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Comparative Evaluation of MaxQuant and Proteome Discoverer MS1-Based Protein Quantification Tools

[Image: see text] MS1-based label-free quantification can compare precursor ion peaks across runs, allowing reproducible protein measurements. Among bioinformatic platforms enabling MS1-based quantification, MaxQuant (MQ) is one of the most used, while Proteome Discoverer (PD) has recently introduce...

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Autores principales: Palomba, Antonio, Abbondio, Marcello, Fiorito, Giovanni, Uzzau, Sergio, Pagnozzi, Daniela, Tanca, Alessandro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2021
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8280745/
https://www.ncbi.nlm.nih.gov/pubmed/34038140
http://dx.doi.org/10.1021/acs.jproteome.1c00143
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author Palomba, Antonio
Abbondio, Marcello
Fiorito, Giovanni
Uzzau, Sergio
Pagnozzi, Daniela
Tanca, Alessandro
author_facet Palomba, Antonio
Abbondio, Marcello
Fiorito, Giovanni
Uzzau, Sergio
Pagnozzi, Daniela
Tanca, Alessandro
author_sort Palomba, Antonio
collection PubMed
description [Image: see text] MS1-based label-free quantification can compare precursor ion peaks across runs, allowing reproducible protein measurements. Among bioinformatic platforms enabling MS1-based quantification, MaxQuant (MQ) is one of the most used, while Proteome Discoverer (PD) has recently introduced the Minora tool. Here, we present a comparative evaluation of six MS1-based quantification methods available in MQ and PD. Intensity (MQ and PD) and area (PD only) of the precursor ion peaks were measured and then subjected or not to normalization. The six methods were applied to data sets simulating various differential proteomics scenarios and covering a wide range of protein abundance ratios and amounts. PD outperformed MQ in terms of quantification yield, dynamic range, and reproducibility, although neither platform reached a fully satisfactory quality of measurements at low-abundance ranges. PD methods including normalization were the most accurate in estimating the abundance ratio between groups and the most sensitive when comparing groups with a narrow abundance ratio; on the contrary, MQ methods generally reached slightly higher specificity, accuracy, and precision values. Moreover, we found that applying an optimized log ratio-based threshold can maximize specificity, accuracy, and precision. Taken together, these results can help researchers choose the most appropriate MS1-based protein quantification strategy for their studies.
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spelling pubmed-82807452021-07-16 Comparative Evaluation of MaxQuant and Proteome Discoverer MS1-Based Protein Quantification Tools Palomba, Antonio Abbondio, Marcello Fiorito, Giovanni Uzzau, Sergio Pagnozzi, Daniela Tanca, Alessandro J Proteome Res [Image: see text] MS1-based label-free quantification can compare precursor ion peaks across runs, allowing reproducible protein measurements. Among bioinformatic platforms enabling MS1-based quantification, MaxQuant (MQ) is one of the most used, while Proteome Discoverer (PD) has recently introduced the Minora tool. Here, we present a comparative evaluation of six MS1-based quantification methods available in MQ and PD. Intensity (MQ and PD) and area (PD only) of the precursor ion peaks were measured and then subjected or not to normalization. The six methods were applied to data sets simulating various differential proteomics scenarios and covering a wide range of protein abundance ratios and amounts. PD outperformed MQ in terms of quantification yield, dynamic range, and reproducibility, although neither platform reached a fully satisfactory quality of measurements at low-abundance ranges. PD methods including normalization were the most accurate in estimating the abundance ratio between groups and the most sensitive when comparing groups with a narrow abundance ratio; on the contrary, MQ methods generally reached slightly higher specificity, accuracy, and precision values. Moreover, we found that applying an optimized log ratio-based threshold can maximize specificity, accuracy, and precision. Taken together, these results can help researchers choose the most appropriate MS1-based protein quantification strategy for their studies. American Chemical Society 2021-05-26 2021-07-02 /pmc/articles/PMC8280745/ /pubmed/34038140 http://dx.doi.org/10.1021/acs.jproteome.1c00143 Text en © 2021 The Authors. Published by American Chemical Society Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Palomba, Antonio
Abbondio, Marcello
Fiorito, Giovanni
Uzzau, Sergio
Pagnozzi, Daniela
Tanca, Alessandro
Comparative Evaluation of MaxQuant and Proteome Discoverer MS1-Based Protein Quantification Tools
title Comparative Evaluation of MaxQuant and Proteome Discoverer MS1-Based Protein Quantification Tools
title_full Comparative Evaluation of MaxQuant and Proteome Discoverer MS1-Based Protein Quantification Tools
title_fullStr Comparative Evaluation of MaxQuant and Proteome Discoverer MS1-Based Protein Quantification Tools
title_full_unstemmed Comparative Evaluation of MaxQuant and Proteome Discoverer MS1-Based Protein Quantification Tools
title_short Comparative Evaluation of MaxQuant and Proteome Discoverer MS1-Based Protein Quantification Tools
title_sort comparative evaluation of maxquant and proteome discoverer ms1-based protein quantification tools
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8280745/
https://www.ncbi.nlm.nih.gov/pubmed/34038140
http://dx.doi.org/10.1021/acs.jproteome.1c00143
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