---
_id: '8707'
abstract:
- lang: eng
  text: Dynamic changes in the three-dimensional (3D) organization of chromatin are
    associated with central biological processes, such as transcription, replication
    and development. Therefore, the comprehensive identification and quantification
    of these changes is fundamental to understanding of evolutionary and regulatory
    mechanisms. Here, we present Comparison of Hi-C Experiments using Structural Similarity
    (CHESS), an algorithm for the comparison of chromatin contact maps and automatic
    differential feature extraction. We demonstrate the robustness of CHESS to experimental
    variability and showcase its biological applications on (1) interspecies comparisons
    of syntenic regions in human and mouse models; (2) intraspecies identification
    of conformational changes in Zelda-depleted Drosophila embryos; (3) patient-specific
    aberrant chromatin conformation in a diffuse large B-cell lymphoma sample; and
    (4) the systematic identification of chromatin contact differences in high-resolution
    Capture-C data. In summary, CHESS is a computationally efficient method for the
    comparison and classification of changes in chromatin contact data.
acknowledgement: 'Work in the Vaquerizas laboratory is funded by the Max Planck Society,
  the Deutsche Forschungsgemeinschaft (DFG) Priority Programme SPP 2202 ‘Spatial Genome
  Architecture in Development and Disease’ (project no. 422857230 to J.M.V.), the
  DFG Clinical Research Unit CRU326 ‘Male Germ Cells: from Genes to Function’ (project
  no. 329621271 to J.M.V.), the European Union’s Horizon 2020 research and innovation
  programme under the Marie Skłodowska-Curie grant agreement no. 643062—ZENCODE-ITN
  to J.M.V.) and the Medical Research Council in the UK. This research was partially
  funded by the European Union’s H2020 Framework Programme through the European Research
  Council (grant no. 609989 to M.A.M.-R.). We thank the support of the Spanish Ministerio
  de Ciencia, Innovación y Universidades through grant no. BFU2017-85926-P to M.A.M.-R.
  The Centre for Genomic Regulation thanks the support of the Ministerio de Ciencia,
  Innovación y Universidades to the European Molecular Biology Laboratory partnership,
  the ‘Centro de Excelencia Severo Ochoa 2013–2017’, agreement no. SEV-2012-0208,
  the CERCA Programme/Generalitat de Catalunya, Spanish Ministerio de Ciencia, Innovación
  y Universidades through the Instituto de Salud Carlos III, the Generalitat de Catalunya
  through the Departament de Salut and Departament d’Empresa i Coneixement and cofinancing
  by the Spanish Ministerio de Ciencia, Innovación y Universidades with funds from
  the European Regional Development Fund corresponding to the 2014–2020 Smart Growth
  Operating Program. S.G. thanks the support from the Company of Biologists (grant
  no. JCSTF181158) and the European Molecular Biology Organization Short-Term Fellowship
  programme.'
article_processing_charge: No
article_type: original
author:
- first_name: Silvia
  full_name: ' Galan, Silvia'
  last_name: ' Galan'
- first_name: Nick N
  full_name: Machnik, Nick N
  id: 3591A0AA-F248-11E8-B48F-1D18A9856A87
  last_name: Machnik
  orcid: 0000-0001-6617-9742
- first_name: Kai
  full_name: Kruse, Kai
  last_name: Kruse
- first_name: Noelia
  full_name: Díaz, Noelia
  last_name: Díaz
- first_name: Marc A
  full_name: Marti-Renom, Marc A
  last_name: Marti-Renom
- first_name: Juan M
  full_name: Vaquerizas, Juan M
  last_name: Vaquerizas
citation:
  ama: Galan S, Machnik NN, Kruse K, Díaz N, Marti-Renom MA, Vaquerizas JM. CHESS
    enables quantitative comparison of chromatin contact data and automatic feature
    extraction. <i>Nature Genetics</i>. 2020;52:1247-1255. doi:<a href="https://doi.org/10.1038/s41588-020-00712-y">10.1038/s41588-020-00712-y</a>
  apa: Galan, S., Machnik, N. N., Kruse, K., Díaz, N., Marti-Renom, M. A., &#38; Vaquerizas,
    J. M. (2020). CHESS enables quantitative comparison of chromatin contact data
    and automatic feature extraction. <i>Nature Genetics</i>. Springer Nature. <a
    href="https://doi.org/10.1038/s41588-020-00712-y">https://doi.org/10.1038/s41588-020-00712-y</a>
  chicago: Galan, Silvia, Nick N Machnik, Kai Kruse, Noelia Díaz, Marc A Marti-Renom,
    and Juan M Vaquerizas. “CHESS Enables Quantitative Comparison of Chromatin Contact
    Data and Automatic Feature Extraction.” <i>Nature Genetics</i>. Springer Nature,
    2020. <a href="https://doi.org/10.1038/s41588-020-00712-y">https://doi.org/10.1038/s41588-020-00712-y</a>.
  ieee: S.  Galan, N. N. Machnik, K. Kruse, N. Díaz, M. A. Marti-Renom, and J. M.
    Vaquerizas, “CHESS enables quantitative comparison of chromatin contact data and
    automatic feature extraction,” <i>Nature Genetics</i>, vol. 52. Springer Nature,
    pp. 1247–1255, 2020.
  ista: Galan S, Machnik NN, Kruse K, Díaz N, Marti-Renom MA, Vaquerizas JM. 2020.
    CHESS enables quantitative comparison of chromatin contact data and automatic
    feature extraction. Nature Genetics. 52, 1247–1255.
  mla: Galan, Silvia, et al. “CHESS Enables Quantitative Comparison of Chromatin Contact
    Data and Automatic Feature Extraction.” <i>Nature Genetics</i>, vol. 52, Springer
    Nature, 2020, pp. 1247–55, doi:<a href="https://doi.org/10.1038/s41588-020-00712-y">10.1038/s41588-020-00712-y</a>.
  short: S.  Galan, N.N. Machnik, K. Kruse, N. Díaz, M.A. Marti-Renom, J.M. Vaquerizas,
    Nature Genetics 52 (2020) 1247–1255.
date_created: 2020-10-25T23:01:20Z
date_published: 2020-10-19T00:00:00Z
date_updated: 2023-08-22T10:37:10Z
day: '19'
department:
- _id: FyKo
doi: 10.1038/s41588-020-00712-y
external_id:
  isi:
  - '000579693500004'
  pmid:
  - '33077914'
intvolume: '        52'
isi: 1
language:
- iso: eng
month: '10'
oa_version: None
page: 1247-1255
pmid: 1
publication: Nature Genetics
publication_identifier:
  eissn:
  - '15461718'
  issn:
  - '10614036'
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: CHESS enables quantitative comparison of chromatin contact data and automatic
  feature extraction
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 52
year: '2020'
...
