scholarly journals Protein-Level Statistical Analysis of Quantitative Label-Free Proteomics Data with ProStaR

Author(s):  
Samuel Wieczorek ◽  
Florence Combes ◽  
Hélène Borges ◽  
Thomas Burger
Author(s):  
Jun Yan ◽  
Hongning Zhai ◽  
Ling Zhu ◽  
Sasha Sa ◽  
Xiaojun Ding

Abstract Motivation Data mining and data quality evaluation are indispensable constituents of quantitative proteomics, but few integrated tools available. Results We introduced obaDIA, a one-step pipeline to generate visualizable and comprehensive results for quantitative proteomics data. obaDIA supports fragment-level, peptide-level and protein-level abundance matrices from DIA technique, as well as protein-level abundance matrices from other quantitative proteomic techniques. The result contains abundance matrix statistics, differential expression analysis, protein functional annotation and enrichment analysis. Additionally, enrichment strategies which use total proteins or expressed proteins as background are optional, and HTML based interactive visualization for differentially expressed proteins in the KEGG pathway is offered, which helps biological significance mining. In short, obaDIA is an automatic tool for bioinformatics analysis for quantitative proteomics. Availability and implementation obaDIA is freely available from https://github.com/yjthu/obaDIA.git. Supplementary information Supplementary data are available at Bioinformatics online.


2019 ◽  
Vol 19 (1) ◽  
pp. 204-211 ◽  
Author(s):  
Anup D. Shah ◽  
Robert J. A. Goode ◽  
Cheng Huang ◽  
David R. Powell ◽  
Ralf B. Schittenhelm

2009 ◽  
Vol 8 (10) ◽  
pp. 2227-2242 ◽  
Author(s):  
Lily Ting ◽  
Mark J. Cowley ◽  
Seah Lay Hoon ◽  
Michael Guilhaus ◽  
Mark J. Raftery ◽  
...  

Author(s):  
M C Rodriguez ◽  
D Mehta ◽  
M Tan ◽  
R G Uhrig

ABSTRACT Abiotic stresses such as drought result in large annual economic losses around the world. As sessile organisms, plants cannot escape the environmental stresses they encounter, but instead must adapt to survive. Studies investigating plant responses to osmotic and/or salt stress have largely focused on short-term systemic responses, leaving our understanding of intermediate to longer-term adaptation (24 h - days) lacking. In addition to protein abundance and phosphorylation changes, evidence suggests reversible lysine acetylation may also be important for abiotic stress responses. Therefore, to characterize the protein-level effects of osmotic and salt stress, we undertook a label-free proteomic analysis of Arabidopsis thaliana roots exposed to 300 mM Mannitol and 150 mM NaCl for 24 h. We assessed protein phosphorylation, lysine acetylation and changes in protein abundance, detecting significant changes in 245, 35 and 107 total proteins, respectively. Comparison with available transcriptome data indicates that transcriptome- and proteome-level changes occur in parallel, while PTMs do not. Further, we find significant changes in PTMs and protein abundance involve different proteins from the same networks, indicating a multifaceted regulatory approach to prolonged osmotic and salt stress. In particular, we find extensive protein-level changes involving sulphur metabolism under both osmotic and salt conditions as well as changes in protein kinases and transcription factors that may represent new targets for drought stress signaling. Collectively, we find that protein-level changes continue to occur in plant roots 24 h from the onset of osmotic and salt stress and that these changes differ across multiple proteome levels.


2021 ◽  
Author(s):  
Jian Song ◽  
Changbin Yu

AbstractThe label-free mass spectrometry-based proteomics data inevitably suffer from the problem of missing values. The existence of missing values prevents the downstream analyses which need a complete data matrix. Our motivation is to introduce the state-of-art machine learning algorithm XGboost to realize a method of imputation which can improve the accuracy of imputation. But in practical, XGboost has many parameters need to be tuned to deliver on its potential high performance. Although cross validation may find the best parameters, it is much time-consuming. Alternatively, we empirically determined the parameters to two kinds of base learners of XGboost. To explore the robustness and performance of XGboost based imputation with predetermined parameters, we conducted tests on three benchmark datasets. As a comparative, six common imputation methods were also experimented in terms of normalized root mean squared error and Pearson correlation coefficient. The comparative experimental results indicated that the XGboost based imputation method using the linear base learner is competitive to or out-performs its competitors, including the random forest based imputation, by achieving smaller imputation errors and better structure preservation under the empirical parameters for the three benchmark datasets.


eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Christoph N Schlaffner ◽  
Konstantin Kahnert ◽  
Jan Muntel ◽  
Ruchi Chauhan ◽  
Bernhard Y Renard ◽  
...  

Improvements in LC-MS/MS methods and technology have enabled the identification of thousands of modified peptides in a single experiment. However, protein regulation by post-translational modifications (PTMs) is not binary, making methods to quantify the modification extent crucial to understanding the role of PTMs. Here, we introduce FLEXIQuant-LF, a software tool for large-scale identification of differentially modified peptides and quantification of their modification extent without knowledge of the types of modifications involved. We developed FLEXIQuant-LF using label-free quantification of unmodified peptides and robust linear regression to quantify the modification extent of peptides. As proof of concept, we applied FLEXIQuant-LF to data-independent-acquisition (DIA) data of the anaphase promoting complex/cyclosome (APC/C) during mitosis. The unbiased FLEXIQuant-LF approach to assess the modification extent in quantitative proteomics data provides a better understanding of the function and regulation of PTMs. The software is available at https://github.com/SteenOmicsLab/FLEXIQuantLF.


2019 ◽  
Author(s):  
Nikita Prianichnikov ◽  
Heiner Koch ◽  
Scarlet Koch ◽  
Markus Lubeck ◽  
Raphael Heilig ◽  
...  

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.


2021 ◽  
Vol 12 ◽  
Author(s):  
Zhen Li ◽  
Lishan Zhang ◽  
Qingli Song ◽  
Guibin Wang ◽  
Wenxiao Yang ◽  
...  

Bacterial antibiotic resistance is a serious global problem; the underlying regulatory mechanisms are largely elusive. The earlier reports states that the vital role of transcriptional regulators (TRs) in bacterial antibiotic resistance. Therefore, we have investigated the role of TRs on enoxacin (ENX) resistance in Aeromonas hydrophila in this study. A label-free quantitative proteomics method was utilized to compare the protein profiles of the ahslyA knockout and wild-type A. hydrophila strains under ENX stress. Bioinformatics analysis showed that the deletion of ahslyA triggers the up-regulated expression of some vital antibiotic resistance proteins in A. hydrophila upon ENX stress and thereby reduce the pressure by preventing the activation of SOS repair system. Moreover, ahslyA directly or indirectly induced at least 11 TRs, which indicates a complicated regulatory network under ENX stress. We also deleted six selected genes in A. hydrophila that altered in proteomics data in order to evaluate their roles in ENX stress. Our results showed that genes such as AHA_0655, narQ, AHA_3721, AHA_2114, and AHA_1239 are regulated by ahslyA and may be involved in ENX resistance. Overall, our data demonstrated the important role of ahslyA in ENX resistance and provided novel insights into the effects of transcriptional regulation on antibiotic resistance in bacteria.


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