For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
19
views
100
references
 Top references
cited by
152
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      388
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      From reads to insight: a hitchhiker’s guide to ATAC-seq data analysis

      review-article
      Author(s): 1 , 2 , 1 , 3 , 1 , 2 ,
      Publication date (Electronic):
      Journal: Genome Biology
      Publisher: BioMed Central

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Assay of Transposase Accessible Chromatin sequencing (ATAC-seq) is widely used in studying chromatin biology, but a comprehensive review of the analysis tools has not been completed yet. Here, we discuss the major steps in ATAC-seq data analysis, including pre-analysis (quality check and alignment), core analysis (peak calling), and advanced analysis (peak differential analysis and annotation, motif enrichment, footprinting, and nucleosome position analysis). We also review the reconstruction of transcriptional regulatory networks with multiomics data and highlight the current challenges of each step. Finally, we describe the potential of single-cell ATAC-seq and highlight the necessity of developing ATAC-seq specific analysis tools to obtain biologically meaningful insights.

          Electronic supplementary material

          Supplementary information accompanies this paper at 10.1186/s13059-020-1929-3.

          Related collections

          Most cited references100

          • Record: found
          • Abstract: found
          • Article: not found

          High-resolution mapping and characterization of open chromatin across the genome.

          Alan Boyle, Sean Davis, Hennady Shulha … (2008)
          Mapping DNase I hypersensitive (HS) sites is an accurate method of identifying the location of genetic regulatory elements, including promoters, enhancers, silencers, insulators, and locus control regions. We employed high-throughput sequencing and whole-genome tiled array strategies to identify DNase I HS sites within human primary CD4+ T cells. Combining these two technologies, we have created a comprehensive and accurate genome-wide open chromatin map. Surprisingly, only 16%-21% of the identified 94,925 DNase I HS sites are found in promoters or first exons of known genes, but nearly half of the most open sites are in these regions. In conjunction with expression, motif, and chromatin immunoprecipitation data, we find evidence of cell-type-specific characteristics, including the ability to identify transcription start sites and locations of different chromatin marks utilized in these cells. In addition, and unexpectedly, our analyses have uncovered detailed features of nucleosome structure.
          Article 0 comments Cited 561 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            A clustering approach for identification of enriched domains from histone modification ChIP-Seq data.

            Chongzhi Zang, Dustin E. Schones, Chen Zeng … (2009)
            Chromatin states are the key to gene regulation and cell identity. Chromatin immunoprecipitation (ChIP) coupled with high-throughput sequencing (ChIP-Seq) is increasingly being used to map epigenetic states across genomes of diverse species. Chromatin modification profiles are frequently noisy and diffuse, spanning regions ranging from several nucleosomes to large domains of multiple genes. Much of the early work on the identification of ChIP-enriched regions for ChIP-Seq data has focused on identifying localized regions, such as transcription factor binding sites. Bioinformatic tools to identify diffuse domains of ChIP-enriched regions have been lacking. Based on the biological observation that histone modifications tend to cluster to form domains, we present a method that identifies spatial clusters of signals unlikely to appear by chance. This method pools together enrichment information from neighboring nucleosomes to increase sensitivity and specificity. By using genomic-scale analysis, as well as the examination of loci with validated epigenetic states, we demonstrate that this method outperforms existing methods in the identification of ChIP-enriched signals for histone modification profiles. We demonstrate the application of this unbiased method in important issues in ChIP-Seq data analysis, such as data normalization for quantitative comparison of levels of epigenetic modifications across cell types and growth conditions. http://home.gwu.edu/ approximately wpeng/Software.htm. Supplementary data are available at Bioinformatics online.
            Article 0 comments Cited 515 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              The chromatin accessibility landscape of primary human cancers

              M Ryan Corces, Jeffrey M Granja, Shadi Shams … (2018)
              We present the genome-wide chromatin accessibility profiles of 410 tumor samples spanning 23 cancer types from The Cancer Genome Atlas (TCGA). We identify 562,709 transposase-accessible DNA elements that substantially extend the compendium of known cis-regulatory elements. Integration of ATAC-seq (the assay for transposase-accessible chromatin using sequencing) with TCGA multi-omic data identifies a large number of putative distal enhancers that distinguish molecular subtypes of cancers, uncovers specific driving transcription factors via protein-DNA footprints, and nominates long-range gene-regulatory interactions in cancer. These data reveal genetic risk loci of cancer predisposition as active DNA regulatory elements in cancer, identify gene-regulatory interactions underlying cancer immune evasion, and pinpoint noncoding mutations that drive enhancer activation and may affect patient survival. These results suggest a systematic approach to understanding the noncoding genome in cancer to advance diagnosis and therapy.
              Article 0 comments Cited 447 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Contributors
                Nicholas C. Wong:
                ORCID: http://orcid.org/0000-0003-4393-7541
                nick.wong@monash.edu
                Journal
                Journal ID (nlm-ta): Genome Biol
                Journal ID (iso-abbrev): Genome Biol
                Title: Genome Biology
                Publisher: BioMed Central (London )
                ISSN (Print): 1474-7596
                ISSN (Electronic): 1474-760X
                Publication date (Electronic): 3 February 2020
                Publication date PMC-release: 3 February 2020
                Publication date Collection: 2020
                Volume: 21
                Electronic Location Identifier: 22
                Affiliations
                [1 ]ISNI 0000 0004 1936 7857, GRID grid.1002.3, Australian Centre for Blood Diseases, Central Clinical School, , Monash University, ; Melbourne, VIC Australia
                [2 ]ISNI 0000 0004 1936 7857, GRID grid.1002.3, Monash Bioinformatics Platform, , Monash University, ; Melbourne, VIC Australia
                [3 ]ISNI 0000 0004 0432 5259, GRID grid.267362.4, Department of Clinical Haematology, , Alfred Health, ; Melbourne, VIC Australia
                Author information
                http://orcid.org/0000-0003-4393-7541
                Article
                Publisher ID: 1929
                DOI: 10.1186/s13059-020-1929-3
                PMC ID: 6996192
                PubMed ID: 32014034
                SO-VID: e2ab709e-e16f-46a2-b10d-01251909d59e
                Copyright © © The Author(s). 2020
                License:

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                Date received : 22 July 2019
                Date accepted : 8 January 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000991, Faculty of Medicine, Nursing and Health Sciences, Monash University;
                Award ID: MGS/MIPRS
                Award Recipient :
                Categories
                Subject: Review
                Custom metadata
                issue-copyright-statement © The Author(s) 2020

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

                Comments

                Comment on this article

                scite_

                Similar content388

                See all similar

                Cited by151

                See all cited by

                Most referenced authors5,961

                See all reference authors