Computer vision for pattern detection in chromosome contact maps
| Autor: | Axel Breuer, Axel Cournac, Antoine Vigouroux, Etienne Jean, Adrien Meot, Edgar Oriol, Romain Koszul, Laurent Politis, Arnaud Campeas, Nadège Guiglielmoni, Cyril Matthey-Doret, Lyam Baudry, Vittore F. Scolari, Philippe Henri Chanut, Pierrick Moreau, Rémi Montagne |
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| Přispěvatelé: | Régulation spatiale des Génomes - Spatial Regulation of Genomes, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Collège doctoral [Sorbonne universités], Sorbonne Université (SU), ENGIE, Biologie de Synthèse - Synthetic biology, Institut Pasteur [Paris], Département de Biologie Computationnelle - Department of Computational Biology, This work used the computational and storage services (TARS cluster) provided by the IT department at Institut Pasteur, Paris. C.M.-D. was supported by the Pasteur—Paris University (PPU) International PhD Program. A.B. works within the framework of a 'Mécénat Compétence' contract of the company ENGIE. V.S. is the recipient of a Roux-Cantarini Pasteur fellowship. This research was supported by funding to R.K. from the European Research Council under the Horizon 2020 Program (ERC grant agreement 771813) and by ANR JCJC 2019, 'Apollo' allocated to A.C., This work was initiated during a Hackathon between Institut Pasteur scientists and ENGIE engineers. We would like to thank all the people that allow the organisation of this event especially Anne-Gaelle Coutris, Romain Tchertchian and Olivier Gascuel. Julien Mozziconacci, Frédéric Beckouët and all the members of Spatial Regulation of Genomes unit are thanked for stimulating discussions and feedback., ANR-19-CE45-0003,Apollo,Intelligence Artificielle pour atteindre la face cachée des chromosomes(2019), European Project: 771813,SynarchiC, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Collège Doctoral, Institut Pasteur [Paris] (IP), European Project: 771813,ERC-2017-COG,SynarchiC(2018) |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Computer science [SDV]Life Sciences [q-bio] General Physics and Astronomy Genome informatics Genome Pattern Recognition Automated Workflow Chromosome conformation capture chemistry.chemical_compound 0302 clinical medicine Chromosomes Human MESH: Pattern Recognition Automated Computer vision lcsh:Science 0303 health sciences Multidisciplinary Fungal genetics MESH: Chromosomes Fungal MESH: Saccharomyces cerevisiae Chromatin Data processing Simulated data MESH: Genome Fungal Pattern recognition (psychology) Chromosomes Fungal Genome Fungal Algorithms MESH: Computers Bioinformatics Science MESH: Algorithms Saccharomyces cerevisiae MESH: Chromosomes Human Chromosomes Article General Biochemistry Genetics and Molecular Biology MESH: Workflow 03 medical and health sciences Pattern detection Code (cryptography) Humans 030304 developmental biology Nuclear organization MESH: Humans Computers business.industry Chromosome General Chemistry 030104 developmental biology chemistry lcsh:Q MESH: Chromosomes Artificial intelligence business DNA 030217 neurology & neurosurgery |
| Zdroj: | Nature Communications Nature Communications, Nature Publishing Group, 2020, 11 (1), pp.5795. ⟨10.1038/s41467-020-19562-7⟩ Nature Communications, Vol 11, Iss 1, Pp 1-11 (2020) Nature Communications, 2020, 11 (1), pp.5795. ⟨10.1038/s41467-020-19562-7⟩ |
| ISSN: | 2041-1723 |
| Popis: | 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. Chromatin loops bridging distant loci within chromosomes can be detected by a variety of techniques such as Hi-C. Here the authors present Chromosight, an algorithm applied on mammalian, bacterial, viral and yeast genomes, able to detect various types of pattern in chromosome contact maps, including chromosomal loops. |
| Databáze: | OpenAIRE |
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