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description Publicationkeyboard_double_arrow_right Article , Other literature type , Preprint 2021Embargo end date: 27 Jan 2022 Spain, Italy, Belgium EnglishPublisher:Nature Research Publicly fundedFunded by:EC | IDPfun, EC | REFRACTEC| IDPfun ,EC| REFRACTIan Walsh; Dmytro Fishman; Dario Garcia-Gasulla; Tiina Titma; Gianluca Pollastri; Emidio Capriotti; Rita Casadio; Salvador Capella-Gutierrez; Davide Cirillo; Alessio Del Conte; Alexandros C. Dimopoulos; Victoria Dominguez Del Angel; Joaquin Dopazo; Piero Fariselli; José Maria Fernández; Florian Huber; Anna Kreshuk; Tom Lenaerts; Pier Luigi Martelli; Arcadi Navarro; Pilib Ó Broin; Janet Piñero; Damiano Piovesan; Martin Reczko; Francesco Ronzano; Venkata Satagopam; Castrense Savojardo; Vojtech Spiwok; Marco Antonio Tangaro; Giacomo Tartari; David Salgado; Alfonso Valencia; Federico Zambelli; Jennifer Harrow; Fotis E. Psomopoulos; Silvio C. E. Tosatto;The work of the Machine Learning Focus Group was funded by ELIXIR, the research infrastructure for life-science data. IW was funded by the A*STAR Career Development Award (project no. C210112057) from the Agency for Science, Technology and Research (A*STAR), Singapore. D.F. was supported by Estonian Research Council grants (PRG1095, PSG59 and ERA-NET TRANSCAN-2 (BioEndoCar)); Project No 2014-2020.4.01.16-0271, ELIXIR and the European Regional Development Fund through EXCITE Center of Excellence. S.C.E.T. has received funding from the European Union’s Horizon 2020 research and innovation programme under Marie Skłodowska-Curie Grant agreements No. 778247 and No. 823886, and Italian Ministry of University and Research PRIN 2017 grant 2017483NH8. "Article signat per 8 autors més 28 autors/es de l' ELIXIR Machine Learning Focus Group: Emidio Capriotti, Rita Casadio, Salvador Capella-Gutierrez, Davide Cirillo, Alessio Del Conte, Alexandros C. Dimopoulos, Victoria Dominguez Del Angel, Joaquin Dopazo, Piero Fariselli, José Maria Fernández, Florian Huber, Anna Kreshuk, Tom Lenaerts, Pier Luigi Martelli, Arcadi Navarro, Pilib Ó Broin, Janet Piñero, Damiano Piovesan, Martin Reczko, Francesco Ronzano, Venkata Satagopam, Castrense Savojardo, Vojtech Spiwok, Marco Antonio Tangaro, Giacomo Tartari, David Salgado, Alfonso Valencia & Federico Zambelli" Supervised machine learning is widely used in biology and deserves more scrutiny. We present a set of community-wide recommendations (DOME) aiming to help establish standards of supervised machine learning validation in biology. Formulated as questions, the DOME recommendations improve the assessment and reproducibility of papers when included as supplementary material. Peer Reviewed
Archivio Istituziona... arrow_drop_down Recolector de Ciencia Abierta, RECOLECTAArticle . 2021 . Peer-reviewedData sources: Recolector de Ciencia Abierta, RECOLECTAVrije Universiteit Brussel Research PortalOther literature type . 2021Data sources: Vrije Universiteit Brussel Research PortalUPCommons. Portal del coneixement obert de la UPCOther literature type . Article . 2021 . Peer-reviewedData sources: UPCommons. Portal del coneixement obert de la UPChttps://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen bronze 90 citations 90 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!visibility 89visibility views 89 download downloads 37 Powered bymore_vert Archivio Istituziona... arrow_drop_down Recolector de Ciencia Abierta, RECOLECTAArticle . 2021 . Peer-reviewedData sources: Recolector de Ciencia Abierta, RECOLECTAVrije Universiteit Brussel Research PortalOther literature type . 2021Data sources: Vrije Universiteit Brussel Research PortalUPCommons. Portal del coneixement obert de la UPCOther literature type . Article . 2021 . Peer-reviewedData sources: UPCommons. Portal del coneixement obert de la UPChttps://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2020 France, Cyprus, Italy EnglishPublisher:Oxford University Press (OUP) Funded by:EC | IDPfun, EC | chemREPEATEC| IDPfun ,EC| chemREPEATMier, Pablo; Paladin, Lisanna; Tamana, Stella; Petrosian, Sophia; Hajdu-Soltész, Borbála; Urbanek, Annika; Gruca, Aleksandra; Plewczynski, Dariusz; Grynberg, Marcin; Bernadó, Pau; Gáspári, Zoltán; Ouzounis, Christos A.; Promponas, Vasilis J.; Kajava, Andrey V.; Hancock, John M.; Tosatto, Silvio C. E.; Dosztanyi, Zsuzsanna; Andrade-Navarro, Miguel A.; Mier, Pablo; Paladin, Lisanna; Tamana, Stella; Petrosian, Sophia; Hajdu-Soltész, Borbála; Urbanek, Annika; Gruca, Aleksandra; Plewczynski, Dariusz; Grynberg, Marcin; Bernadó, Pau; Gáspári, Zoltán; Ouzounis, Christos A.; Promponas, Vasilis J.; Kajava, Andrey V.; Hancock, John M.; Tosatto, Silvio C. E.; Dosztanyi, Zsuzsanna; Andrade-Navarro, Miguel A.;Abstract There are multiple definitions for low complexity regions (LCRs) in protein sequences, with all of them broadly considering LCRs as regions with fewer amino acid types compared to an average composition. Following this view, LCRs can also be defined as regions showing composition bias. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, and more generally the overlaps between different properties related to LCRs, using examples. We argue that statistical measures alone cannot capture all structural aspects of LCRs and recommend the combined usage of a variety of predictive tools and measurements. While the methodologies available to study LCRs are already very advanced, we foresee that a more comprehensive annotation of sequences in the databases will enable the improvement of predictions and a better understanding of the evolution and the connection between structure and function of LCRs. This will require the use of standards for the generation and exchange of data describing all aspects of LCRs. Short abstract There are multiple definitions for low complexity regions (LCRs) in protein sequences. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, plus overlaps between different properties related to LCRs, using examples.
Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2019Full-Text: http://europepmc.org/articles/PMC7299295Data sources: PubMed CentralArchivio istituzionale della ricerca - Università di Padova; Briefings in BioinformaticsOther literature type . Article . 2020 . 2019 . Peer-reviewedLicense: CC BY NCadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 67 citations 67 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!more_vert Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2019Full-Text: http://europepmc.org/articles/PMC7299295Data sources: PubMed CentralArchivio istituzionale della ricerca - Università di Padova; Briefings in BioinformaticsOther literature type . Article . 2020 . 2019 . Peer-reviewedLicense: CC BY NCadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2019 Cyprus, Portugal, France, Serbia, France, Denmark, Italy, Spain, Belgium EnglishPublisher:HAL CCSD Funded by:MESTD | Application of the EIIP/I..., EC | IDPfunMESTD| Application of the EIIP/ISM bioinformatics platform in discovery of novel therapeutic targets and potential therapeutic molecules ,EC| IDPfunAuthors: Hatos, András; Hajdu-Soltész, Borbála; Monzon, Alexander M.; Palopoli, Nicolas; +124 AuthorsHatos, András; Hajdu-Soltész, Borbála; Monzon, Alexander M.; Palopoli, Nicolas; Álvarez, Lucía; Aykac-Fas, Burcu; Bassot, Claudio; Benítez, Guillermo I.; Bevilacqua, Martina; Chasapi, Anastasia; Chemes, Lucia; Davey, Norman E.; Davidović, Radoslav; Dunker, A. Keith; Elofsson, Arne; Gobeill, Julien; Foutel, Nicolás S. González; Sudha, Govindarajan; Guharoy, Mainak; Horvath, Tamas; Iglesias, Valentin; Kajava, Andrey V.; Kovacs, Orsolya P.; Lamb, John; Lambrughi, Matteo; Lazar, Tamas; Leclercq, Jeremy Y.; Leonardi, Emanuela; Macedo-Ribeiro, Sandra; Macossay-Castillo, Mauricio; Maiani, Emiliano; Manso, José A.; Marino-Buslje, Cristina; Martínez-Pérez, Elizabeth; Mészáros, Bálint; Mičetić, Ivan; Minervini, Giovanni; Murvai, Nikoletta; Necci, Marco; Ouzounis, Christos A.; Pajkos, Mátyás; Paladin, Lisanna; Pancsa, Rita; Papaleo, Elena; Parisi, Gustavo; Pasche, Emilie; Barbosa Pereira, Pedro J.; Promponas, Vasilis J.; Pujols, Jordi; Quaglia, Federica; Ruch, Patrick; Salvatore, Marco; Schad, Eva; Szabo, Beata; Szaniszló, Tamás; Tamana, Stella; Tantos, Agnes; Veljkovic, Nevena; Ventura, Salvador; Vranken, Wim; Dosztányi, Zsuzsanna; Tompa, Peter; Tosatto, Silvio C. E.; Piovesan, Damiano; Hatos, András; Hajdu-Soltész, Borbála; Monzon, Alexander M.; Palopoli, Nicolas; Álvarez, Lucía; Aykac-Fas, Burcu; Bassot, Claudio; Benítez, Guillermo I.; Bevilacqua, Martina; Chasapi, Anastasia; Chemes, Lucia; Davey, Norman E.; Davidović, Radoslav; Dunker, A. Keith; Elofsson, Arne; Gobeill, Julien; Foutel, Nicolás S. González; Sudha, Govindarajan; Guharoy, Mainak; Horvath, Tamas; Iglesias, Valentin; Kajava, Andrey V.; Kovacs, Orsolya P.; Lamb, John; Lambrughi, Matteo; Lazar, Tamas; Leclercq, Jeremy Y.; Leonardi, Emanuela; Macedo-Ribeiro, Sandra; Macossay-Castillo, Mauricio; Maiani, Emiliano; Manso, José A.; Marino-Buslje, Cristina; Martínez-Pérez, Elizabeth; Mészáros, Bálint; Mičetić, Ivan; Minervini, Giovanni; Murvai, Nikoletta; Necci, Marco; Ouzounis, Christos A.; Pajkos, Mátyás; Paladin, Lisanna; Pancsa, Rita; Papaleo, Elena; Parisi, Gustavo; Pasche, Emilie; Barbosa Pereira, Pedro J.; Promponas, Vasilis J.; Pujols, Jordi; Quaglia, Federica; Ruch, Patrick; Salvatore, Marco; Schad, Eva; Szabo, Beata; Szaniszló, Tamás; Tamana, Stella; Tantos, Agnes; Veljkovic, Nevena; Ventura, Salvador; Vranken, Wim; Dosztányi, Zsuzsanna; Tompa, Peter; Tosatto, Silvio C. E.; Piovesan, Damiano;The Database of Protein Disorder (DisProt, URL: https://disprot.org) provides manually curated annotations of intrinsically disordered proteins from the literature. Here we report recent developments with DisProt (version 8), including the doubling of protein entries, a new disorder ontology, improvements of the annotation format and a completely new website. The website includes a redesigned graphical interface, a better search engine, a clearer API for programmatic access and a new annotation interface that integrates text mining technologies. The new entry format provides a greater flexibility, simplifies maintenance and allows the capture of more information from the literature. The new disorder ontology has been formalized and made interoperable by adopting the OWL format, as well as its structure and term definitions have been improved. The new annotation interface has made the curation process faster and more effective. We recently showed that new DisProt annotations can be effectively used to train and validate disorder predictors. We believe the growth of DisProt will accelerate, contributing to the improvement of function and disorder predictors and therefore to illuminate the 'dark' proteome. Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT) of Argentina [PICT-2015/3367, PICT-2017/1924]; Ministry of Education, Science and Technological Development of the Republic of Serbia [ON173001]; Vetenskapsrådet [2016-03798]; Hungarian National Research, Development, and Innovation Office (NKFIH) [FK-128133]; Italian Ministry of Health Young Investigator Grant [GR-2011-02347754]; Ministerio de Economía y Competitividad (MINECO) [BIO2016-78310-R]; ICREA (ICREA-Academia 2015); Fundac¸ão para a Ciência e a Tecnologia (FCT, Portugal); European Regional Development Fund [POCI-01-0145-FEDER-031173, POCI-01-0145-FEDER-029221]; Mexican National Council of Science and Technology (CONACYT) [215503]; Elixir-GR, Action ‘Reinforcement of the Research and Innovation Infrastructure’, Operational Programme ‘Competitiveness, Entrepreneurship and Innovation’ [NSRF 2014-2020]. co-financed by Greece and the European Union (European Regional Development Fund); Hungarian Academy of Sciences [PREMIUM-2017-48]; Carlsberg Distinguished Fellowship [CF18-0314]; Danmarks Grundforskningsfond [DNRF125]; National Research, Development and Innovation Office [K-125340]; Research Foundation Flanders (FWO) [G.0328.16N]; Hungarian Academy of Sciences [LP2014-18]; OTKA [K108798 and K124670]. This project has received funding from the European Union’s Horizon 2020 research and innovation programme [778247]. Funding for open access charge: European Union’s Horizon 2020 research and innovation programme [778247]. Conflict of interest statement. None declared.
Nucleic Acids Resear... arrow_drop_down Nucleic Acids Research; Archivio istituzionale della ricerca - Università di PadovaOther literature type . Article . 2019 . Peer-reviewedLicense: CC BYEurope PubMed CentralArticle . 2019Full-Text: http://europepmc.org/articles/PMC7145575Data sources: PubMed CentralVrije Universiteit Brussel Research PortalOther literature type . 2020Data sources: Vrije Universiteit Brussel Research PortalRepositório Aberto da Universidade do PortoArticle . 2020Data sources: Repositório Aberto da Universidade do PortoOpenAIRE; Copenhagen University Research Information SystemArticle . 2020 . 2019Recolector de Ciencia Abierta, RECOLECTA; Dipòsit Digital de Documents de la UABArticle . 2020License: CC BYadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 154 citations 154 popularity Top 1% influence Top 10% impulse Top 0.1% Powered by BIP!visibility 71visibility views 71 download downloads 172 Powered bymore_vert Nucleic Acids Resear... arrow_drop_down Nucleic Acids Research; Archivio istituzionale della ricerca - Università di PadovaOther literature type . Article . 2019 . Peer-reviewedLicense: CC BYEurope PubMed CentralArticle . 2019Full-Text: http://europepmc.org/articles/PMC7145575Data sources: PubMed CentralVrije Universiteit Brussel Research PortalOther literature type . 2020Data sources: Vrije Universiteit Brussel Research PortalRepositório Aberto da Universidade do PortoArticle . 2020Data sources: Repositório Aberto da Universidade do PortoOpenAIRE; Copenhagen University Research Information SystemArticle . 2020 . 2019Recolector de Ciencia Abierta, RECOLECTA; Dipòsit Digital de Documents de la UABArticle . 2020License: CC BYadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Other literature type , Article 2019 Spain, United Kingdom, Cyprus, Belgium, Netherlands EnglishPublisher:F1000 Research Ltd Publicly fundedFunded by:WT | Protein Data Bank in Euro..., UKRI | Bioinformatics Resources ..., NIH | Identification of synapti... +2 projectsWT| Protein Data Bank in Europe - an integrated resource for 3D molecular and cellular structure. ,UKRI| Bioinformatics Resources for Circular Dichroism Spectroscopy and Structural Biology: Operation, Enhancement, Curation, and New Developments ,NIH| Identification of synaptic gene sets for CNS synapse taxonomy and antibodies for their engagement ,EC| IDPfun ,NIH| REGULATION OF HEPATIC AND PERIPHERAL GLUCOSE METABOLISMDavey, Norman E.; Babu, M. Madan; Blackledge, Martin; Bridge, Alan; Capella-Gutierrez, Salvador; Dosztanyi, Zsuzsanna; Drysdale, Rachel; Edwards, Richard J.; Elofsson, Arne; Felli, Isabella C.; Gibson, Toby J.; Gutmanas, Aleksandras; Hancock, John M.; Harrow, Jen; Higgins, Desmond; Jeffries, Cy M.; Le Mercier, Philippe; Mészáros, Balint; Necci, Marco; Notredame, Cedric; Orchard, Sandra; Ouzounis, Christos A.; Pancsa, Rita; Papaleo, Elena; Pierattelli, Roberta; Piovesan, Damiano; Promponas, Vasilis J.; Ruch, Patrick; Rustici, Gabriella; Romero, Pedro; Sarntivijai, Sirarat; Saunders, Gary; Schuler, Benjamin; Sharan, Malvika; Shields, Denis C.; Sussman, Joel L.; Tedds, Jonathan A.; Tompa, Peter; Turewicz, Michael; Vondrasek, Jiri; Vranken, Wim F.; Wallace, Bonnie Ann; Wichapong, Kanin; Tosatto, Silvio C. E.; Davey, Norman E.; Babu, M. Madan; Blackledge, Martin; Bridge, Alan; Capella-Gutierrez, Salvador; Dosztanyi, Zsuzsanna; Drysdale, Rachel; Edwards, Richard J.; Elofsson, Arne; Felli, Isabella C.; Gibson, Toby J.; Gutmanas, Aleksandras; Hancock, John M.; Harrow, Jen; Higgins, Desmond; Jeffries, Cy M.; Le Mercier, Philippe; Mészáros, Balint; Necci, Marco; Notredame, Cedric; Orchard, Sandra; Ouzounis, Christos A.; Pancsa, Rita; Papaleo, Elena; Pierattelli, Roberta; Piovesan, Damiano; Promponas, Vasilis J.; Ruch, Patrick; Rustici, Gabriella; Romero, Pedro; Sarntivijai, Sirarat; Saunders, Gary; Schuler, Benjamin; Sharan, Malvika; Shields, Denis C.; Sussman, Joel L.; Tedds, Jonathan A.; Tompa, Peter; Turewicz, Michael; Vondrasek, Jiri; Vranken, Wim F.; Wallace, Bonnie Ann; Wichapong, Kanin; Tosatto, Silvio C. E.;pmc: PMC6880265
pmid: 31824649
Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) are now recognised as major determinants in cellular regulation. This white paper presents a roadmap for future e-infrastructure developments in the field of IDP research within the ELIXIR framework. The goal of these developments is to drive the creation of high-quality tools and resources to support the identification, analysis and functional characterisation of IDPs. The roadmap is the result of a workshop titled "An intrinsically disordered protein user community proposal for ELIXIR" held at the University of Padua. The workshop, and further consultation with the members of the wider IDP community, identified the key priority areas for the roadmap including the development of standards for data annotation, storage and dissemination; integration of IDP data into the ELIXIR Core Data Resources; and the creation of benchmarking criteria for IDP-related software. Here, we discuss these areas of priority, how they can be implemented in cooperation with the ELIXIR platforms, and their connections to existing ELIXIR Communities and international consortia. The article provides a preliminary blueprint for an IDP Community in ELIXIR and is an appeal to identify and involve new stakeholders. BW was funded by a grant from the BBSRC [BB/P024092/1]. AB, SO and PLM were supported by the National Eye Institute (NEI), National Human Genome Research Institute (NHGRI), National Heart, Lung, and Blood Institute (NHLBI), National Institute of Allergy and Infectious Diseases (NIAID), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of General Medical Sciences (NIGMS), and National Institute of Mental Health (NIMH) of the National Institutes of Health under Award Number [U24HG007822] (the content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health), by European Molecular Biology Laboratory (EMBL) and by the Swiss Federal Government through the State Secretariat for Education, Research and Innovation SERI. AG was supported by the European Molecular Biology Laboratory (EMBL) and the Wellcome Trust [104948]. AE was supported by a Vetenskapsrådet grant [2016-03798]. EP was supported by Danmarks Grundforskningsfond [DNRF125] and a Carlsberg Foundation Distinguished Fellowship [CF18-0314]. JLS was supported by the Israel I-CORE Project: Integrated Structural Cell Biology and by Instruct-ERIC. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement [778247] and COST Action BM1405 NGP-net.
F1000Research arrow_drop_down F1000ResearchArticle . 2019 . Peer-reviewedFull-Text: http://europepmc.org/articles/PMC6880265Data sources: PubMed CentralGNOSIS Institutional RepositoryArticle . 2019 . Peer-reviewedData sources: GNOSIS Institutional RepositoryVrije Universiteit Brussel Research PortalOther literature type . 2019Data sources: Vrije Universiteit Brussel Research Portaladd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 12 citations 12 popularity Top 10% influence Average impulse Top 10% Powered by BIP!visibility 6visibility views 6 download downloads 84 Powered bymore_vert F1000Research arrow_drop_down F1000ResearchArticle . 2019 . Peer-reviewedFull-Text: http://europepmc.org/articles/PMC6880265Data sources: PubMed CentralGNOSIS Institutional RepositoryArticle . 2019 . Peer-reviewedData sources: GNOSIS Institutional RepositoryVrije Universiteit Brussel Research PortalOther literature type . 2019Data sources: Vrije Universiteit Brussel Research Portaladd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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description Publicationkeyboard_double_arrow_right Article , Other literature type , Preprint 2021Embargo end date: 27 Jan 2022 Spain, Italy, Belgium EnglishPublisher:Nature Research Publicly fundedFunded by:EC | IDPfun, EC | REFRACTEC| IDPfun ,EC| REFRACTIan Walsh; Dmytro Fishman; Dario Garcia-Gasulla; Tiina Titma; Gianluca Pollastri; Emidio Capriotti; Rita Casadio; Salvador Capella-Gutierrez; Davide Cirillo; Alessio Del Conte; Alexandros C. Dimopoulos; Victoria Dominguez Del Angel; Joaquin Dopazo; Piero Fariselli; José Maria Fernández; Florian Huber; Anna Kreshuk; Tom Lenaerts; Pier Luigi Martelli; Arcadi Navarro; Pilib Ó Broin; Janet Piñero; Damiano Piovesan; Martin Reczko; Francesco Ronzano; Venkata Satagopam; Castrense Savojardo; Vojtech Spiwok; Marco Antonio Tangaro; Giacomo Tartari; David Salgado; Alfonso Valencia; Federico Zambelli; Jennifer Harrow; Fotis E. Psomopoulos; Silvio C. E. Tosatto;The work of the Machine Learning Focus Group was funded by ELIXIR, the research infrastructure for life-science data. IW was funded by the A*STAR Career Development Award (project no. C210112057) from the Agency for Science, Technology and Research (A*STAR), Singapore. D.F. was supported by Estonian Research Council grants (PRG1095, PSG59 and ERA-NET TRANSCAN-2 (BioEndoCar)); Project No 2014-2020.4.01.16-0271, ELIXIR and the European Regional Development Fund through EXCITE Center of Excellence. S.C.E.T. has received funding from the European Union’s Horizon 2020 research and innovation programme under Marie Skłodowska-Curie Grant agreements No. 778247 and No. 823886, and Italian Ministry of University and Research PRIN 2017 grant 2017483NH8. "Article signat per 8 autors més 28 autors/es de l' ELIXIR Machine Learning Focus Group: Emidio Capriotti, Rita Casadio, Salvador Capella-Gutierrez, Davide Cirillo, Alessio Del Conte, Alexandros C. Dimopoulos, Victoria Dominguez Del Angel, Joaquin Dopazo, Piero Fariselli, José Maria Fernández, Florian Huber, Anna Kreshuk, Tom Lenaerts, Pier Luigi Martelli, Arcadi Navarro, Pilib Ó Broin, Janet Piñero, Damiano Piovesan, Martin Reczko, Francesco Ronzano, Venkata Satagopam, Castrense Savojardo, Vojtech Spiwok, Marco Antonio Tangaro, Giacomo Tartari, David Salgado, Alfonso Valencia & Federico Zambelli" Supervised machine learning is widely used in biology and deserves more scrutiny. We present a set of community-wide recommendations (DOME) aiming to help establish standards of supervised machine learning validation in biology. Formulated as questions, the DOME recommendations improve the assessment and reproducibility of papers when included as supplementary material. Peer Reviewed
Archivio Istituziona... arrow_drop_down Recolector de Ciencia Abierta, RECOLECTAArticle . 2021 . Peer-reviewedData sources: Recolector de Ciencia Abierta, RECOLECTAVrije Universiteit Brussel Research PortalOther literature type . 2021Data sources: Vrije Universiteit Brussel Research PortalUPCommons. Portal del coneixement obert de la UPCOther literature type . Article . 2021 . Peer-reviewedData sources: UPCommons. Portal del coneixement obert de la UPChttps://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen bronze 90 citations 90 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!visibility 89visibility views 89 download downloads 37 Powered bymore_vert Archivio Istituziona... arrow_drop_down Recolector de Ciencia Abierta, RECOLECTAArticle . 2021 . Peer-reviewedData sources: Recolector de Ciencia Abierta, RECOLECTAVrije Universiteit Brussel Research PortalOther literature type . 2021Data sources: Vrije Universiteit Brussel Research PortalUPCommons. Portal del coneixement obert de la UPCOther literature type . Article . 2021 . Peer-reviewedData sources: UPCommons. Portal del coneixement obert de la UPChttps://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2020 France, Cyprus, Italy EnglishPublisher:Oxford University Press (OUP) Funded by:EC | IDPfun, EC | chemREPEATEC| IDPfun ,EC| chemREPEATMier, Pablo; Paladin, Lisanna; Tamana, Stella; Petrosian, Sophia; Hajdu-Soltész, Borbála; Urbanek, Annika; Gruca, Aleksandra; Plewczynski, Dariusz; Grynberg, Marcin; Bernadó, Pau; Gáspári, Zoltán; Ouzounis, Christos A.; Promponas, Vasilis J.; Kajava, Andrey V.; Hancock, John M.; Tosatto, Silvio C. E.; Dosztanyi, Zsuzsanna; Andrade-Navarro, Miguel A.; Mier, Pablo; Paladin, Lisanna; Tamana, Stella; Petrosian, Sophia; Hajdu-Soltész, Borbála; Urbanek, Annika; Gruca, Aleksandra; Plewczynski, Dariusz; Grynberg, Marcin; Bernadó, Pau; Gáspári, Zoltán; Ouzounis, Christos A.; Promponas, Vasilis J.; Kajava, Andrey V.; Hancock, John M.; Tosatto, Silvio C. E.; Dosztanyi, Zsuzsanna; Andrade-Navarro, Miguel A.;Abstract There are multiple definitions for low complexity regions (LCRs) in protein sequences, with all of them broadly considering LCRs as regions with fewer amino acid types compared to an average composition. Following this view, LCRs can also be defined as regions showing composition bias. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, and more generally the overlaps between different properties related to LCRs, using examples. We argue that statistical measures alone cannot capture all structural aspects of LCRs and recommend the combined usage of a variety of predictive tools and measurements. While the methodologies available to study LCRs are already very advanced, we foresee that a more comprehensive annotation of sequences in the databases will enable the improvement of predictions and a better understanding of the evolution and the connection between structure and function of LCRs. This will require the use of standards for the generation and exchange of data describing all aspects of LCRs. Short abstract There are multiple definitions for low complexity regions (LCRs) in protein sequences. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, plus overlaps between different properties related to LCRs, using examples.
Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2019Full-Text: http://europepmc.org/articles/PMC7299295Data sources: PubMed CentralArchivio istituzionale della ricerca - Università di Padova; Briefings in BioinformaticsOther literature type . Article . 2020 . 2019 . Peer-reviewedLicense: CC BY NCadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 67 citations 67 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!more_vert Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2019Full-Text: http://europepmc.org/articles/PMC7299295Data sources: PubMed CentralArchivio istituzionale della ricerca - Università di Padova; Briefings in BioinformaticsOther literature type . Article . 2020 . 2019 . Peer-reviewedLicense: CC BY NCadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2019 Cyprus, Portugal, France, Serbia, France, Denmark, Italy, Spain, Belgium EnglishPublisher:HAL CCSD Funded by:MESTD | Application of the EIIP/I..., EC | IDPfunMESTD| Application of the EIIP/ISM bioinformatics platform in discovery of novel therapeutic targets and potential therapeutic molecules ,EC| IDPfunAuthors: Hatos, András; Hajdu-Soltész, Borbála; Monzon, Alexander M.; Palopoli, Nicolas; +124 AuthorsHatos, András; Hajdu-Soltész, Borbála; Monzon, Alexander M.; Palopoli, Nicolas; Álvarez, Lucía; Aykac-Fas, Burcu; Bassot, Claudio; Benítez, Guillermo I.; Bevilacqua, Martina; Chasapi, Anastasia; Chemes, Lucia; Davey, Norman E.; Davidović, Radoslav; Dunker, A. Keith; Elofsson, Arne; Gobeill, Julien; Foutel, Nicolás S. González; Sudha, Govindarajan; Guharoy, Mainak; Horvath, Tamas; Iglesias, Valentin; Kajava, Andrey V.; Kovacs, Orsolya P.; Lamb, John; Lambrughi, Matteo; Lazar, Tamas; Leclercq, Jeremy Y.; Leonardi, Emanuela; Macedo-Ribeiro, Sandra; Macossay-Castillo, Mauricio; Maiani, Emiliano; Manso, José A.; Marino-Buslje, Cristina; Martínez-Pérez, Elizabeth; Mészáros, Bálint; Mičetić, Ivan; Minervini, Giovanni; Murvai, Nikoletta; Necci, Marco; Ouzounis, Christos A.; Pajkos, Mátyás; Paladin, Lisanna; Pancsa, Rita; Papaleo, Elena; Parisi, Gustavo; Pasche, Emilie; Barbosa Pereira, Pedro J.; Promponas, Vasilis J.; Pujols, Jordi; Quaglia, Federica; Ruch, Patrick; Salvatore, Marco; Schad, Eva; Szabo, Beata; Szaniszló, Tamás; Tamana, Stella; Tantos, Agnes; Veljkovic, Nevena; Ventura, Salvador; Vranken, Wim; Dosztányi, Zsuzsanna; Tompa, Peter; Tosatto, Silvio C. E.; Piovesan, Damiano; Hatos, András; Hajdu-Soltész, Borbála; Monzon, Alexander M.; Palopoli, Nicolas; Álvarez, Lucía; Aykac-Fas, Burcu; Bassot, Claudio; Benítez, Guillermo I.; Bevilacqua, Martina; Chasapi, Anastasia; Chemes, Lucia; Davey, Norman E.; Davidović, Radoslav; Dunker, A. Keith; Elofsson, Arne; Gobeill, Julien; Foutel, Nicolás S. González; Sudha, Govindarajan; Guharoy, Mainak; Horvath, Tamas; Iglesias, Valentin; Kajava, Andrey V.; Kovacs, Orsolya P.; Lamb, John; Lambrughi, Matteo; Lazar, Tamas; Leclercq, Jeremy Y.; Leonardi, Emanuela; Macedo-Ribeiro, Sandra; Macossay-Castillo, Mauricio; Maiani, Emiliano; Manso, José A.; Marino-Buslje, Cristina; Martínez-Pérez, Elizabeth; Mészáros, Bálint; Mičetić, Ivan; Minervini, Giovanni; Murvai, Nikoletta; Necci, Marco; Ouzounis, Christos A.; Pajkos, Mátyás; Paladin, Lisanna; Pancsa, Rita; Papaleo, Elena; Parisi, Gustavo; Pasche, Emilie; Barbosa Pereira, Pedro J.; Promponas, Vasilis J.; Pujols, Jordi; Quaglia, Federica; Ruch, Patrick; Salvatore, Marco; Schad, Eva; Szabo, Beata; Szaniszló, Tamás; Tamana, Stella; Tantos, Agnes; Veljkovic, Nevena; Ventura, Salvador; Vranken, Wim; Dosztányi, Zsuzsanna; Tompa, Peter; Tosatto, Silvio C. E.; Piovesan, Damiano;The Database of Protein Disorder (DisProt, URL: https://disprot.org) provides manually curated annotations of intrinsically disordered proteins from the literature. Here we report recent developments with DisProt (version 8), including the doubling of protein entries, a new disorder ontology, improvements of the annotation format and a completely new website. The website includes a redesigned graphical interface, a better search engine, a clearer API for programmatic access and a new annotation interface that integrates text mining technologies. The new entry format provides a greater flexibility, simplifies maintenance and allows the capture of more information from the literature. The new disorder ontology has been formalized and made interoperable by adopting the OWL format, as well as its structure and term definitions have been improved. The new annotation interface has made the curation process faster and more effective. We recently showed that new DisProt annotations can be effectively used to train and validate disorder predictors. We believe the growth of DisProt will accelerate, contributing to the improvement of function and disorder predictors and therefore to illuminate the 'dark' proteome. Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT) of Argentina [PICT-2015/3367, PICT-2017/1924]; Ministry of Education, Science and Technological Development of the Republic of Serbia [ON173001]; Vetenskapsrådet [2016-03798]; Hungarian National Research, Development, and Innovation Office (NKFIH) [FK-128133]; Italian Ministry of Health Young Investigator Grant [GR-2011-02347754]; Ministerio de Economía y Competitividad (MINECO) [BIO2016-78310-R]; ICREA (ICREA-Academia 2015); Fundac¸ão para a Ciência e a Tecnologia (FCT, Portugal); European Regional Development Fund [POCI-01-0145-FEDER-031173, POCI-01-0145-FEDER-029221]; Mexican National Council of Science and Technology (CONACYT) [215503]; Elixir-GR, Action ‘Reinforcement of the Research and Innovation Infrastructure’, Operational Programme ‘Competitiveness, Entrepreneurship and Innovation’ [NSRF 2014-2020]. co-financed by Greece and the European Union (European Regional Development Fund); Hungarian Academy of Sciences [PREMIUM-2017-48]; Carlsberg Distinguished Fellowship [CF18-0314]; Danmarks Grundforskningsfond [DNRF125]; National Research, Development and Innovation Office [K-125340]; Research Foundation Flanders (FWO) [G.0328.16N]; Hungarian Academy of Sciences [LP2014-18]; OTKA [K108798 and K124670]. This project has received funding from the European Union’s Horizon 2020 research and innovation programme [778247]. Funding for open access charge: European Union’s Horizon 2020 research and innovation programme [778247]. Conflict of interest statement. None declared.
Nucleic Acids Resear... arrow_drop_down Nucleic Acids Research; Archivio istituzionale della ricerca - Università di PadovaOther literature type . Article . 2019 . Peer-reviewedLicense: CC BYEurope PubMed CentralArticle . 2019Full-Text: http://europepmc.org/articles/PMC7145575Data sources: PubMed CentralVrije Universiteit Brussel Research PortalOther literature type . 2020Data sources: Vrije Universiteit Brussel Research PortalRepositório Aberto da Universidade do PortoArticle . 2020Data sources: Repositório Aberto da Universidade do PortoOpenAIRE; Copenhagen University Research Information SystemArticle . 2020 . 2019Recolector de Ciencia Abierta, RECOLECTA; Dipòsit Digital de Documents de la UABArticle . 2020License: CC BYadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 154 citations 154 popularity Top 1% influence Top 10% impulse Top 0.1% Powered by BIP!visibility 71visibility views 71 download downloads 172 Powered bymore_vert Nucleic Acids Resear... arrow_drop_down Nucleic Acids Research; Archivio istituzionale della ricerca - Università di PadovaOther literature type . Article . 2019 . Peer-reviewedLicense: CC BYEurope PubMed CentralArticle . 2019Full-Text: http://europepmc.org/articles/PMC7145575Data sources: PubMed CentralVrije Universiteit Brussel Research PortalOther literature type . 2020Data sources: Vrije Universiteit Brussel Research PortalRepositório Aberto da Universidade do PortoArticle . 2020Data sources: Repositório Aberto da Universidade do PortoOpenAIRE; Copenhagen University Research Information SystemArticle . 2020 . 2019Recolector de Ciencia Abierta, RECOLECTA; Dipòsit Digital de Documents de la UABArticle . 2020License: CC BYadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Other literature type , Article 2019 Spain, United Kingdom, Cyprus, Belgium, Netherlands EnglishPublisher:F1000 Research Ltd Publicly fundedFunded by:WT | Protein Data Bank in Euro..., UKRI | Bioinformatics Resources ..., NIH | Identification of synapti... +2 projectsWT| Protein Data Bank in Europe - an integrated resource for 3D molecular and cellular structure. ,UKRI| Bioinformatics Resources for Circular Dichroism Spectroscopy and Structural Biology: Operation, Enhancement, Curation, and New Developments ,NIH| Identification of synaptic gene sets for CNS synapse taxonomy and antibodies for their engagement ,EC| IDPfun ,NIH| REGULATION OF HEPATIC AND PERIPHERAL GLUCOSE METABOLISMDavey, Norman E.; Babu, M. Madan; Blackledge, Martin; Bridge, Alan; Capella-Gutierrez, Salvador; Dosztanyi, Zsuzsanna; Drysdale, Rachel; Edwards, Richard J.; Elofsson, Arne; Felli, Isabella C.; Gibson, Toby J.; Gutmanas, Aleksandras; Hancock, John M.; Harrow, Jen; Higgins, Desmond; Jeffries, Cy M.; Le Mercier, Philippe; Mészáros, Balint; Necci, Marco; Notredame, Cedric; Orchard, Sandra; Ouzounis, Christos A.; Pancsa, Rita; Papaleo, Elena; Pierattelli, Roberta; Piovesan, Damiano; Promponas, Vasilis J.; Ruch, Patrick; Rustici, Gabriella; Romero, Pedro; Sarntivijai, Sirarat; Saunders, Gary; Schuler, Benjamin; Sharan, Malvika; Shields, Denis C.; Sussman, Joel L.; Tedds, Jonathan A.; Tompa, Peter; Turewicz, Michael; Vondrasek, Jiri; Vranken, Wim F.; Wallace, Bonnie Ann; Wichapong, Kanin; Tosatto, Silvio C. E.; Davey, Norman E.; Babu, M. Madan; Blackledge, Martin; Bridge, Alan; Capella-Gutierrez, Salvador; Dosztanyi, Zsuzsanna; Drysdale, Rachel; Edwards, Richard J.; Elofsson, Arne; Felli, Isabella C.; Gibson, Toby J.; Gutmanas, Aleksandras; Hancock, John M.; Harrow, Jen; Higgins, Desmond; Jeffries, Cy M.; Le Mercier, Philippe; Mészáros, Balint; Necci, Marco; Notredame, Cedric; Orchard, Sandra; Ouzounis, Christos A.; Pancsa, Rita; Papaleo, Elena; Pierattelli, Roberta; Piovesan, Damiano; Promponas, Vasilis J.; Ruch, Patrick; Rustici, Gabriella; Romero, Pedro; Sarntivijai, Sirarat; Saunders, Gary; Schuler, Benjamin; Sharan, Malvika; Shields, Denis C.; Sussman, Joel L.; Tedds, Jonathan A.; Tompa, Peter; Turewicz, Michael; Vondrasek, Jiri; Vranken, Wim F.; Wallace, Bonnie Ann; Wichapong, Kanin; Tosatto, Silvio C. E.;pmc: PMC6880265
pmid: 31824649
Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) are now recognised as major determinants in cellular regulation. This white paper presents a roadmap for future e-infrastructure developments in the field of IDP research within the ELIXIR framework. The goal of these developments is to drive the creation of high-quality tools and resources to support the identification, analysis and functional characterisation of IDPs. The roadmap is the result of a workshop titled "An intrinsically disordered protein user community proposal for ELIXIR" held at the University of Padua. The workshop, and further consultation with the members of the wider IDP community, identified the key priority areas for the roadmap including the development of standards for data annotation, storage and dissemination; integration of IDP data into the ELIXIR Core Data Resources; and the creation of benchmarking criteria for IDP-related software. Here, we discuss these areas of priority, how they can be implemented in cooperation with the ELIXIR platforms, and their connections to existing ELIXIR Communities and international consortia. The article provides a preliminary blueprint for an IDP Community in ELIXIR and is an appeal to identify and involve new stakeholders. BW was funded by a grant from the BBSRC [BB/P024092/1]. AB, SO and PLM were supported by the National Eye Institute (NEI), National Human Genome Research Institute (NHGRI), National Heart, Lung, and Blood Institute (NHLBI), National Institute of Allergy and Infectious Diseases (NIAID), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of General Medical Sciences (NIGMS), and National Institute of Mental Health (NIMH) of the National Institutes of Health under Award Number [U24HG007822] (the content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health), by European Molecular Biology Laboratory (EMBL) and by the Swiss Federal Government through the State Secretariat for Education, Research and Innovation SERI. AG was supported by the European Molecular Biology Laboratory (EMBL) and the Wellcome Trust [104948]. AE was supported by a Vetenskapsrådet grant [2016-03798]. EP was supported by Danmarks Grundforskningsfond [DNRF125] and a Carlsberg Foundation Distinguished Fellowship [CF18-0314]. JLS was supported by the Israel I-CORE Project: Integrated Structural Cell Biology and by Instruct-ERIC. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement [778247] and COST Action BM1405 NGP-net.
F1000Research arrow_drop_down F1000ResearchArticle . 2019 . Peer-reviewedFull-Text: http://europepmc.org/articles/PMC6880265Data sources: PubMed CentralGNOSIS Institutional RepositoryArticle . 2019 . Peer-reviewedData sources: GNOSIS Institutional RepositoryVrije Universiteit Brussel Research PortalOther literature type . 2019Data sources: Vrije Universiteit Brussel Research Portaladd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 12 citations 12 popularity Top 10% influence Average impulse Top 10% Powered by BIP!visibility 6visibility views 6 download downloads 84 Powered bymore_vert F1000Research arrow_drop_down F1000ResearchArticle . 2019 . Peer-reviewedFull-Text: http://europepmc.org/articles/PMC6880265Data sources: PubMed CentralGNOSIS Institutional RepositoryArticle . 2019 . Peer-reviewedData sources: GNOSIS Institutional RepositoryVrije Universiteit Brussel Research PortalOther literature type . 2019Data sources: Vrije Universiteit Brussel Research Portaladd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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