Haibo Liu, Department of Animal Science, Iowa State University, Ames, IA, 50010, USA. [email protected]
Jianhong Ou, Department of Cell Biology, Duke University Medical Center, Durham, NC, 27710, USA. [email protected]
Rui Li, Department of Molecular, Cell and Cancer Biology, Worcester, MA01655, USA. [email protected]
Kai Hu, Department of Molecular, Cell and Cancer Biology, Worcester, MA01655, USA. [email protected]
Corresponding author: Lihua Julie Zhu, Department of Molecular, Cell and Cancer Biology, Program in Molecular Medicine, Program in Bioinformatics and Integrative Biology, Worcester, MA01655, USA. [email protected]
*Denotes workshop presenter

In this workshop, we will provide a valuable introduction to the current best practices for ATAC-seq assays, high quality data generation and computational analysis workflow. Then, we will walk the participants through the analysis of an ATAC-seq data set. Single cell ATAC-seq data analysis will be briefly covered at the end by comparing to the bulk ATAC-seq data analysis. Detailed tutorials including R scripts will be provided for reproducibility and follow-up exploration.

Expectation: After this workshop, participants should be able to apply the learned skills to analyzing their own ATAC-seq data, provide constructive feedback to experimenters who expect to generate high-quality ATAC-seq data, and identify ATAC-seq data of reliable quality for further analysis.

Participants are expected to have basic knowledge as follows:

• Basic knowledge of R syntax
• Basic knowledge of simple UNIX commands, such as grep, and awk
• Some familiarity with the GenomicRanges, BSgenome, GenomicAlignments classes
• Familiarity with the SAM file format (https://samtools.github.io/hts-specs/SAMv1.pdf)

Basic understanding on how ATAC-seq data are generated is helpful but not required. Please refer to the following reference for detailed information about the ATAC-seq technology.

Jason Buenrostro, Beijing Wu, Howard Chang, William Greenleaf. ATAC-seq: A Method for Assaying Chromatin Accessibility Genome-Wide. Curr Protoc Mol Biol. 2015; 109: 21.29.1–21.29.9. doi:10.1002/0471142727.mb2129s109.

Please refer to the following resource to preprocess the ATAC-seq data prior to performing quality assessment using the ATACseqQC package.

The Additional File 1 from our publication (Ou et al., 2018; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5831847/)

Participants are expected to have basic knowledge about R and several R packages as described above in advance. To follow along the hands-on session, we recommend participants bring your own laptop. We will post a Docker image with required packages and data pre-installed for you to download and run the analysis within a Docker container. If you will use the Docker image, please get Docker installed (https://www.docker.com/get-started) in advance. For participants who wish to install all packages by themselves, you will also need to install the following computing tools.

• R (version >3.5.0; https://cran.r-project.org/)
• RStudio (https://www.rstudio.com/products/rstudio/download/#download)
• The ATACseqQC package along its dependencies
• Integrated Genome Browser (IGV, http://software.broadinstitute.org/software/igv/download)

For participants who wish to install all packages by themselves and use a Windows with OS lower than Win 10, which lacks a UNIX terminal, we recommend one of the following alternatives.

• Install Cygwin (https://www.cygwin.com/)
• Install git Bash (https://gitforwindows.org/)
• Install UnxUtils (https://sourceforge.net/projects/unxutils/)

The following R/Bioconductor packages will be explicitly used:

• library(ATACseqQC)
• library(ChIPpeakAnno)
• library(BSgenome.Hsapiens.UCSC.hg38)
• library(TxDb.Hsapiens.UCSC.hg38.knownGene)
• library(MotifDb)
• library(motifStack)
• library(GenomicAlignments)

• Understand how ATAC-seq data are generated
• Learn how to perform comprehensive quality control of ATAC-seq data
• Identify high quality ATAC-seq data for downstream analysis
• Identify most likely reasons for ATAC-seq data failing QC
• ATAC-seq data analysis workflow

• Analyze a pre-aligned, excerpted ATAC-seq dataset from the original ATAC-seq publication (Buenrostro et al., 2015) to produce comprehensive insights into the quality of the data
• Create a plot showing library fragment size distribution
• Create overview plots showing signal distribution around transcription start sites
• Automatically generate IGV snapshots showing signal distribution along multiple housing keeping genes (positive control genes)
• Create a plot showing CTCF footprints
• Evaluate the ATAC-seq data for mitochondrial DNA contamination, duplication rate, background noise level, library complexity and Tn5 transposition optimality