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AIAP: A Quality Control and Integrative Analysis Package to Improve ATAC-seq Data Analysis
Assay for transposase-accessible chromatin with high-throughput sequencing (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-qua...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9040017/ https://www.ncbi.nlm.nih.gov/pubmed/34273560 http://dx.doi.org/10.1016/j.gpb.2020.06.025 |
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author | Liu, Shaopeng Li, Daofeng Lyu, Cheng Gontarz, Paul M. Miao, Benpeng Madden, Pamela A.F. Wang, Ting Zhang, Bo |
author_facet | Liu, Shaopeng Li, Daofeng Lyu, Cheng Gontarz, Paul M. Miao, Benpeng Madden, Pamela A.F. Wang, Ting Zhang, Bo |
author_sort | Liu, Shaopeng |
collection | PubMed |
description | Assay for transposase-accessible chromatin with high-throughput sequencing (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 for ATAC-seq data, 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 it can be executed by one command line to generate 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. The software, source code, and documentation of AIAP are freely available at https://github.com/Zhang-lab/ATAC-seq_QC_analysis. |
format | Online Article Text |
id | pubmed-9040017 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-90400172022-04-27 AIAP: A Quality Control and Integrative Analysis Package to Improve ATAC-seq Data Analysis Liu, Shaopeng Li, Daofeng Lyu, Cheng Gontarz, Paul M. Miao, Benpeng Madden, Pamela A.F. Wang, Ting Zhang, Bo Genomics Proteomics Bioinformatics Application Note Assay for transposase-accessible chromatin with high-throughput sequencing (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 for ATAC-seq data, 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 it can be executed by one command line to generate 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. The software, source code, and documentation of AIAP are freely available at https://github.com/Zhang-lab/ATAC-seq_QC_analysis. Elsevier 2021-08 2021-07-15 /pmc/articles/PMC9040017/ /pubmed/34273560 http://dx.doi.org/10.1016/j.gpb.2020.06.025 Text en © 2021 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Application Note Liu, Shaopeng Li, Daofeng Lyu, Cheng Gontarz, Paul M. Miao, Benpeng Madden, Pamela A.F. Wang, Ting Zhang, Bo AIAP: A Quality Control and Integrative Analysis Package to Improve ATAC-seq Data Analysis |
title | AIAP: A Quality Control and Integrative Analysis Package to Improve ATAC-seq Data Analysis |
title_full | AIAP: A Quality Control and Integrative Analysis Package to Improve ATAC-seq Data Analysis |
title_fullStr | AIAP: A Quality Control and Integrative Analysis Package to Improve ATAC-seq Data Analysis |
title_full_unstemmed | AIAP: A Quality Control and Integrative Analysis Package to Improve ATAC-seq Data Analysis |
title_short | AIAP: A Quality Control and Integrative Analysis Package to Improve ATAC-seq Data Analysis |
title_sort | aiap: a quality control and integrative analysis package to improve atac-seq data analysis |
topic | Application Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9040017/ https://www.ncbi.nlm.nih.gov/pubmed/34273560 http://dx.doi.org/10.1016/j.gpb.2020.06.025 |
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