Cargando…
Statistical control of peptide and protein error rates in large-scale targeted DIA analyses
Liquid chromatography coupled to tandem mass spectrometry is the main method for high-throughput identification and quantification of peptides and inferred proteins. Within this field, data-independent acquisition (DIA) combined with peptide-centric scoring, exemplified by SWATH-MS, emerged as a sca...
Autores principales: | , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5581544/ https://www.ncbi.nlm.nih.gov/pubmed/28825704 http://dx.doi.org/10.1038/nmeth.4398 |
_version_ | 1783261070467530752 |
---|---|
author | Rosenberger, George Bludau, Isabell Schmitt, Uwe Heusel, Moritz Hunter, Christie Liu, Yansheng MacCoss, Michael J. MacLean, Brendan X. Nesvizhskii, Alexey I. Pedrioli, Patrick G. A. Reiter, Lukas Röst, Hannes L. Tate, Stephen Ting, Ying S. Collins, Ben C. Aebersold, Ruedi |
author_facet | Rosenberger, George Bludau, Isabell Schmitt, Uwe Heusel, Moritz Hunter, Christie Liu, Yansheng MacCoss, Michael J. MacLean, Brendan X. Nesvizhskii, Alexey I. Pedrioli, Patrick G. A. Reiter, Lukas Röst, Hannes L. Tate, Stephen Ting, Ying S. Collins, Ben C. Aebersold, Ruedi |
author_sort | Rosenberger, George |
collection | PubMed |
description | Liquid chromatography coupled to tandem mass spectrometry is the main method for high-throughput identification and quantification of peptides and inferred proteins. Within this field, data-independent acquisition (DIA) combined with peptide-centric scoring, exemplified by SWATH-MS, emerged as a scalable method to achieve deep and consistent proteome coverage across large-scale datasets. Here we discuss the adaptation of statistical concepts developed for discovery proteomics based on spectrum-centric scoring to large-scale DIA experiments analyzed with peptide-centric scoring strategies and provide guidance on their application. We show that optimal tradeoffs between sensitivity and specificity require careful considerations of the relationship between proteins in the samples and proteins represented in the spectral library. We propose the application of a global analyte constraint to prevent accumulation of false positives across large-scale datasets. Furthermore, to increase the quality and reproducibility of published proteomic results, well-established confidence criteria should be reported for detected peptide queries, peptides and inferred proteins. |
format | Online Article Text |
id | pubmed-5581544 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
record_format | MEDLINE/PubMed |
spelling | pubmed-55815442018-02-21 Statistical control of peptide and protein error rates in large-scale targeted DIA analyses Rosenberger, George Bludau, Isabell Schmitt, Uwe Heusel, Moritz Hunter, Christie Liu, Yansheng MacCoss, Michael J. MacLean, Brendan X. Nesvizhskii, Alexey I. Pedrioli, Patrick G. A. Reiter, Lukas Röst, Hannes L. Tate, Stephen Ting, Ying S. Collins, Ben C. Aebersold, Ruedi Nat Methods Article Liquid chromatography coupled to tandem mass spectrometry is the main method for high-throughput identification and quantification of peptides and inferred proteins. Within this field, data-independent acquisition (DIA) combined with peptide-centric scoring, exemplified by SWATH-MS, emerged as a scalable method to achieve deep and consistent proteome coverage across large-scale datasets. Here we discuss the adaptation of statistical concepts developed for discovery proteomics based on spectrum-centric scoring to large-scale DIA experiments analyzed with peptide-centric scoring strategies and provide guidance on their application. We show that optimal tradeoffs between sensitivity and specificity require careful considerations of the relationship between proteins in the samples and proteins represented in the spectral library. We propose the application of a global analyte constraint to prevent accumulation of false positives across large-scale datasets. Furthermore, to increase the quality and reproducibility of published proteomic results, well-established confidence criteria should be reported for detected peptide queries, peptides and inferred proteins. 2017-08-21 2017-09 /pmc/articles/PMC5581544/ /pubmed/28825704 http://dx.doi.org/10.1038/nmeth.4398 Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms |
spellingShingle | Article Rosenberger, George Bludau, Isabell Schmitt, Uwe Heusel, Moritz Hunter, Christie Liu, Yansheng MacCoss, Michael J. MacLean, Brendan X. Nesvizhskii, Alexey I. Pedrioli, Patrick G. A. Reiter, Lukas Röst, Hannes L. Tate, Stephen Ting, Ying S. Collins, Ben C. Aebersold, Ruedi Statistical control of peptide and protein error rates in large-scale targeted DIA analyses |
title | Statistical control of peptide and protein error rates in large-scale targeted DIA analyses |
title_full | Statistical control of peptide and protein error rates in large-scale targeted DIA analyses |
title_fullStr | Statistical control of peptide and protein error rates in large-scale targeted DIA analyses |
title_full_unstemmed | Statistical control of peptide and protein error rates in large-scale targeted DIA analyses |
title_short | Statistical control of peptide and protein error rates in large-scale targeted DIA analyses |
title_sort | statistical control of peptide and protein error rates in large-scale targeted dia analyses |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5581544/ https://www.ncbi.nlm.nih.gov/pubmed/28825704 http://dx.doi.org/10.1038/nmeth.4398 |
work_keys_str_mv | AT rosenbergergeorge statisticalcontrolofpeptideandproteinerrorratesinlargescaletargeteddiaanalyses AT bludauisabell statisticalcontrolofpeptideandproteinerrorratesinlargescaletargeteddiaanalyses AT schmittuwe statisticalcontrolofpeptideandproteinerrorratesinlargescaletargeteddiaanalyses AT heuselmoritz statisticalcontrolofpeptideandproteinerrorratesinlargescaletargeteddiaanalyses AT hunterchristie statisticalcontrolofpeptideandproteinerrorratesinlargescaletargeteddiaanalyses AT liuyansheng statisticalcontrolofpeptideandproteinerrorratesinlargescaletargeteddiaanalyses AT maccossmichaelj statisticalcontrolofpeptideandproteinerrorratesinlargescaletargeteddiaanalyses AT macleanbrendanx statisticalcontrolofpeptideandproteinerrorratesinlargescaletargeteddiaanalyses AT nesvizhskiialexeyi statisticalcontrolofpeptideandproteinerrorratesinlargescaletargeteddiaanalyses AT pedriolipatrickga statisticalcontrolofpeptideandproteinerrorratesinlargescaletargeteddiaanalyses AT reiterlukas statisticalcontrolofpeptideandproteinerrorratesinlargescaletargeteddiaanalyses AT rosthannesl statisticalcontrolofpeptideandproteinerrorratesinlargescaletargeteddiaanalyses AT tatestephen statisticalcontrolofpeptideandproteinerrorratesinlargescaletargeteddiaanalyses AT tingyings statisticalcontrolofpeptideandproteinerrorratesinlargescaletargeteddiaanalyses AT collinsbenc statisticalcontrolofpeptideandproteinerrorratesinlargescaletargeteddiaanalyses AT aebersoldruedi statisticalcontrolofpeptideandproteinerrorratesinlargescaletargeteddiaanalyses |