Dense neural networks for predicting chromatin conformation
Abstract 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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2018-10-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-018-2286-z |
| _version_ | 1818147447392698368 |
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| author | Pau Farré Alexandre Heurteau Olivier Cuvier Eldon Emberly |
| author_facet | Pau Farré Alexandre Heurteau Olivier Cuvier Eldon Emberly |
| author_sort | Pau Farré |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-11T12:35:23Z |
| format | Article |
| id | doaj.art-f310a6f4469b41aca8d3063c3e7bf5bb |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-11T12:35:23Z |
| publishDate | 2018-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-f310a6f4469b41aca8d3063c3e7bf5bb2022-12-22T01:07:09ZengBMCBMC Bioinformatics1471-21052018-10-0119111210.1186/s12859-018-2286-zDense neural networks for predicting chromatin conformationPau Farré0Alexandre Heurteau1Olivier Cuvier2Eldon Emberly3Department of Physics, Simon Fraser UniversityLaboratoire de Biologie Moléculaire des Eucaryotes (LBME), CNRSLaboratoire de Biologie Moléculaire des Eucaryotes (LBME), CNRSDepartment of Physics, Simon Fraser UniversityAbstract 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.http://link.springer.com/article/10.1186/s12859-018-2286-zChromatin foldingDense neural networkHI-CChIP |
| spellingShingle | Pau Farré Alexandre Heurteau Olivier Cuvier Eldon Emberly Dense neural networks for predicting chromatin conformation BMC Bioinformatics Chromatin folding Dense neural network HI-C ChIP |
| 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 | Chromatin folding Dense neural network HI-C ChIP |
| url | http://link.springer.com/article/10.1186/s12859-018-2286-z |
| work_keys_str_mv | AT paufarre denseneuralnetworksforpredictingchromatinconformation AT alexandreheurteau denseneuralnetworksforpredictingchromatinconformation AT oliviercuvier denseneuralnetworksforpredictingchromatinconformation AT eldonemberly denseneuralnetworksforpredictingchromatinconformation |