# Manuscript data availabiliy

## Datasets

Two datasets were used to produce the figure in the paper:

1. snmC-seq2 scWGBS data (Luo et al. 2018) for single-cell benchmarks.
2. bulk WGBS from a project in our lab

Both datasets were aligned with BISCUIT (Zhou et al. 2024). To produce Bismark BEDgraph files (Krueger and Andrews 2011), we used a python script to convert the beta and coverage value columns to percentage methylation, unmethylated reads and methylated read columns.

    import sys
    import gzip

    bedfile = sys.argv[1]

    def bed2cov(line):
        chr, start, end, beta, cov = line.split('\t')
        percent, meth, unmeth = convert(float(beta), int(cov))
        return '\t'.join([chr, start, start, str(percent), str(meth), str(unmeth)])

    def convert(beta, cov):
        percent = round(beta * 100)
        meth = round(beta * cov)
        unmeth = cov - meth
        return [percent, meth, unmeth]

    with gzip.open(bedfile, 'rt') as bed:
        for line in bed:
            print(bed2cov(line))

Using GNU parallel:

    parallel python bed2cov.py <biscuit_path>/{} '>' <bismark_path>/{/.} ::: biscuit_path/*.bed.gz

Then using the `iscream.paper` package at https://github.com/huishenlab/iscream.paper we ran the benchmarks and produced the figures.

## References

Krueger, Felix, and Simon R. Andrews. 2011. “Bismark: A Flexible Aligner and Methylation Caller for Bisulfite-Seq Applications.” Bioinformatics 27 (11): 1571–72. https://doi.org/10.1093/bioinformatics/btr167.
Luo, Chongyuan, Angeline Rivkin, Jingtian Zhou, et al. 2018. “Robust Single-Cell DNA Methylome Profiling with snmC-seq2.” Nat Commun 9 (1): 3824. https://doi.org/10.1038/s41467-018-06355-2.
Zhou, Wanding, Benjamin K Johnson, Jacob Morrison, et al. 2024. “BISCUIT: An Efficient, Standards-Compliant Tool Suite for Simultaneous Genetic and Epigenetic Inference in Bulk and Single-Cell Studies.” Nucleic Acids Research 52 (6): gkae097. https://doi.org/10.1093/nar/gkae097.