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

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]

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

Nature Communications ◽

10.1038/s41467-021-25077-6 ◽

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.

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

10.1101/2020.03.23.998237 ◽

2020 ◽

Cited By ~ 1

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.

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

10.1101/2020.11.01.363101 ◽

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.

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NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses

Nucleic Acids Research ◽

10.1093/nar/gkaa498 ◽

2020 ◽

Vol 48 (14) ◽

pp. e83-e83 ◽

Cited By ~ 1

Author(s):

Shisheng Wang ◽

Wenxue Li ◽

Liqiang Hu ◽

Jingqiu Cheng ◽

Hao Yang ◽

...

Keyword(s):

Mass Spectrometry ◽

Quantitative Proteomics ◽

Missing Values ◽

Protein Complexes ◽

Label Free ◽

Missing Value ◽

Imputation Methods ◽

Data Independent Acquisition ◽

Low Performance ◽

User Friendly

Abstract Mass spectrometry (MS)-based quantitative proteomics experiments frequently generate data with missing values, which may profoundly affect downstream analyses. A wide variety of imputation methods have been established to deal with the missing-value issue. To date, however, there is a scarcity of efficient, systematic, and easy-to-handle tools that are tailored for proteomics community. Herein, we developed a user-friendly and powerful stand-alone software, NAguideR, to enable implementation and evaluation of different missing value methods offered by 23 widely used missing-value imputation algorithms. NAguideR further evaluates data imputation results through classic computational criteria and, unprecedentedly, proteomic empirical criteria, such as quantitative consistency between different charge-states of the same peptide, different peptides belonging to the same proteins, and individual proteins participating protein complexes and functional interactions. We applied NAguideR into three label-free proteomic datasets featuring peptide-level, protein-level, and phosphoproteomic variables respectively, all generated by data independent acquisition mass spectrometry (DIA-MS) with substantial biological replicates. The results indicate that NAguideR is able to discriminate the optimal imputation methods that are facilitating DIA-MS experiments over those sub-optimal and low-performance algorithms. NAguideR further provides downloadable tables and figures supporting flexible data analysis and interpretation. NAguideR is freely available at http://www.omicsolution.org/wukong/NAguideR/ and the source code: https://github.com/wangshisheng/NAguideR/.

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Analysis of High Accuracy, Quantitative Proteomics Data in the MaxQB Database

Molecular & Cellular Proteomics ◽

10.1074/mcp.m111.014068 ◽

2012 ◽

Vol 11 (3) ◽

pp. M111.014068 ◽

Cited By ~ 96

Author(s):

Christoph Schaab ◽

Tamar Geiger ◽

Gabriele Stoehr ◽

Juergen Cox ◽

Matthias Mann

Keyword(s):

Quantitative Proteomics ◽

High Accuracy ◽

Proteomics Data

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MaxQuant software for ion mobility enhanced shotgun proteomics

10.1101/651760 ◽

2019 ◽

Cited By ~ 6

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.

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

10.1101/328864 ◽

2018 ◽

Cited By ~ 1

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.

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Graph Theoretical Analysis, Insilico Modeling and Formulation of Pyrimidine Nanoparticles as p38α MAP Kinases inhibitors: A Quantitative Proteomics Approach

Drug Research ◽

10.1055/a-0650-3979 ◽

2018 ◽

Vol 69 (02) ◽

pp. 100-110 ◽

Cited By ~ 2

Author(s):

Panneerselvam Theivendren ◽

Selvaraj Kunjiappan ◽

Saravanan Govindraj ◽

Jaikanth Chandrasekarn ◽

Parasuraman Pavadai ◽

...

Keyword(s):

Theoretical Analysis ◽

Anticancer Activity ◽

Quantitative Proteomics ◽

Density Functional ◽

Map Kinases ◽

Label Free ◽

Proteomics Data ◽

Experimental Conditions ◽

Regulatory Pathways ◽

Graph Theoretical Analysis

AbstractIn this study, the optimized 4-(4-hydroxybenzyl)-2-amino-6-hydroxypyrimidine-5-carboxamide derivative was formulated as nanoparticles to evaluate for their anticancer activity. The response surface methodology (RSM) was performed with utilization of Box-Behnken statistical design (BBSD) to optimize the experimental conditions for identification of significant synthetic methodology. To explore the stability of the derivative was done by density functional theory (DFT). Graph theoretical analysis was introduced to identify the drug target p38α MAP Kinases and then insilico modeling was performed to provide straightforward information for further structural optimization. The experimental results under optimal experimental conditions obtained 74.55–76% yield of 4-(4-hydroxybenzyl)-2-amino-6-hydroxypyrimidine-5-carboxamide, 127oC melting point and Rf value 0.59 were well matched with the predicted results and this was gaining 95% of confidence level and suitability of RSM. The spectral data were reliable with the assigned structures of synthetic yields. The formulated nanoparticles were exhibited a good anticancer activity against used cancer cell line MCF7. Amusingly the observed docking scores and in-vitro anticancer activity was proving the compound significance and potential as a potent p38α inhibitor. Further, we have elucidated the mechanism of action at its functional level using label-free quantitative proteomics. Interestingly the observed results were indicating that the derived proteomics data involving in the alteration process in cancer-related regulatory pathways.

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MaxQuant Software for Ion Mobility Enhanced Shotgun Proteomics

Molecular & Cellular Proteomics ◽

10.1074/mcp.tir119.001720 ◽

2020 ◽

Vol 19 (6) ◽

pp. 1058-1069 ◽

Cited By ~ 11

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.

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