crucial for reliable ATAC-seq results.
- atqav can be integrated into existing pipelines to enhance ATAC-seq data analysis.
