Comparing 22 Popular Phosphoproteomics Pipelines for Peptide Identification and Site Localization

Research output: Contribution to journalJournal articlepeer-review

Standard

Comparing 22 Popular Phosphoproteomics Pipelines for Peptide Identification and Site Localization. / Locard-Paulet, Marie; Bouyssié, David; Froment, Carine; Burlet-Schiltz, Odile; Jensen, Lars J.

In: Journal of Proteome Research, Vol. 19, No. 3, 2020, p. 1338-1345.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Locard-Paulet, M, Bouyssié, D, Froment, C, Burlet-Schiltz, O & Jensen, LJ 2020, 'Comparing 22 Popular Phosphoproteomics Pipelines for Peptide Identification and Site Localization', Journal of Proteome Research, vol. 19, no. 3, pp. 1338-1345. https://doi.org/10.1021/acs.jproteome.9b00679

APA

Locard-Paulet, M., Bouyssié, D., Froment, C., Burlet-Schiltz, O., & Jensen, L. J. (2020). Comparing 22 Popular Phosphoproteomics Pipelines for Peptide Identification and Site Localization. Journal of Proteome Research, 19(3), 1338-1345. https://doi.org/10.1021/acs.jproteome.9b00679

Vancouver

Locard-Paulet M, Bouyssié D, Froment C, Burlet-Schiltz O, Jensen LJ. Comparing 22 Popular Phosphoproteomics Pipelines for Peptide Identification and Site Localization. Journal of Proteome Research. 2020;19(3):1338-1345. https://doi.org/10.1021/acs.jproteome.9b00679

Author

Locard-Paulet, Marie ; Bouyssié, David ; Froment, Carine ; Burlet-Schiltz, Odile ; Jensen, Lars J. / Comparing 22 Popular Phosphoproteomics Pipelines for Peptide Identification and Site Localization. In: Journal of Proteome Research. 2020 ; Vol. 19, No. 3. pp. 1338-1345.

Bibtex

@article{91d2ae469a7c4c788f7fc6508cca1bd2,
title = "Comparing 22 Popular Phosphoproteomics Pipelines for Peptide Identification and Site Localization",
abstract = "Phosphorylation-driven cell signaling governs most biological functions and is widely studied using mass-spectrometry-based phosphoproteomics. Identifying the peptides and localizing the phosphorylation sites within them from the raw data is challenging and can be performed by several algorithms that return scores that are not directly comparable. This increases the heterogeneity among published phosphoproteomics data sets and prevents their direct integration. Here we compare 22 pipelines implemented in the main software tools used for bottom-up phosphoproteomics analysis (MaxQuant, Proteome Discoverer, PeptideShaker). We test six search engines (Andromeda, Comet, Mascot, MS Amanda, SequestHT, and X!Tandem) in combination with several localization scoring algorithms (delta score, D-score, PTM-score, phosphoRS, and Ascore). We show that these follow very different score distributions, which can lead to different false localization rates for the same threshold. We provide a strategy to discriminate correctly from incorrectly localized phosphorylation sites in a consistent manner across the tested pipelines. The results presented here can help users choose the most appropriate pipeline and cutoffs for their phosphoproteomics analysis.",
author = "Marie Locard-Paulet and David Bouyssi{\'e} and Carine Froment and Odile Burlet-Schiltz and Jensen, {Lars J.}",
year = "2020",
doi = "10.1021/acs.jproteome.9b00679",
language = "English",
volume = "19",
pages = "1338--1345",
journal = "Journal of Proteome Research",
issn = "1535-3893",
publisher = "American Chemical Society",
number = "3",

}

RIS

TY - JOUR

T1 - Comparing 22 Popular Phosphoproteomics Pipelines for Peptide Identification and Site Localization

AU - Locard-Paulet, Marie

AU - Bouyssié, David

AU - Froment, Carine

AU - Burlet-Schiltz, Odile

AU - Jensen, Lars J.

PY - 2020

Y1 - 2020

N2 - Phosphorylation-driven cell signaling governs most biological functions and is widely studied using mass-spectrometry-based phosphoproteomics. Identifying the peptides and localizing the phosphorylation sites within them from the raw data is challenging and can be performed by several algorithms that return scores that are not directly comparable. This increases the heterogeneity among published phosphoproteomics data sets and prevents their direct integration. Here we compare 22 pipelines implemented in the main software tools used for bottom-up phosphoproteomics analysis (MaxQuant, Proteome Discoverer, PeptideShaker). We test six search engines (Andromeda, Comet, Mascot, MS Amanda, SequestHT, and X!Tandem) in combination with several localization scoring algorithms (delta score, D-score, PTM-score, phosphoRS, and Ascore). We show that these follow very different score distributions, which can lead to different false localization rates for the same threshold. We provide a strategy to discriminate correctly from incorrectly localized phosphorylation sites in a consistent manner across the tested pipelines. The results presented here can help users choose the most appropriate pipeline and cutoffs for their phosphoproteomics analysis.

AB - Phosphorylation-driven cell signaling governs most biological functions and is widely studied using mass-spectrometry-based phosphoproteomics. Identifying the peptides and localizing the phosphorylation sites within them from the raw data is challenging and can be performed by several algorithms that return scores that are not directly comparable. This increases the heterogeneity among published phosphoproteomics data sets and prevents their direct integration. Here we compare 22 pipelines implemented in the main software tools used for bottom-up phosphoproteomics analysis (MaxQuant, Proteome Discoverer, PeptideShaker). We test six search engines (Andromeda, Comet, Mascot, MS Amanda, SequestHT, and X!Tandem) in combination with several localization scoring algorithms (delta score, D-score, PTM-score, phosphoRS, and Ascore). We show that these follow very different score distributions, which can lead to different false localization rates for the same threshold. We provide a strategy to discriminate correctly from incorrectly localized phosphorylation sites in a consistent manner across the tested pipelines. The results presented here can help users choose the most appropriate pipeline and cutoffs for their phosphoproteomics analysis.

U2 - 10.1021/acs.jproteome.9b00679

DO - 10.1021/acs.jproteome.9b00679

M3 - Journal article

C2 - 31975593

VL - 19

SP - 1338

EP - 1345

JO - Journal of Proteome Research

JF - Journal of Proteome Research

SN - 1535-3893

IS - 3

ER -

ID: 240408550