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dc.contributor.author
Andreatta, Massimo  
dc.contributor.author
Nicastri, Annalisa  
dc.contributor.author
Peng, Xu  
dc.contributor.author
Hancock, Gemma  
dc.contributor.author
Dorrell, Lucy  
dc.contributor.author
Ternette, Nicola  
dc.contributor.author
Nielsen, Morten  
dc.date.available
2021-02-03T13:59:01Z  
dc.date.issued
2019-02  
dc.identifier.citation
Andreatta, Massimo; Nicastri, Annalisa; Peng, Xu; Hancock, Gemma; Dorrell, Lucy; et al.; MS-Rescue: A Computational Pipeline to Increase the Quality and Yield of Immunopeptidomics Experiments; Wiley VCH Verlag; Proteomics (weinheim. Print); 19; 4; 2-2019; 1-7  
dc.identifier.issn
1615-9853  
dc.identifier.uri
http://hdl.handle.net/11336/124583  
dc.description.abstract
LC–MS/MS has become the standard platform for the characterization of immunopeptidomes, the collection of peptides naturally presented by major histocompatibility complex molecules to the cell surface. The protocols and algorithms used for immunopeptidomics data analysis are based on tools developed for traditional bottom-up proteomics that address the identification of peptides generated by tryptic digestion. Such algorithms are generally not tailored to the specific requirements of MHC ligand identification and, as a consequence, immunopeptidomics datasets suffer from dismissal of informative spectral information and high false discovery rates. Here, a new pipeline for the refinement of peptide-spectrum matches (PSM) is proposed, based on the assumption that immunopeptidomes contain a limited number of recurring peptide motifs, corresponding to MHC specificities. Sequence motifs are learned directly from the individual peptidome by training a prediction model on high-confidence PSMs. The model is then applied to PSM candidates with lower confidence, and sequences that score significantly higher than random peptides are rescued as likely true ligands. The pipeline is applied to MHC class I immunopeptidomes from three different species, and it is shown that it can increase the number of identified ligands by up to 20–30%, while effectively removing false positives and products of co-precipitation. Spectral validation using synthetic peptides confirms the identity of a large proportion of rescued ligands in the experimental peptidome.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Wiley VCH Verlag  
dc.rights
info:eu-repo/semantics/restrictedAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
MACHINE LEARNING  
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MASS SPECTROMETRY  
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MHC  
dc.subject
PEPTIDOME  
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SEQUENCE MOTIFS  
dc.subject.classification
Otras Ciencias de la Salud  
dc.subject.classification
Ciencias de la Salud  
dc.subject.classification
CIENCIAS MÉDICAS Y DE LA SALUD  
dc.title
MS-Rescue: A Computational Pipeline to Increase the Quality and Yield of Immunopeptidomics Experiments  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2020-11-11T12:38:56Z  
dc.journal.volume
19  
dc.journal.number
4  
dc.journal.pagination
1-7  
dc.journal.pais
Alemania  
dc.journal.ciudad
Weinheim  
dc.description.fil
Fil: Andreatta, Massimo. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; Argentina  
dc.description.fil
Fil: Nicastri, Annalisa. University of Oxford; Reino Unido  
dc.description.fil
Fil: Peng, Xu. University of Oxford; Reino Unido  
dc.description.fil
Fil: Hancock, Gemma. University of Oxford; Reino Unido  
dc.description.fil
Fil: Dorrell, Lucy. University of Oxford; Reino Unido  
dc.description.fil
Fil: Ternette, Nicola. University of Oxford; Reino Unido  
dc.description.fil
Fil: Nielsen, Morten. Technical University of Denmark; Dinamarca. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; Argentina  
dc.journal.title
Proteomics (weinheim. Print)  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1002/pmic.201800357