Computer vision for pattern detection in chromosome contact maps

Cyril Matthey-Doret, Lyam Baudry, Axel Breuer, Rémi Montagne, Nadège Guiglielmoni, Vittore Scolari, Etienne Jean, Arnaud Campeas, Philippe Henri Chanut, Edgar Oriol, Adrien Méot, Laurent Politis, Antoine Vigouroux, Pierrick Moreau, Romain Koszul () and Axel Cournac ()
Additional contact information
Cyril Matthey-Doret: Institut Pasteur, Unité Régulation Spatiale des Génomes, CNRS, UMR 3525, C3BI USR 3756
Lyam Baudry: Institut Pasteur, Unité Régulation Spatiale des Génomes, CNRS, UMR 3525, C3BI USR 3756
Axel Breuer: ENGIE, Global Energy Management
Rémi Montagne: Institut Pasteur, Unité Régulation Spatiale des Génomes, CNRS, UMR 3525, C3BI USR 3756
Nadège Guiglielmoni: Institut Pasteur, Unité Régulation Spatiale des Génomes, CNRS, UMR 3525, C3BI USR 3756
Vittore Scolari: Institut Pasteur, Unité Régulation Spatiale des Génomes, CNRS, UMR 3525, C3BI USR 3756
Etienne Jean: Institut Pasteur, Unité Régulation Spatiale des Génomes, CNRS, UMR 3525, C3BI USR 3756
Arnaud Campeas: ENGIE, Global Energy Management
Philippe Henri Chanut: ENGIE, Global Energy Management
Edgar Oriol: ENGIE, Global Energy Management
Adrien Méot: ENGIE, Global Energy Management
Laurent Politis: ENGIE, Global Energy Management
Antoine Vigouroux: Institut Pasteur, Synthetic Biology Group
Pierrick Moreau: Institut Pasteur, Unité Régulation Spatiale des Génomes, CNRS, UMR 3525, C3BI USR 3756
Romain Koszul: Institut Pasteur, Unité Régulation Spatiale des Génomes, CNRS, UMR 3525, C3BI USR 3756
Axel Cournac: Institut Pasteur, Unité Régulation Spatiale des Génomes, CNRS, UMR 3525, C3BI USR 3756

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: Abstract Chromosomes of all species studied so far display a variety of higher-order organisational features, such as self-interacting domains or loops. These structures, which are often associated to biological functions, form distinct, visible patterns on genome-wide contact maps generated by chromosome conformation capture approaches such as Hi-C. Here we present Chromosight, an algorithm inspired from computer vision that can detect patterns in contact maps. Chromosight has greater sensitivity than existing methods on synthetic simulated data, while being faster and applicable to any type of genomes, including bacteria, viruses, yeasts and mammals. Our method does not require any prior training dataset and works well with default parameters on data generated with various protocols.

Date: 2020
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DOI: 10.1038/s41467-020-19562-7

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