{"doi":"10.1016/j.gpb.2020.06.025","title":"AIAP: A Quality Control and Integrative Analysis Package to Improve ATAC-Seq Data Analysis","abstract":"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.","journal":"Genomics Proteomics & Bioinformatics","year":2021,"id":162921,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":44,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9381,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":417526,"name":"Daofeng Li","orcid":"0000-0001-7492-3703","position":1,"is_corresponding":false},{"id":285031,"name":"Cheng Lyu","orcid":"0000-0002-0345-8143","position":2,"is_corresponding":false},{"id":233608,"name":"Paul Gontarz","orcid":"0000-0001-5115-7479","position":3,"is_corresponding":false},{"id":285030,"name":"Benpeng Miao","orcid":"0000-0002-2070-2339","position":4,"is_corresponding":false},{"id":5338,"name":"Pamela A. F. Madden","orcid":"0000-0001-8987-7439","position":5,"is_corresponding":false},{"id":24564,"name":"Ting Wang","orcid":"0000-0002-6800-242X","position":6,"is_corresponding":false},{"id":228314,"name":"Bo Zhang","orcid":"0000-0003-2962-5314","position":7,"is_corresponding":false},{"id":294994,"name":"Shaopeng Liu","orcid":"0000-0003-3112-4068","position":0,"is_corresponding":true}],"reference_count":40,"raw_metadata":null,"created_at":"2026-07-18T23:45:17.971865Z","pmid":"34273560","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}