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dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts

The dia-PASEF technology uses ion mobility separation to reduce signal interferences and increase sensitivity in proteomic experiments. Here we present a two-dimensional peak-picking algorithm and generation of optimized spectral libraries, as well as take advantage of neural network-based processin...

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Autores principales: Demichev, Vadim, Szyrwiel, Lukasz, Yu, Fengchao, Teo, Guo Ci, Rosenberger, George, Niewienda, Agathe, Ludwig, Daniela, Decker, Jens, Kaspar-Schoenefeld, Stephanie, Lilley, Kathryn S., Mülleder, Michael, Nesvizhskii, Alexey I., Ralser, Markus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9270362/
https://www.ncbi.nlm.nih.gov/pubmed/35803928
http://dx.doi.org/10.1038/s41467-022-31492-0
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author Demichev, Vadim
Szyrwiel, Lukasz
Yu, Fengchao
Teo, Guo Ci
Rosenberger, George
Niewienda, Agathe
Ludwig, Daniela
Decker, Jens
Kaspar-Schoenefeld, Stephanie
Lilley, Kathryn S.
Mülleder, Michael
Nesvizhskii, Alexey I.
Ralser, Markus
author_facet Demichev, Vadim
Szyrwiel, Lukasz
Yu, Fengchao
Teo, Guo Ci
Rosenberger, George
Niewienda, Agathe
Ludwig, Daniela
Decker, Jens
Kaspar-Schoenefeld, Stephanie
Lilley, Kathryn S.
Mülleder, Michael
Nesvizhskii, Alexey I.
Ralser, Markus
author_sort Demichev, Vadim
collection PubMed
description The dia-PASEF technology uses ion mobility separation to reduce signal interferences and increase sensitivity in proteomic experiments. Here we present a two-dimensional peak-picking algorithm and generation of optimized spectral libraries, as well as take advantage of neural network-based processing of dia-PASEF data. Our computational platform boosts proteomic depth by up to 83% compared to previous work, and is specifically beneficial for fast proteomic experiments and those with low sample amounts. It quantifies over 5300 proteins in single injections recorded at 200 samples per day throughput using Evosep One chromatography system on a timsTOF Pro mass spectrometer and almost 9000 proteins in single injections recorded with a 93-min nanoflow gradient on timsTOF Pro 2, from 200 ng of HeLa peptides. A user-friendly implementation is provided through the incorporation of the algorithms in the DIA-NN software and by the FragPipe workflow for spectral library generation.
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spelling pubmed-92703622022-07-10 dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts Demichev, Vadim Szyrwiel, Lukasz Yu, Fengchao Teo, Guo Ci Rosenberger, George Niewienda, Agathe Ludwig, Daniela Decker, Jens Kaspar-Schoenefeld, Stephanie Lilley, Kathryn S. Mülleder, Michael Nesvizhskii, Alexey I. Ralser, Markus Nat Commun Article The dia-PASEF technology uses ion mobility separation to reduce signal interferences and increase sensitivity in proteomic experiments. Here we present a two-dimensional peak-picking algorithm and generation of optimized spectral libraries, as well as take advantage of neural network-based processing of dia-PASEF data. Our computational platform boosts proteomic depth by up to 83% compared to previous work, and is specifically beneficial for fast proteomic experiments and those with low sample amounts. It quantifies over 5300 proteins in single injections recorded at 200 samples per day throughput using Evosep One chromatography system on a timsTOF Pro mass spectrometer and almost 9000 proteins in single injections recorded with a 93-min nanoflow gradient on timsTOF Pro 2, from 200 ng of HeLa peptides. A user-friendly implementation is provided through the incorporation of the algorithms in the DIA-NN software and by the FragPipe workflow for spectral library generation. Nature Publishing Group UK 2022-07-08 /pmc/articles/PMC9270362/ /pubmed/35803928 http://dx.doi.org/10.1038/s41467-022-31492-0 Text en © The Author(s) 2022 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
Demichev, Vadim
Szyrwiel, Lukasz
Yu, Fengchao
Teo, Guo Ci
Rosenberger, George
Niewienda, Agathe
Ludwig, Daniela
Decker, Jens
Kaspar-Schoenefeld, Stephanie
Lilley, Kathryn S.
Mülleder, Michael
Nesvizhskii, Alexey I.
Ralser, Markus
dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts
title dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts
title_full dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts
title_fullStr dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts
title_full_unstemmed dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts
title_short dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts
title_sort dia-pasef data analysis using fragpipe and dia-nn for deep proteomics of low sample amounts
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9270362/
https://www.ncbi.nlm.nih.gov/pubmed/35803928
http://dx.doi.org/10.1038/s41467-022-31492-0
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