Solution to Statistical Challenges in Proteomics Is More Statistics, Not Less

Serang, Oliver; Käll, Lukas

doi:10.1021/acs.jproteome.5b00568

Cited by 47 publications

(48 citation statements)

References 26 publications

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“…This may allow for the revision of certain dogmas of identification-centered proteomics, such as 1% FDR for peptide-spectral matches. A deeper understanding of the error propagation in mass spectrometry experiments might allow for more flexible treatment of error rates (49), which could have a significant positive effect on the depth of the quantitative proteome analysis. More advanced quantification algorithms that will take into account the variance and covariance of peptide and protein abundance in multiple experiments urgently need to be developed.…”

Section: Figmentioning

confidence: 99%

DeMix-Q: Quantification-Centered Data Processing Workflow

Zhang

Käll

Zubarev

2016

Molecular & Cellular Proteomics

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120

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Label-free quantification (LFQ) is one of the most efficient approaches for quantifying proteome differences between multiple states of a biological system. LFQ aims to reproducibly identify and quantify peptides through multiple liquid-chromatography-coupled tandem mass spectrometry (LC-MS/MS) experiments. In the popular data-dependent acquisition (DDA) approach named Top-N DDA, the appearance of a peptide-like signal in a "survey" mass spectrum triggers a tandem mass spectrometry (MS/MS) event, targeting the (N) most-abundant precursor ions. Previous studies have shown that, due to the limited speed of a mass spectrometer, the majority of peptide ions detected in MS 1 are not targeted in MS/MS, especially when a nonfractionated complex sample is analyzed (1, 2). This low sampling efficiency (Ͻ50%), combined with the stochastic nature of precursor selection and a limited efficiency of MS/MS identification (Ͻ70%) (3), frequently causes the absence of MS/MS identification for an individual peptide in some LC-MS/MS experiments ("runs") within a larger dataset, even when replicate measurements are made (4). This deficiency is known as the missing value problem in LFQ. The problem significantly limits the size of the DDA-acquired proteomics dataset across which reliable quantification can be made for each protein (5, 6).One of the causes of the missing value problem is the traditional focus on the process of identifying a peptide as opposed to its quantification. For historical reasons, peptide sequence identification has been considered the focal point and the most important step in the whole proteomics procedure, while quantification came as almost an afterthought (7,8). This dominant proteomics paradigm can be characterized as the identification-centered approach, also known as a spectrum-centric approach (9). Only gradually the missing value problem has been identified as one of the biggest drawbacks of the DDA approach (4, 5). To address the reproducibility issue in MS/MS identification, several alternative data acquisition strategies had been suggested, including targeted (10) and semi-targeted (11, 12) approaches. However, none of the improved DDA strategies has solved the missing value problem anywhere close to the data-independent acquisition (DIA) (13,14). The latter approach, however, typically provides somewhat lower depth and breadth of the proteome coverage than the DDA methods.In our opinion, the DDA-associated missing value problem is caused by the sequential execution of two independent processes: peptide identification by MS/MS and its quantification by MS 1 . At first glance, performing MS 1 -based quantification simultaneously with MS/MS identification should provide an obvious solution to the missing value problem. Since MS 1 spectra contain many more peptide ions than are selected for MS/MS in DDA (or identified in DIA), the peptide's mass information is practically always present when an iden-

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Section: Figmentioning

confidence: 99%

DeMix-Q: Quantification-Centered Data Processing Workflow

Zhang

Käll

Zubarev

2016

Molecular & Cellular Proteomics

Self Cite

120

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“…20 When assessing imputation strategies, the comparison of the estimated/imputed values to known values can be performed using the root-mean-square error or any of its many variants. Assessment of search engines often uses the false discovery rate, the percent of identified features that are incorrect, 35 computed as the ratio of false positives to the total of positive discoveries (false and true ones). In an ideal situation, one would also want to measure true and false negative outcomes of an experiment.…”

Section: Recommendations For Methods Assessmentmentioning

confidence: 99%

Testing and Validation of Computational Methods for Mass Spectrometry

et al. 2015

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High-throughput methods based on mass spectrometry (proteomics, metabolomics, lipidomics, etc.) produce a wealth of data that cannot be analyzed without computational methods. The impact of the choice of method on the overall result of a biological study is often underappreciated, but different methods can result in very different biological findings. It is thus essential to evaluate and compare the correctness and relative performance of computational methods. The volume of the data as well as the complexity of the algorithms render unbiased comparisons challenging. This paper discusses some problems and challenges in testing and validation of computational methods. We discuss the different types of data (simulated and experimental validation data) as well as different metrics to compare methods. We also introduce a new public repository for mass spectrometric reference data sets (http://compms.org/RefData) that contains a collection of publicly available data sets for performance evaluation for a wide range of different methods.

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“…The FDR q-values were calculated, based on the target-decoy approach, to control the false rates at the protein level (37). We employed the protein FDR q-value <= 0.01 threshold for all metrics except for the pseudo-ROC plots.…”

Section: The Benchmark Workflowmentioning

confidence: 99%