Downstream ATAC-seq analysis

Nathan Sheffield, PhD

Common types of downstream ATAC analysis

  • Determining differential chromatin accessibility
  • Clustering elements that share accessibility patterns
  • Footprinting
  • Region of Interest investigation

Differential ATAC-seq analysis

How to create the count matrix? 1) Consensus peaks; 2) tiles.

Consensus peaks

Genome tiles

Hybrid: Smart tiles

Statistical tests

Use count-based statistical tools for RNA-seq: DEseq or edgeR.

Clustering ATAC-seq data

Chromatin accessibility across cell types

These still require 'common reference peaks'...

  • JAMM: a peak finder for joint analysis of NGS replicates
    (Ibrahim et al. 2015)
  • HMMRATAC: a Hidden Markov ModeleR for ATAC-seq
    (Tarbell and Liu 2019)
  • It is possible to extend HMMRATAC to identify differentially accessible regions between two or more conditions... (Tarbell and Liu 2019)


    Footprinting concept

    Vernon et al. 2012
    Caveats: 1) Depth; 2) Sequence bias; 3) Factor dynamics
    # Footprinting controversies: Sequence bias - [Yardimci et al. 2014]( - Explicit DNase sequence bias modeling enables high-resolution transcription factor footprint detection - [Madrigal et al. 2015]( - On accounting for sequence-specific bias in genome-wide chromatin accessibility experiments: recent advances and contradictions - [Wang et al. 2017]( - Correcting Nucleotide-Specific Biases in High-Throughput Sequencing Data - [Martins et al. 2018]( - Universal Correction of Enzymatic Sequence Bias Reveals Molecular Signatures of protein/DNA Interactions - [Calviello et al. 2019]( - Reproducible inference of transcription factor footprints in ATAC-seq and DNase-seq datasets using protocol-specific bias modeling

    Bias correction

    Martins et al. 2018
    # Footprinting controversies: Factor dynamics - [Sung et al. 2014](http://10.1016/j.molcel.2014.08.016) - DNase footprint signatures are dictated by factor dynamics and DNA sequence. - [Sung et al. 2016]( - Genome-wide footprinting: ready for prime time? - [Oh et al. 2019](http://10.1186/s13072-019-0277-6) - XL-DNase-seq: improved footprinting of dynamic transcription factors

    Factor dynamics

    Sung et al. 2014
    # ATAC footprinting [Li et al. 2019]( - Identification of transcription factor binding sites using ATAC-seq HINT-ATAC, which is based on hidden Markov models, uses strand-specific, nucleosome-size decomposed, and bias-corrected signals to identify footprints. [Ouyang and Boyle 2020]( TRACE: transcription factor footprinting using chromatin accessibility data and DNA sequence ![](/images/tweets/2020-04-18-boyle.png)

    Single-cell ATAC

    Buenrostro et al. 2015
    Xiong et al. 2019 - SCALE method for single-cell ATAC-seq analysis via latent feature extraction
    # Take-home guidelines - Be wary of consensus peaks as the size/diversity in your data increases. - Count-based statistical approaches for other methods are applicable - For footprinting, make sure you understand the caveats - Expect lots of new developments in the near future
    # Investigating regions of interest ROI -> insight - Top hits - Distribution across chromosomes - Distribution of annotated features (genes, enhancers, etc) - Motif analysis - Enrichment analysis
    # Enrichment analysis for regions of interest - GREAT: assigns biological meaning to a set of non-coding genomic regions by analyzing the annotations of the nearby genes. - BART: predicting functional factors (including transcription factors and chromatin regulators) that bind at cis-regulatory regions - LOLA: enrichment of overlap between a user-provided query region set (a bed file) and a database of region sets.
    [Awesome list of ATAC-seq analysis tools](

    Thank You

    nsheff · ·