Artículo
Automated benchmarking of peptide-MHC class i binding predictions
Trolle, Thomas; Metushi, Imir G.; Greenbaum, Jason A.; Kim, Yohan; Sidney, John; Lund, Ole; Sette, Alessandro; Peters, Bjoern; Nielsen, Morten
Fecha de publicación:
07/2015
Editorial:
Oxford University Press
Revista:
Bioinformatics (Oxford, England)
ISSN:
1367-4803
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
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.
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Articulos(IIB-INTECH)
Articulos de INST.DE INVEST.BIOTECNOLOGICAS - INSTITUTO TECNOLOGICO CHASCOMUS
Articulos de INST.DE INVEST.BIOTECNOLOGICAS - INSTITUTO TECNOLOGICO CHASCOMUS
Citación
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
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