The simplest way to run chromosight without any input data is to use:

chromosight test

Which will download a test dataset and run chromosight on it. This is useful to have a look at the output files.


chromosight detect takes input in the form of Hi-C matrices in cool format. This command allows to detect patterns on Hi-C maps, such as chromatin loops or domain (e.g. TADS) borders, and report their coordinates.

The following command line can be used to run loop detection (the default pattern):

chromosight detect -t12 sample1.cool results/sample1_loops

The program will run in parallel on 12 threads and write loop coordinates and their pattern matching scores in a file named sample1_loops.tsv inside the results folder. Those scores represent pearson correlation coefficients (i.e. between -1 and 1) between the loop kernel and the detected pattern. Similarly, to run domain borders detection, one can use:

chromosight detect --pattern borders -t12 sample1.cool results/sample1_borders

Which will write the coordinates and scores of borders in results/sample1_borders.tsv.

At this point, the results folder will also contain files sample1_loops.json and sample1_borders.json, which contain images of the matrix regions around each detected loop or border, respectively. These files are in JSON format, which can be natively loaded in most programming languages.

Chromosight has several command line options which can affect the output format or filter the results based on different criteria. All parameters have sane default values defined for each pattern, which are printed during the run, but these can be overriden using command line options to optimize results if needed. The list of command line options can be shown using:

chromosight --help


The chromosight quantify command can be used to assign a pattern matching score to a set of 2D coordinates for an input Hi-C matrix. It will accept coordinates in bed2d format (tab-separated text file with 6 columns without headers, where columns denote chrom1, start1, end1, chrom2, start2, end2), or the output coordinates file chromosight detect. This can be useful to score the same set of coordinates on multiple Hi-C libraries, for instance.

For example, to compute loop scores for the positions detected in sample1.cool for a second sample, one could use:

chromosight quantify results/sample1_loops.tsv sample2.cool results/sample2_loops

Similarly, for borders:

chromosight quantify --pattern=borders results/sample1_borders.tsv sample2.cool results/sample2_borders

These commands will each generate two files in the results directory, named sample2_loops.tsv and sample2_loops.json for the first command, and sample2_borders.tsv and sample2_borders.json for the second. Those files have the same format as the output from chromosight detect.

chromosight quantify can also be useful to compute pattern scores at ChIP-seq peaks, genes, or other features of interest. BEDtools can be used to generate a 2D bed file from an input bed file.

Assuming we have a BED file of cohesin peaks, all 2-way combinations of peaks at distances between 20kb and 1Mb can be retrieved with the following command:

bedtools window -a cohesin_peaks.bed -b cohesin_peaks.bed -w $MAXDIST \
    | awk -vmdist=$MINDIST '$1 == $4 && ($5 - $2) >= mdist {print}' \
    | sort -k1,1 -k2,2n -k4,4 -k5,5n \
    > cohesin_combinations_20kb_1Mb.bed2d

To quantify a pattern present only on the diagonal (e.g. borders, hairpins), the following command can be used instead.

paste cohesin_peaks.bed cohesin_peaks.bed > cohesin_combinations_0.bed2d

Generating custom patterns

More advanced users with specific questions or problems may wish to create new patterns and configurations. Both detect and quantify will accept custom patterns through the --kernel-config option. In order to provide a custom pattern, the user needs 2 files:

  • A JSON file containing default values for the different detection parameters.
  • One or more text files containing the pattern kernel(s) (i.e. matrix) in the form of a dense numeric matrix.

A template configuration can be generated using chromosight generate-config. A preset on which the template will be based can be selected, loops being the default preset. For example, to generate a template config based on the borders pattern, the folowing command can be used:

chromosight generate-config --preset borders demo_pattern

This will generate a JSON file named demo_pattern.json, pre-filled with parameter values from the borders pattern. This JSON file will have the following contents:

    "name": "borders",
    "kernels": [
    "max_dist": 1,
    "min_dist": 0,
    "max_iterations": 3,
    "max_perc_undetected": 30.0,
    "min_separation": 5000,
    "pearson": 0.3,
    "resolution": 5000

The user can edit the configuration parameters in a text editor. Notably, the kernels entry points to 3 files, demo_pattern.[1-3].txt, which have also been created by chromosight generate-config. Those 3 paths are relative to the config, which means the kernel files have to be in the same folder as the JSON config.

When given a config with multiple kernels, chromosight detect will scan the matrix once for each kernel and return the union of all detected coordinates for the different kernels. This is useful when a pattern is asymetric and can be flipped in different orientations, for example.

Kernels matrices are text files and can be edited using external program, or alternatively, the user can use the --click option from generate-config in order to manually build the kernel by double-clicking on relevant regions in a Hi-C matrix.

Note: The --click option will consume lots of RAM as it visualises the entire Hi-C matrix and should be reserved for small or subsetted contact maps.

For example:

chromosight generate-config --click sample1.cool --win-size 15 demo_manual

This command will generate a config file based on the loops template (the default) and will display the contact map sample1.cool. Every time the user double-clicks on a pixel, a window of 15x15 pixels centered on that position is recorded. The operation can be repeated as many times as the user wishes, and when the window is closed, all windows are averaged, a slight gaussian blur is added to reduce the impact of random noise, and the resulting pileup is used as the kernel when writing the config files.

A note on borders and kernels

One constraint in chromosight is that kernels must have an odd number of rows/columns. This is because chromosight always reports the center pixel of each window when computing correlations. For patterns which do not have a central pixel, such as borders which are between two pixels, a choice has to be made when making the kernel. In the case of borders, the kernel is shifted so that the central pixel is always the pixel on the right of the border.