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Dense neural networks for predicting chromatin conformation

BACKGROUND: DNA inside eukaryotic cells wraps around histones to form the 11nm chromatin fiber that can further fold into higher-order DNA loops, which may depend on the binding of architectural factors. Predicting how the DNA will fold given a distribution of bound factors, here viewed as a type of...

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Autores principales: Farré, Pau, Heurteau, Alexandre, Cuvier, Olivier, Emberly, Eldon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6186068/
https://www.ncbi.nlm.nih.gov/pubmed/30314429
http://dx.doi.org/10.1186/s12859-018-2286-z
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author Farré, Pau
Heurteau, Alexandre
Cuvier, Olivier
Emberly, Eldon
author_facet Farré, Pau
Heurteau, Alexandre
Cuvier, Olivier
Emberly, Eldon
author_sort Farré, Pau
collection PubMed
description BACKGROUND: DNA inside eukaryotic cells wraps around histones to form the 11nm chromatin fiber that can further fold into higher-order DNA loops, which may depend on the binding of architectural factors. Predicting how the DNA will fold given a distribution of bound factors, here viewed as a type of sequence, is currently an unsolved problem and several heterogeneous polymer models have shown that many features of the measured structure can be reproduced from simulations. However a model that determines the optimal connection between sequence and structure and that can rapidly assess the effects of varying either one is still lacking. RESULTS: Here we train a dense neural network to solve for the local folding of chromatin, connecting structure, represented as a contact map, to a sequence of bound chromatin factors. The network includes a convolutional filter that compresses the large number of bound chromatin factors into a single 1D sequence representation that is optimized for predicting structure. We also train a network to solve the inverse problem, namely given only structural information in the form of a contact map, predict the likely sequence of chromatin states that generated it. CONCLUSIONS: By carrying out sensitivity analysis on both networks, we are able to highlight the importance of chromatin contexts and neighborhoods for regulating long-range contacts, along with critical alterations that affect contact formation. Our analysis shows that the networks have learned physical insights that are informative and intuitive about this complex polymer problem. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2286-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-61860682018-10-19 Dense neural networks for predicting chromatin conformation Farré, Pau Heurteau, Alexandre Cuvier, Olivier Emberly, Eldon BMC Bioinformatics Research Article BACKGROUND: DNA inside eukaryotic cells wraps around histones to form the 11nm chromatin fiber that can further fold into higher-order DNA loops, which may depend on the binding of architectural factors. Predicting how the DNA will fold given a distribution of bound factors, here viewed as a type of sequence, is currently an unsolved problem and several heterogeneous polymer models have shown that many features of the measured structure can be reproduced from simulations. However a model that determines the optimal connection between sequence and structure and that can rapidly assess the effects of varying either one is still lacking. RESULTS: Here we train a dense neural network to solve for the local folding of chromatin, connecting structure, represented as a contact map, to a sequence of bound chromatin factors. The network includes a convolutional filter that compresses the large number of bound chromatin factors into a single 1D sequence representation that is optimized for predicting structure. We also train a network to solve the inverse problem, namely given only structural information in the form of a contact map, predict the likely sequence of chromatin states that generated it. CONCLUSIONS: By carrying out sensitivity analysis on both networks, we are able to highlight the importance of chromatin contexts and neighborhoods for regulating long-range contacts, along with critical alterations that affect contact formation. Our analysis shows that the networks have learned physical insights that are informative and intuitive about this complex polymer problem. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2286-z) contains supplementary material, which is available to authorized users. BioMed Central 2018-10-11 /pmc/articles/PMC6186068/ /pubmed/30314429 http://dx.doi.org/10.1186/s12859-018-2286-z Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Farré, Pau
Heurteau, Alexandre
Cuvier, Olivier
Emberly, Eldon
Dense neural networks for predicting chromatin conformation
title Dense neural networks for predicting chromatin conformation
title_full Dense neural networks for predicting chromatin conformation
title_fullStr Dense neural networks for predicting chromatin conformation
title_full_unstemmed Dense neural networks for predicting chromatin conformation
title_short Dense neural networks for predicting chromatin conformation
title_sort dense neural networks for predicting chromatin conformation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6186068/
https://www.ncbi.nlm.nih.gov/pubmed/30314429
http://dx.doi.org/10.1186/s12859-018-2286-z
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