Profiling the epigenome at home

Steven Henikoff; Jorja G. Henikoff

• We align paired-end reads to hg19 using Bowtie2 version 2.3.4.3 with options: -end-to-end --very-sensitive --no-unal --no-mixed --no-discordant --phred33 -I 10 -X 700. For mapping E. coli carry-over fragments, we also use the --no-overlap -no-dovetail options to avoid possible cross-mapping of the experimental genome to that of the carry-over E. coli DNA that is used for calibration. • Tracks are made as bedgraph files of normalized counts, which are the fraction of total counts at each basepair scaled by the size of the hg19 genome. • To calibrate samples in a series for samples done in parallel using the same antibody we use counts of E. coli fragments carried over with the pA-Tn5 the same as one would for an ordinary spike-in. Our sample script (https://github.com/Henikoff/Cut-and-Run/blob/master/spike_in_calibration.csh) can be used to calibrate based on either a spike-in or E. coli carry-over DNA. • Most data analysis tools used for ChIP-seq data, such as bedtools (https://bedtools.readthedocs.io/en/latest/), Picard (https://broadinstitute.github.io/picard/) and deepTools (https://deeptools.readthedocs.io/en/develop/), can be used on CUT&Tag data. • Analysis tools designed specifically for CUT&RUN/Tag data include the SEACR peak caller (Meers et al., 2019) also available as a public web server (https://seacr.fredhutch.org), CUT&RUNTools (Zhu et al., 2019) and henipipe (https://github.com/scfurl/henipipe).