epic allows for many flags to denote (optional) output files or to change the execution of epic.

  • -t, –treatment

    One or more ChIP files (bed or bedpe format)

  • -c, –control

    One or more input files (bed or bedpe format)

  • -o, –outfile

    File to write results to. By default sent to stdout.

  • -l, –log

    File to write log messages to. Also written to stderr by default.

  • -cpu, –number-cores

    The number of cores epic should use. Can at most take advantage of 1 core per strand per chromosome (i.e. 46 for humans). Default: 1

  • -gn, –genome

    Which genome to analyze. By default hg19.

  • -k, –keep-duplicates

    Keep reads mapping to the same position on the same strand within a library. The default is to remove all but the first duplicate (this is done once per file, not for all files collectively.)

  • -w, –window-size

    Size of the windows (bins) used to scan the genome. This is also the smallest possible enriched region you can get. Default 200.

  • -g, –gaps-allowed

    How many non-enriched windows in a row can be part of the same enriched region. If the number of gaps between two enriched windows is higher than this number, they are considered separate regions. Default: 3

  • -fs, –fragment-size

    (Only used for single-end files) Size of the sequenced fragment. The center of the fragment will be used to calculate which window a read ended up in. So reads are shifted by fragment-size/2. Default 150.

  • -fdr, –false-discovery-rate-cutoff

    Remove all regions with an FDR below cutoff. Note: this also affects which windows are considered enriched in the optional matrix output and which regions are included in the optional bed output.

  • -egf, –effective-genome-fraction

    Use a different effective genome fraction than the one included in epic. Or include an egf for custom genomes that are not a part of epic. Should be a number between 0 and 1. Autoinferred by sampled read-length and genome by default.

  • -cs, –chromsizes

    Set the chromosome lengths yourself in a file with two columns: chromosome names and sizes. Useful to analyze custom genomes, assemblies or simulated data. Only chromosomes included in the file will be analyzed.

  • -sm, –store-matrix

    The path in which to store a gzipped matrix of read counts per window. One column for each ChIP and Input file.

  • -b, –bed

    A summary bed file of all regions, for display in the UCSC genome browser or for use in downstream analyses with e.g. bedtools. The score field is log2(#ChIP/#Input) * 100 capped at a 1000.

  • -bw, –bigwig

    The folder in which to store a RPKM-normalized bigwig for each file in the dataset. The bigwig shows how epic sees the data. It shows all bins, not just those in enriched regions.

  • -i2bw, –individual-log2fc-bigwigs

    The folder in which to store a log2FC bigwig for each ChIP bed/bedpe. Each file is divided against a normalized sum of the Input.

  • -cbw, –chip-bigwig

    Store the sum of the RPKM-normalized ChIP-data to a bigwig file.

  • -ibw, –input-bigwig

    Store the sum of the RPKM-normalized Input-data to a bigwig file.

  • -v, –version

    Show version.