DeepRes: a new deep-learning- and aspect-based local resolution method for electron-microscopy maps

In this article, a method is presented to estimate a new local quality measure for 3D cryoEM maps that adopts the form of a `local resolution' type of information. The algorithm (DeepRes) is based on deep-learning 3D feature detection. DeepRes is fully automatic and parameter-free, and avoids t...

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Detalles Bibliográficos
Autores: Ramírez-Aportela, Erney, Mota, J., Conesa Mingo, Pablo, Carazo, José M., Sorzano, Carlos Óscar S.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/240121
Acceso en línea:http://hdl.handle.net/10261/240121
Access Level:acceso abierto
Palabra clave:DeepRes
Electron microscopy
Single-particle analysis
Local resolution
3D reconstruction and image processing
Single-particle cryoEM
Structure determination
Cryo-electron microscopy (cryo-EM)
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spelling DeepRes: a new deep-learning- and aspect-based local resolution method for electron-microscopy mapsRamírez-Aportela, ErneyMota, J.Conesa Mingo, PabloCarazo, José M.Sorzano, Carlos Óscar S.DeepResElectron microscopySingle-particle analysisLocal resolution3D reconstruction and image processingSingle-particle cryoEMStructure determinationCryo-electron microscopy (cryo-EM)In this article, a method is presented to estimate a new local quality measure for 3D cryoEM maps that adopts the form of a `local resolution' type of information. The algorithm (DeepRes) is based on deep-learning 3D feature detection. DeepRes is fully automatic and parameter-free, and avoids the issues of most current methods, such as their insensitivity to enhancements owing to B-factor sharpening (unless the 3D mask is changed), among others, which is an issue that has been virtually neglected in the cryoEM field until now. In this way, DeepRes can be applied to any map, detecting subtle changes in local quality after applying enhancement processes such as isotropic filters or substantially more complex procedures, such as model-based local sharpening, non-model-based methods or denoising, that may be very difficult to follow using current methods. It performs as a human observer expects. The comparison with traditional local resolution indicators is also addressed.The authors would like to acknowledge financial support from: the Comunidad de Madrid through grant CAM (S2017/BMD-3817), the Spanish Ministry of Economy and Competitiveness (BIO2016-76400-R) and the European Union and Horizon 2020 through INSTRUCT-ULTRA (INFRADEV-03-016-2017, Proposal 731005), iNEXT (INFRAIA-1-2014-2015, Proposal 653706) and West-Life (EINFRA-2015-1, Proposal 675858).International Union of CrystallographyComunidad de MadridMinisterio de Economía y Competitividad (España)European CommissionConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2021202120192021info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/240121reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#S2017/BMD-3817/TomoXLiver-CMinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/BIO2016-76400-Rinfo:eu-repo/grantAgreement/EC/H2020/731005info:eu-repo/grantAgreement/EC/H2020/653706info:eu-repo/grantAgreement/EC/H2020/675858http://dx.doi.org/10.1107/S2052252519011692Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2401212026-05-22T06:33:51Z
dc.title.none.fl_str_mv DeepRes: a new deep-learning- and aspect-based local resolution method for electron-microscopy maps
title DeepRes: a new deep-learning- and aspect-based local resolution method for electron-microscopy maps
spellingShingle DeepRes: a new deep-learning- and aspect-based local resolution method for electron-microscopy maps
Ramírez-Aportela, Erney
DeepRes
Electron microscopy
Single-particle analysis
Local resolution
3D reconstruction and image processing
Single-particle cryoEM
Structure determination
Cryo-electron microscopy (cryo-EM)
title_short DeepRes: a new deep-learning- and aspect-based local resolution method for electron-microscopy maps
title_full DeepRes: a new deep-learning- and aspect-based local resolution method for electron-microscopy maps
title_fullStr DeepRes: a new deep-learning- and aspect-based local resolution method for electron-microscopy maps
title_full_unstemmed DeepRes: a new deep-learning- and aspect-based local resolution method for electron-microscopy maps
title_sort DeepRes: a new deep-learning- and aspect-based local resolution method for electron-microscopy maps
dc.creator.none.fl_str_mv Ramírez-Aportela, Erney
Mota, J.
Conesa Mingo, Pablo
Carazo, José M.
Sorzano, Carlos Óscar S.
author Ramírez-Aportela, Erney
author_facet Ramírez-Aportela, Erney
Mota, J.
Conesa Mingo, Pablo
Carazo, José M.
Sorzano, Carlos Óscar S.
author_role author
author2 Mota, J.
Conesa Mingo, Pablo
Carazo, José M.
Sorzano, Carlos Óscar S.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Comunidad de Madrid
Ministerio de Economía y Competitividad (España)
European Commission
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv DeepRes
Electron microscopy
Single-particle analysis
Local resolution
3D reconstruction and image processing
Single-particle cryoEM
Structure determination
Cryo-electron microscopy (cryo-EM)
topic DeepRes
Electron microscopy
Single-particle analysis
Local resolution
3D reconstruction and image processing
Single-particle cryoEM
Structure determination
Cryo-electron microscopy (cryo-EM)
description In this article, a method is presented to estimate a new local quality measure for 3D cryoEM maps that adopts the form of a `local resolution' type of information. The algorithm (DeepRes) is based on deep-learning 3D feature detection. DeepRes is fully automatic and parameter-free, and avoids the issues of most current methods, such as their insensitivity to enhancements owing to B-factor sharpening (unless the 3D mask is changed), among others, which is an issue that has been virtually neglected in the cryoEM field until now. In this way, DeepRes can be applied to any map, detecting subtle changes in local quality after applying enhancement processes such as isotropic filters or substantially more complex procedures, such as model-based local sharpening, non-model-based methods or denoising, that may be very difficult to follow using current methods. It performs as a human observer expects. The comparison with traditional local resolution indicators is also addressed.
publishDate 2019
dc.date.none.fl_str_mv 2019
2021
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/240121
url http://hdl.handle.net/10261/240121
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
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#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
S2017/BMD-3817/TomoXLiver-CM
info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/BIO2016-76400-R
info:eu-repo/grantAgreement/EC/H2020/731005
info:eu-repo/grantAgreement/EC/H2020/653706
info:eu-repo/grantAgreement/EC/H2020/675858
http://dx.doi.org/10.1107/S2052252519011692
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv International Union of Crystallography
publisher.none.fl_str_mv International Union of Crystallography
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
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