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Improving ATAC-seq Data Analysis with AIAP, a Quality Control and Integrative Analysis Package

Shaopeng Liu, Daofeng Li, Cheng Lyu, Paul Gontarz, Benpeng Miao, Pamela Madden, Ting Wang, Bo Zhang
doi: https://doi.org/10.1101/686808
Shaopeng Liu
1Department of Developmental Biology, Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, 63108, USA
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Daofeng Li
2Department of Genetics, Center for Genomic Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, 63108, USA
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Cheng Lyu
1Department of Developmental Biology, Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, 63108, USA
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Paul Gontarz
1Department of Developmental Biology, Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, 63108, USA
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Benpeng Miao
1Department of Developmental Biology, Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, 63108, USA
2Department of Genetics, Center for Genomic Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, 63108, USA
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Pamela Madden
3Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63108, USA
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Ting Wang
2Department of Genetics, Center for Genomic Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, 63108, USA
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  • For correspondence: twang@wustl.edu bzhang29@wustl.edu
Bo Zhang
1Department of Developmental Biology, Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, 63108, USA
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  • For correspondence: twang@wustl.edu bzhang29@wustl.edu
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ABSTRACT

ATAC-seq is a technique widely used to investigate genome-wide chromatin accessibility. The recently published Omni-ATAC-seq protocol substantially improves the signal/noise ratio and reduces the input cell number. High-quality data are critical to ensure accurate analysis. Several tools have been developed for assessing sequencing quality and insertion size distribution for ATAC-seq data; however, key quality control (QC) metrics have not yet been established to accurately determine the quality of ATAC-seq data. Here, we optimized the analysis strategy for ATAC-seq and defined a series of QC metrics, including reads under peak ratio (RUPr), background (BG), promoter enrichment (ProEn), subsampling enrichment (SubEn), and other measurements. We incorporated these QC tests into our recently developed ATAC-seq Integrative Analysis Package (AIAP) to provide a complete ATAC-seq analysis system, including quality assurance, improved peak calling, and downstream differential analysis. We demonstrated a significant improvement of sensitivity (20%~60%) in both peak calling and differential analysis by processing paired-end ATAC-seq datasets using AIAP. AIAP is compiled into Docker/Singularity, and with one command line execution, it generates a comprehensive QC report. We used ENCODE ATAC-seq data to benchmark and generate QC recommendations, and developed qATACViewer for the user-friendly interaction with the QC report.

Footnotes

  • ↵$ The authors wish it to be known that, in their opinion, the first three authors should be regarded as joint First Authors

  • https://github.com/Zhang-lab/ATAC-seq_QC_analysis

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted June 28, 2019.
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Improving ATAC-seq Data Analysis with AIAP, a Quality Control and Integrative Analysis Package
Shaopeng Liu, Daofeng Li, Cheng Lyu, Paul Gontarz, Benpeng Miao, Pamela Madden, Ting Wang, Bo Zhang
bioRxiv 686808; doi: https://doi.org/10.1101/686808
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Improving ATAC-seq Data Analysis with AIAP, a Quality Control and Integrative Analysis Package
Shaopeng Liu, Daofeng Li, Cheng Lyu, Paul Gontarz, Benpeng Miao, Pamela Madden, Ting Wang, Bo Zhang
bioRxiv 686808; doi: https://doi.org/10.1101/686808

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