scholarly journals Benchmarking accuracy and precision of intensity-based absolute quantification of protein abundances in Saccharomyces cerevisiae

2020 ◽  
Author(s):  
Benjamín J. Sánchez ◽  
Petri-Jaan Lahtvee ◽  
Kate Campbell ◽  
Sergo Kasvandik ◽  
Rosemary Yu ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Absolute Quantification ◽  
Protein Quantification ◽  
Label Free ◽  
Proteomics Data ◽  
Popular Method ◽  
The Poor ◽  
Genome Wide ◽  
Accuracy And Precision ◽  
Technical Reproducibility

AbstractProtein quantification via label-free mass spectrometry (MS) has become an increasingly popular method for determining genome-wide absolute protein abundances. A known caveat of this approach is the poor technical reproducibility, i.e. how consistent the estimations are when the same sample is measured repeatedly. Here, we measured proteomics data for Saccharomyces cerevisiae with both biological and inter-batch technical triplicates, to analyze both accuracy and precision of protein quantification via MS. Moreover, we analyzed how these metrics vary when applying different methods for converting MS intensities to absolute protein abundances. We found that a simple normalization and rescaling approach performs as accurately yet more precisely than methods that rely on external standards. Additionally, we show that inter-batch reproducibility is worse than biological reproducibility for all evaluated methods. These results subsequently serve as a benchmark for assessing MS data quality for protein quantification, whilst also underscoring current limitations in this approach.

scholarly journals PANDA: A comprehensive and flexible tool for proteomics data quantitative analysis

2018 ◽  
Author(s):  
Cheng Chang ◽  
Chaoping Guo ◽  
Yuqing Ding ◽  
Kaikun Xu ◽  
Mingfei Han ◽  
...  
Keyword(s):  
Quantitative Proteomics ◽  
Computation Time ◽  
Peptide Identification ◽  
High Accuracy ◽  
Protein Quantification ◽  
Label Free ◽  
Proteomics Data ◽  
Flexible Tool ◽  
Accuracy And Precision ◽  
User Friendly

ABSTRACTSummaryAs the experiment techniques and strategies in quantitative proteomics are improving rapidly, the corresponding algorithms and tools for protein quantification with high accuracy and precision are continuously required to be proposed. Here, we present a comprehensive and flexible tool named PANDA for proteomics data quantification. PANDA, which supports both label-free and labeled quantifications, is compatible with existing peptide identification tools and pipelines with considerable flexibility. Compared with MaxQuant on two complex da-tasets, PANDA was proved to be more accurate and precise with less computation time. Additionally, PANDA is an easy-to-use desktop ap-plication tool with user-friendly interfaces.AvailabilityPANDA is freely available for download at https://sourceforge.net/projects/panda-tools/[email protected] and [email protected]

scholarly journals MaxQuant software for ion mobility enhanced shotgun proteomics

2019 ◽  
Author(s):  
Nikita Prianichnikov ◽  
Heiner Koch ◽  
Scarlet Koch ◽  
Markus Lubeck ◽  
Raphael Heilig ◽  
...  
Keyword(s):  
Ion Mobility ◽  
Retention Time ◽  
Signal Intensity ◽  
Feature Detection ◽  
Dynamic Range ◽  
Shotgun Proteomics ◽  
Detection Algorithm ◽  
Protein Quantification ◽  
Label Free ◽  
Proteomics Data

SummaryIon mobility can add a dimension to LC-MS based shotgun proteomics which has the potential to boost proteome coverage, quantification accuracy and dynamic range. Required for this is suitable software that extracts the information contained in the four-dimensional (4D) data space spanned by m/z, retention time, ion mobility and signal intensity. Here we describe the ion mobility enhanced MaxQuant software, which utilizes the added data dimension. It offers an end to end computational workflow for the identification and quantification of peptides, proteins and posttranslational modification sites in LC-IMS-MS/MS shotgun proteomics data. We apply it to trapped ion mobility spectrometry (TIMS) coupled to a quadrupole time-of-flight (QTOF) analyzer. A highly parallelizable 4D feature detection algorithm extracts peaks which are assembled to isotope patterns. Masses are recalibrated with a non-linear m/z, retention time, ion mobility and signal intensity dependent model, based on peptides from the sample. A new matching between runs (MBR) algorithm that utilizes collisional cross section (CCS) values of MS1 features in the matching process significantly gains specificity from the extra dimension. Prerequisite for using CCS values in MBR is a relative alignment of the ion mobility values between the runs. The missing value problem in protein quantification over many samples is greatly reduced by CCS aware MBR.MS1 level label-free quantification is also implemented which proves to be highly precise and accurate on a benchmark dataset with known ground truth. MaxQuant for LC-IMS-MS/MS is part of the basic MaxQuant release and can be downloaded from http://maxquant.org.

scholarly journals IceR improves proteome coverage and data completeness in global and single-cell proteomics

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Mathias Kalxdorf ◽  
Torsten Müller ◽  
Oliver Stegle ◽  
Jeroen Krijgsveld
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Missing Values ◽  
Peptide Identification ◽  
Protein Quantification ◽  
Developmental Trajectory ◽  
Ion Current ◽  
Label Free ◽  
Proteomics Data ◽  
Data Completeness

AbstractLabel-free proteomics by data-dependent acquisition enables the unbiased quantification of thousands of proteins, however it notoriously suffers from high rates of missing values, thus prohibiting consistent protein quantification across large sample cohorts. To solve this, we here present IceR (Ion current extraction Re-quantification), an efficient and user-friendly quantification workflow that combines high identification rates of data-dependent acquisition with low missing value rates similar to data-independent acquisition. Specifically, IceR uses ion current information for a hybrid peptide identification propagation approach with superior quantification precision, accuracy, reliability and data completeness compared to other quantitative workflows. Applied to plasma and single-cell proteomics data, IceR enhanced the number of reliably quantified proteins, improved discriminability between single-cell populations, and allowed reconstruction of a developmental trajectory. IceR will be useful to improve performance of large scale global as well as low-input proteomics applications, facilitated by its availability as an easy-to-use R-package.

scholarly journals Comparison of Different Label-Free Techniques for the Semi-Absolute Quantification of Protein Abundance

Proteomes ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 2
Author(s):  
Aarón Millán-Oropeza ◽  
Mélisande Blein-Nicolas ◽  
Véronique Monnet ◽  
Michel Zivy ◽  
Céline Henry
Keyword(s):  
Shotgun Proteomics ◽  
Absolute Quantification ◽  
Biological Data ◽  
Protein Quantification ◽  
Protein Abundance ◽  
Label Free ◽  
Absolute Abundance ◽  
Yeast Saccharomyces Cerevisiae ◽  
Genome Scale ◽  
Free Quantification

In proteomics, it is essential to quantify proteins in absolute terms if we wish to compare results among studies and integrate high-throughput biological data into genome-scale metabolic models. While labeling target peptides with stable isotopes allow protein abundance to be accurately quantified, the utility of this technique is constrained by the low number of quantifiable proteins that it yields. Recently, label-free shotgun proteomics has become the “gold standard” for carrying out global assessments of biological samples containing thousands of proteins. However, this tool must be further improved if we wish to accurately quantify absolute levels of proteins. Here, we used different label-free quantification techniques to estimate absolute protein abundance in the model yeast Saccharomyces cerevisiae. More specifically, we evaluated the performance of seven different quantification methods, based either on spectral counting (SC) or extracted-ion chromatogram (XIC), which were applied to samples from five different proteome backgrounds. We also compared the accuracy and reproducibility of two strategies for transforming relative abundance into absolute abundance: a UPS2-based strategy and the total protein approach (TPA). This study mentions technical challenges related to UPS2 use and proposes ways of addressing them, including utilizing a smaller, more highly optimized amount of UPS2. Overall, three SC-based methods (PAI, SAF, and NSAF) yielded the best results because they struck a good balance between experimental performance and protein quantification.

scholarly journals LFAQ: towards unbiased label-free absolute protein quantification by predicting peptide quantitative factors

2018 ◽  
Author(s):  
Cheng Chang ◽  
Zhiqiang Gao ◽  
Wantao Ying ◽  
Yan Zhao ◽  
Yan Fu ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Physicochemical Properties ◽  
Data Acquisition ◽  
Large Scale ◽  
Protein Quantification ◽  
Label Free ◽  
Crucial Issue ◽  
Accuracy And Precision ◽  
Quantification Accuracy ◽  
Novel Algorithm

AbstractMass spectrometry (MS) has become a prominent choice for large-scale absolute protein quantification, but its quantification accuracy still has substantial room for improvement. A crucial issue is the bias between the peptide MS intensity and the actual peptide abundance, i.e., the fact that peptides with equal abundance may have different MS intensities. This bias is mainly caused by the diverse physicochemical properties of peptides. Here, we propose a novel algorithm for label-free absolute protein quantification, LFAQ, which can correct the biased MS intensities by using the predicted peptide quantitative factors for all identified peptides. When validated on datasets produced by different MS instruments and data acquisition modes, LFAQ presented accuracy and precision superior to those of existing methods. In particular, it reduced the quantification error by an average of 46% for low-abundance proteins.

scholarly journals MaxQuant Software for Ion Mobility Enhanced Shotgun Proteomics

2020 ◽  
Vol 19 (6) ◽  
pp. 1058-1069 ◽  
Author(s):  
Nikita Prianichnikov ◽  
Heiner Koch ◽  
Scarlet Koch ◽  
Markus Lubeck ◽  
Raphael Heilig ◽  
...  
Keyword(s):  
Ion Mobility ◽  
Retention Time ◽  
Signal Intensity ◽  
Feature Detection ◽  
Dynamic Range ◽  
Shotgun Proteomics ◽  
Detection Algorithm ◽  
Protein Quantification ◽  
Label Free ◽  
Proteomics Data

Ion mobility can add a dimension to LC-MS based shotgun proteomics which has the potential to boost proteome coverage, quantification accuracy and dynamic range. Required for this is suitable software that extracts the information contained in the four-dimensional (4D) data space spanned by m/z, retention time, ion mobility and signal intensity. Here we describe the ion mobility enhanced MaxQuant software, which utilizes the added data dimension. It offers an end to end computational workflow for the identification and quantification of peptides and proteins in LC-IMS-MS/MS shotgun proteomics data. We apply it to trapped ion mobility spectrometry (TIMS) coupled to a quadrupole time-of-flight (QTOF) analyzer. A highly parallelizable 4D feature detection algorithm extracts peaks which are assembled to isotope patterns. Masses are recalibrated with a non-linear m/z, retention time, ion mobility and signal intensity dependent model, based on peptides from the sample. A new matching between runs (MBR) algorithm that utilizes collisional cross section (CCS) values of MS1 features in the matching process significantly gains specificity from the extra dimension. Prerequisite for using CCS values in MBR is a relative alignment of the ion mobility values between the runs. The missing value problem in protein quantification over many samples is greatly reduced by CCS aware MBR.MS1 level label-free quantification is also implemented which proves to be highly precise and accurate on a benchmark dataset with known ground truth. MaxQuant for LC-IMS-MS/MS is part of the basic MaxQuant release and can be downloaded from http://maxquant.org.

scholarly journals Benchmarking accuracy and precision of intensity‐based absolute quantification of protein abundances in Saccharomyces cerevisiae

PROTEOMICS ◽  
2021 ◽  
pp. 2000093
Author(s):  
Benjamín J. Sánchez ◽  
Petri‐Jaan Lahtvee ◽  
Kate Campbell ◽  
Sergo Kasvandik ◽  
Rosemary Yu ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Absolute Quantification ◽  
Accuracy And Precision

scholarly journals IceR improves proteome coverage and data completeness in global and single-cell proteomics

2020 ◽  
Author(s):  
Mathias Kalxdorf ◽  
Torsten Müller ◽  
Oliver Stegle ◽  
Jeroen Krijgsveld
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Missing Values ◽  
Peptide Identification ◽  
R Package ◽  
Protein Quantification ◽  
Developmental Trajectory ◽  
Label Free ◽  
Proteomics Data ◽  
Data Completeness

AbstractLabel-free proteomics by data-dependent acquisition (DDA) enables the unbiased quantification of thousands of proteins, however it notoriously suffers from high rates of missing values, thus prohibiting consistent protein quantification across large sample cohorts. To solve this, we here present IceR, an efficient and user-friendly quantification workflow that combines high identification rates of DDA with low missing value rates similar to DIA. Specifically, IceR uses ion current information in DDA data for a hybrid peptide identification propagation (PIP) approach with superior quantification precision, accuracy, reliability and data completeness compared to other quantitative workflows. We demonstrate greatly improved quantification sensitivity on published plasma and single-cell proteomics data, enhancing the number of reliably quantified proteins, improving discriminability between single-cell populations, and allowing reconstruction of a developmental trajectory. IceR will be useful to improve performance of large scale global as well as low-input proteomics applications, facilitated by its availability as an easy-to-use R-package.

scholarly journals Benchmarking accuracy and precision of intensity‐based absolute quantification of protein abundances in  Saccharomyces cerevisiae

PROTEOMICS ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 2170095
Author(s):  
Benjamín J. Sánchez ◽  
Petri‐Jaan Lahtvee ◽  
Kate Campbell ◽  
Sergo Kasvandik ◽  
Rosemary Yu ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Absolute Quantification ◽  
Accuracy And Precision

scholarly journals Comparison of Different Label-free Techniques for the Semi-absolute Quantification of Protein Abundance

2021 ◽  
Author(s):  
Aarón Millán-Oropeza ◽  
Mélisande Blein-Nicolas ◽  
Véronique Monnet ◽  
Michel Zivy ◽  
Céline Henry
Keyword(s):  
Shotgun Proteomics ◽  
Absolute Quantification ◽  
Biological Data ◽  
Protein Quantification ◽  
Protein Abundance ◽  
Label Free ◽  
Absolute Abundance ◽  
Yeast Saccharomyces Cerevisiae ◽  
Genome Scale ◽  
Free Quantification

In proteomics, it is essential to quantify proteins in absolute terms if we wish compare results among studies and integrate high-throughput biological data into genome-scale metabolic models. While labeling target peptides with stable isotopes allows protein abundance to be accurately quantified, the utility of this technique is constrained by the low number of quantifiable proteins that it yields. Recently, label-free shotgun proteomics has become the “gold standard” for carrying out global assessments of biological samples containing thousands of proteins. However, this tool must be further improved if we wish to accurately quantify absolute levels of proteins. Here, we used different label-free quantification techniques to estimate absolute protein abundance in the model yeast Saccharomyces cerevisiae. More specifically, we evaluated the performance of seven different quantification methods, based either on spectral counting (SC) or extracted-ion chromatogram (XIC), which were applied to samples from five different proteome backgrounds. We also compared the accuracy and reproducibility of two strategies for transforming relative abundance into absolute abundance: a UPS2-based strategy and the total protein approach (TPA). This study mentions technical challenges related to UPS2 use and proposes ways of addressing them, including utilizing a smaller, more highly optimized amount of UPS2. Overall, three SC-based methods (PAI, SAF, and NSAF) yielded the best results because they struck a good balance between experimental performance and protein quantification.

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