fontanka

Fountains are novel features of Hi-C maps, resembling "flares" [1], "plumes" [2] and "jets" [3]. They can be detected as inverted triangular structures along the main diagonal of Hi-C maps. Fountains are also similar to "hairpins" [4].

Fontanka aims to provide a flexible Python API and specialized CLI for calling fountains from cool Hi-C files.

Note that Chromosight [4] can be modified to do fountains calling with the binary fountain mask (see examples), although producing patterns with much sharper edges.

Installation

It is highly recommended to work in conda environment, which can be found in environment.yml:

conda env create -f environment.yml
conda activate fontanka

Note that fontanka version 0.1 runs old versions of cooltools and bioframe, as specified in the requirements and environment file.

Optionally, you may want to activate IPython kernel to work with API:

python -m ipykernel install --user --name fontanka --display-name "fontanka"

git clone https://github.com/agalitsyna/fontanka.git
cd fontanka
pip install -e ./

Example usage

Fontanka is cooltools-based utility [5], that requires ICE-balanced cool files as input [6]. If you start from some other format, consider cooler cload and hic2cool converters. After conversion, run cooler balance or cooler zoomify --balance to get ICE-balanced cool file.

1. Calculate expected with cooltools (v0.5.2):

cooltools expected-cis test.mcool::resolutions/10000 \
    --regions test.viewframe.tsv \
    -p 20 --clr-weight-name weight --ignore-diags 2 \
    -o test.expected.tsv

2. Store whole-genome snippets of Hi-C maps:

fontanka slice-windows test.mcool::resolutions/10000 \
    output.200Kb.snips.npy \
    -W 200_000 -p 20 \
    --view test.viewframe.tsv --expected test.expected.tsv

3. Call for the binary fountain mask:

fontanka apply-binary-fountain-mask test.mcool::resolutions/10000 \
         output.fountains.tsv \
         -A 0.7854 \
         -W 200_000 \
         -p 20 \
         --snips output.binary.fountains.npy \
         --regions chromosome.regions.txt 

4. Call for custom mask:

fontanka apply-fountain-mask test.mcool::resolutions/10000 \
         output.fountains.tsv \
         -M test.fountain.mask.npy  \
         -W 200_000 \
         -p 20 \
         --snips output.mask.fountains.npy \
         --regions chromosome.regions.txt 

For running fontanka as done in Galitsyna et al. 2023, see the notebook in the "examples/".

References:

1. Wike, C. L., Guo, Y., Tan, M., Nakamura, R., Shaw, D. K., Díaz, N., ... & Cairns, B. R. (2021). Chromatin architecture transitions from zebrafish sperm through early embryogenesis. Genome Research, 31(6), 981-994.

2. Liu, N. Q., Magnitov, M., Schijns, M., van Schaik, T., van der Weide, R. H., Teunissen, H., ... & de Wit, E. (2021). Rapid depletion of CTCF and cohesin proteins reveals dynamic features of chromosome architecture.

3. Guo, Y., Al-Jibury, E., Garcia-Millan, R., Ntagiantas, K., King, J.W., Nash, A.J., Galjart, N., Lenhard, B., Rueckert, D., Fisher, A.G. and Pruessner, G., 2022. Chromatin jets define the properties of cohesin-driven in vivo loop extrusion. Molecular Cell, 82(20), pp.3769-3780.

4. Matthey-Doret, C., Baudry, L., Breuer, A., Montagne, R., Guiglielmoni, N., Scolari, V., ... & Cournac, A. (2020). Computer vision for pattern detection in chromosome contact maps. Nature Communications, 11(1), 1-11.

5. Open 2C, Abdennur N, Abraham S, Fudenberg G, Flyamer IM, Galitsyna AA, Goloborodko A, Imakaev M, Oksuz BA, Venev SV. Cooltools: enabling high-resolution Hi-C analysis in Python. BioRxiv. 2022:2022-10.

6. Abdennur N, Mirny LA. Cooler: scalable storage for Hi-C data and other genomically labeled arrays. Bioinformatics. 2020 Jan 1;36(1) :311-6.