ATAC-seq analysis methods are similar to those used for CUT&RUN and CUT&Tag datasets. Briefly:
ATAC-seq typically produces sub-nucleosomal-sized fragments from open chromatin regions. Trimming sequencing adapters prior to alignment is highly recommended, because any genomic fragment shorter than the sequencing read length will contain adapter sequence and fail to map to the genome. At 2x50 bp this may represent only ~5% of sequencing reads, but at 2x100 bp it can account for as much as 25% of the read depth. Tools such as Trimmomatic and Trim Galore are recommended for this purpose.
Align raw reads to a reference genome using Bowtie2 [1]. The Integrative Genomics Viewer (IGV) [2] and/or deepTools2 [3] can be used to visualize enrichment (e.g. bigWig files graphed over a genome browser)
For peak calling, EpiCypher frequently uses MACS2 (Narrow) [4], which can be adjusted for analysis of sharp enrichment peaks as suited for open chromatin profiles in ATAC-seq [5].
To determine signal over background, EpiCypher uses bedTools to calculate fractions of reads in peaks (FRiP) and compare FRiP scores from experimental samples vs. controls [6]. Other tools can be applied for differential analysis and heatmap generation (e.g. DESeq2 [7], deepTools2 [3]).
For more tips on ATAC-seq data analysis, follow the ENCODE guidelines here.
CUTANA Cloud
CUTANA Cloud ATAC-seq data analysis pipeline is coming soon! Check back on our website for updates or reach out to our tech support team (techsupport@epicypher.com) to get notified when it’s live.
References
Langmead & Salzberg. Fast gapped-read alignment with Bowtie 2. Nat Methods 9, 357-359 (2012).
Robinson et al. Integrative Genomics Viewer. Nat Biotechnol 29, 24–26 (2011).
Ramírez et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res 8, 44 (2016).
Liu T. Use model-based Analysis of ChIP-Seq (MACS) to analyze short reads generated by sequencing protein-DNA interactions in embryonic stem cells. Methods Mol Biol 1150, 81-95 (2014).
Laczik M et al. Iterative Fragmentation Improves the Detection of ChIP-seq Peaks for Inactive Histone Marks. Bioinform Biol Insights 10, 209-224 (2016).
Schep AN et al. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat Methods 14, 975-978 (2017).
Love MI et al. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15, 550 (2014).