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

被引:41
作者
Palomba, Antonio [1 ]
Abbondio, Marcello [2 ]
Fiorito, Giovanni [2 ,3 ]
Uzzau, Sergio [2 ]
Pagnozzi, Daniela [1 ]
Tanca, Alessandro [2 ]
机构
[1] Porto Conte Ric, I-07041 Alghero, Italy
[2] Univ Sassari, Dept Biomed Sci, I-07100 Sassari, Italy
[3] Imperial Coll London, MRC Ctr Environm & Hlth, London W2 1PG, England
关键词
accuracy; differential analysis; label-free quantification; log ratio; mass spectrometry; precision; proteomics; sensitivity; specificity; LABEL-FREE; COMPUTATIONAL PLATFORM; NORMALIZATION;
D O I
10.1021/acs.jproteome.1c00143
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
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 (MQand 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 rang; 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.
引用
收藏
页码:3497 / 3507
页数:11
相关论文
共 17 条
[1]   CONTROLLING THE FALSE DISCOVERY RATE - A PRACTICAL AND POWERFUL APPROACH TO MULTIPLE TESTING [J].
BENJAMINI, Y ;
HOCHBERG, Y .
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 1995, 57 (01) :289-300
[2]   Thousand and one ways to quantify and compare protein abundances in label-free bottom-up proteomics [J].
Blein-Nicolas, Melisande ;
Zivy, Michel .
BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS, 2016, 1864 (08) :883-895
[3]   Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ [J].
Cox, Juergen ;
Hein, Marco Y. ;
Luber, Christian A. ;
Paron, Igor ;
Nagaraj, Nagarjuna ;
Mann, Matthias .
MOLECULAR & CELLULAR PROTEOMICS, 2014, 13 (09) :2513-2526
[4]   MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification [J].
Cox, Juergen ;
Mann, Matthias .
NATURE BIOTECHNOLOGY, 2008, 26 (12) :1367-1372
[5]   Analysis of Intrinsic Peptide Detectability via Integrated Label-Free and SRM-Based Absolute Quantitative Proteomics [J].
Jarnuczak, Andrew F. ;
Lee, Dave C. H. ;
Lawless, Craig ;
Holman, Stephen W. ;
Eyers, Claire E. ;
Hubbard, Simon J. .
JOURNAL OF PROTEOME RESEARCH, 2016, 15 (09) :2945-2959
[6]   Normalization and missing value imputation for label-free LC-MS analysis [J].
Karpievitch, Yuliya V. ;
Dabney, Alan R. ;
Smith, Richard D. .
BMC BIOINFORMATICS, 2012, 13 :S5
[7]   Tools for Label-free Peptide Quantification [J].
Nahnsen, Sven ;
Bielow, Chris ;
Reinert, Knut ;
Kohlbacher, Oliver .
MOLECULAR & CELLULAR PROTEOMICS, 2013, 12 (03) :549-556
[8]   Benchmarking quantitative label-free LC-MS data processing workflows using a complex spiked proteomic standard dataset [J].
Ramus, Claire ;
Hovasse, Agnes ;
Marcellin, Marlene ;
Hesse, Anne-Marie ;
Mouton-Barbosa, Emmanuelle ;
Bouyssie, David ;
Vaca, Sebastian ;
Carapito, Christine ;
Chaoui, Karima ;
Bruley, Christophe ;
Garin, Jerome ;
Cianferani, Sarah ;
Ferro, Myriam ;
Van Dorssaeler, Alain ;
Burlet-Schiltz, Odile ;
Schaeffer, Christine ;
Coute, Yohann ;
de Peredo, Anne Gonzalez .
JOURNAL OF PROTEOMICS, 2016, 132 :51-62
[9]   Antibody-free, targeted mass-spectrometric approach for quantification of proteins at low picogram per milliliter levels in human plasma/serum [J].
Shi, Tujin ;
Fillmore, Thomas L. ;
Sun, Xuefei ;
Zhao, Rui ;
Schepmoes, Athena A. ;
Hossain, Mahmud ;
Xie, Fang ;
Wu, Si ;
Kim, Jong-Seo ;
Jones, Nathan ;
Moore, Ronald J. ;
Pasa-Tolic, Ljiljana ;
Kagan, Jacob ;
Rodland, Karin D. ;
Liu, Tao ;
Tang, Keqi ;
Camp, David G., II ;
Smith, Richard D. ;
Qian, Wei-Jun .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2012, 109 (38) :15395-15400
[10]   Systematic Comparison of Label-Free, SILAC, and TMT Techniques to Study Early Adaption toward Inhibition of EGFR Signaling in the Colorectal Cancer Cell Line DiFi [J].
Stepath, Markus ;
Zuelch, Birgit ;
Maghnouj, Abdelouahid ;
Schork, Karin ;
Turewicz, Michael ;
Eisenacher, Martin ;
Hahn, Stephan ;
Sitek, Barbara ;
Bracht, Thilo .
JOURNAL OF PROTEOME RESEARCH, 2020, 19 (02) :926-937