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IceR improves proteome coverage and data completeness in global and single-cell proteomics
Label-free proteomics by data-dependent acquisition enables the unbiased quantification of thousands of proteins, however it notoriously suffers from high rates of missing values, thus prohibiting consistent protein quantification across large sample cohorts. To solve this, we here present IceR (Ion...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8352929/ https://www.ncbi.nlm.nih.gov/pubmed/34373457 http://dx.doi.org/10.1038/s41467-021-25077-6 |
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author | Kalxdorf, Mathias Müller, Torsten Stegle, Oliver Krijgsveld, Jeroen |
author_facet | Kalxdorf, Mathias Müller, Torsten Stegle, Oliver Krijgsveld, Jeroen |
author_sort | Kalxdorf, Mathias |
collection | PubMed |
description | Label-free proteomics by data-dependent acquisition enables the unbiased quantification of thousands of proteins, however it notoriously suffers from high rates of missing values, thus prohibiting consistent protein quantification across large sample cohorts. To solve this, we here present IceR (Ion current extraction Re-quantification), an efficient and user-friendly quantification workflow that combines high identification rates of data-dependent acquisition with low missing value rates similar to data-independent acquisition. Specifically, IceR uses ion current information for a hybrid peptide identification propagation approach with superior quantification precision, accuracy, reliability and data completeness compared to other quantitative workflows. Applied to plasma and single-cell proteomics data, IceR enhanced the number of reliably quantified proteins, improved discriminability between single-cell populations, and allowed reconstruction of a developmental trajectory. IceR will be useful to improve performance of large scale global as well as low-input proteomics applications, facilitated by its availability as an easy-to-use R-package. |
format | Online Article Text |
id | pubmed-8352929 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-83529292021-08-19 IceR improves proteome coverage and data completeness in global and single-cell proteomics Kalxdorf, Mathias Müller, Torsten Stegle, Oliver Krijgsveld, Jeroen Nat Commun Article Label-free proteomics by data-dependent acquisition enables the unbiased quantification of thousands of proteins, however it notoriously suffers from high rates of missing values, thus prohibiting consistent protein quantification across large sample cohorts. To solve this, we here present IceR (Ion current extraction Re-quantification), an efficient and user-friendly quantification workflow that combines high identification rates of data-dependent acquisition with low missing value rates similar to data-independent acquisition. Specifically, IceR uses ion current information for a hybrid peptide identification propagation approach with superior quantification precision, accuracy, reliability and data completeness compared to other quantitative workflows. Applied to plasma and single-cell proteomics data, IceR enhanced the number of reliably quantified proteins, improved discriminability between single-cell populations, and allowed reconstruction of a developmental trajectory. IceR will be useful to improve performance of large scale global as well as low-input proteomics applications, facilitated by its availability as an easy-to-use R-package. Nature Publishing Group UK 2021-08-09 /pmc/articles/PMC8352929/ /pubmed/34373457 http://dx.doi.org/10.1038/s41467-021-25077-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Kalxdorf, Mathias Müller, Torsten Stegle, Oliver Krijgsveld, Jeroen IceR improves proteome coverage and data completeness in global and single-cell proteomics |
title | IceR improves proteome coverage and data completeness in global and single-cell proteomics |
title_full | IceR improves proteome coverage and data completeness in global and single-cell proteomics |
title_fullStr | IceR improves proteome coverage and data completeness in global and single-cell proteomics |
title_full_unstemmed | IceR improves proteome coverage and data completeness in global and single-cell proteomics |
title_short | IceR improves proteome coverage and data completeness in global and single-cell proteomics |
title_sort | icer improves proteome coverage and data completeness in global and single-cell proteomics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8352929/ https://www.ncbi.nlm.nih.gov/pubmed/34373457 http://dx.doi.org/10.1038/s41467-021-25077-6 |
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