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dc.contributor.author
Trolle, Thomas
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dc.contributor.author
Metushi, Imir G.
dc.contributor.author
Greenbaum, Jason A.
dc.contributor.author
Kim, Yohan
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dc.contributor.author
Sidney, John
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dc.contributor.author
Lund, Ole
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dc.contributor.author
Sette, Alessandro
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dc.contributor.author
Peters, Bjoern
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dc.contributor.author
Nielsen, Morten
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dc.date.available
2018-03-07T19:41:40Z
dc.date.issued
2015-07
dc.identifier.citation
Trolle, Thomas; Metushi, Imir G.; Greenbaum, Jason A.; Kim, Yohan; Sidney, John; et al.; Automated benchmarking of peptide-MHC class i binding predictions; Oxford University Press; Bioinformatics (Oxford, England); 31; 13; 7-2015; 2174-2181
dc.identifier.issn
1367-4803
dc.identifier.uri
http://hdl.handle.net/11336/38180
dc.description.abstract
Motivation: Numerous in silico methods predicting peptide binding to major histocompatibility complex (MHC) class I molecules have been developed over the last decades. However, the multitude of available prediction tools makes it non-trivial for the end-user to select which tool to use for a given task. To provide a solid basis on which to compare different prediction tools, we here describe a framework for the automated benchmarking of peptide-MHC class I binding prediction tools. The framework runs weekly benchmarks on data that are newly entered into the Immune Epitope Database (IEDB), giving the public access to frequent, up-to-date performance evaluations of all participating tools. To overcome potential selection bias in the data included in the IEDB, a strategy was implemented that suggests a set of peptides for which different prediction methods give divergent predictions as to their binding capability. Upon experimental binding validation, these peptides entered the benchmark study. Results: The benchmark has run for 15 weeks and includes evaluation of 44 datasets covering 17 MHC alleles and more than 4000 peptide-MHC binding measurements. Inspection of the results allows the end-user to make educated selections between participating tools. Of the four participating servers, NetMHCpan performed the best, followed by ANN, SMM and finally ARB. Availability and implementation: Up-to-date performance evaluations of each server can be found online at http://tools.iedb.org/auto-bench/mhci/weekly. All prediction tool developers are invited to participate in the benchmark. Sign-up instructions are available at http://tools.iedb.org/auto-bench/mhci/join.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Oxford University Press
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Mhc
dc.subject
Benchmark
dc.subject.classification
Ciencias de la Computación
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dc.subject.classification
Ciencias de la Computación e Información
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
Automated benchmarking of peptide-MHC class i binding predictions
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2018-03-06T17:44:03Z
dc.journal.volume
31
dc.journal.number
13
dc.journal.pagination
2174-2181
dc.journal.pais
Reino Unido
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dc.journal.ciudad
Oxford
dc.description.fil
Fil: Trolle, Thomas. Technical University of Denmark; Dinamarca
dc.description.fil
Fil: Metushi, Imir G.. La Jolla Institute for Allergy and Immunology; Estados Unidos
dc.description.fil
Fil: Greenbaum, Jason A.. La Jolla Institute for Allergy and Immunology; Estados Unidos
dc.description.fil
Fil: Kim, Yohan. La Jolla Institute for Allergy and Immunology; Estados Unidos
dc.description.fil
Fil: Sidney, John. La Jolla Institute for Allergy and Immunology; Estados Unidos
dc.description.fil
Fil: Lund, Ole. Technical University of Denmark; Dinamarca
dc.description.fil
Fil: Sette, Alessandro. La Jolla Institute for Allergy and Immunology; Estados Unidos
dc.description.fil
Fil: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados Unidos
dc.description.fil
Fil: Nielsen, Morten. Technical University of Denmark; Dinamarca. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina
dc.journal.title
Bioinformatics (Oxford, England)
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1093/bioinformatics/btv123
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/bioinformatics/article/31/13/2174/196331
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