DAPAR &amp; ProStaR: software to perform statistical analyses in quantitative discovery proteomics

Wieczorek, Samuel; Combes, Florence; Lazar, Cosmin; Gianetto, Quentin Giai; Guy, Laurent; Dorffer, Alexia; Hesse, Anne-Marie; Couté, Yohann; Ferro, Myriam; Bruley, Christophe; Bürger, Thomas

doi:10.1093/bioinformatics/btw580

Cited by 278 publications

(230 citation statements)

References 8 publications

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“…Peptides and proteins were identified and quantified using MaxQuant (version 1.5.7.4 [28]) and Uniprot database (February 2017 version, Escherichia coli K-12 taxonomy). For statistical analysis, we used ProStaR (29). Proteins identified in the reverse and contaminant databases and proteins exhibiting less than 4 intensity values in one condition were discarded from the list.…”

Section: Methodsmentioning

confidence: 99%

The Long Hunt for pssR —Looking for a Phospholipid Synthesis Transcriptional Regulator, Finding the Ribosome

Bartoli

Belmudes

et al. 2017

J Bacteriol

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The phospholipid (PL) composition of bacterial membranes varies as a function of growth rate and in response to changes in the environment. While growth adaptation can be explained by biochemical feedback in the PL synthesis pathway, recent transcriptome studies have revealed that the expression of PL synthesis genes can also be tuned in response to various stresses. We previously showed that the BasRS two-component pathway controls the expression of the diacylglycerol kinase gene, dgkA, in Escherichia coli (A. Wahl, L. My, R. Dumoulin, J. N. Sturgis, and E. Bouveret, Mol Microbiol, 80:1260 -1275, 2011, https://doi.org/10.1111/j .1365-2958.2011. In this study, we set up a strategy to identify the mutation responsible for the upregulation of pssA observed in the historical pssR1 mutant and supposedly corresponding to a transcriptional repressor (C. P. Sparrow and J. Raetz, J Biol Chem, 258:9963-9967, 1983). pssA encodes phosphatidylserine synthase, the first step of phosphatidylethanolamine synthesis. We showed that this mutation corresponded to a single nucleotide change in the anti-Shine-Dalgarno sequence of the 16S rRNA encoded by the rrnC operon. We further demonstrated that this mutation enhanced the translation of pssA. Though this effect appeared to be restricted to PssA among phospholipid synthesis enzymes, it was not specific, as evidenced by a global effect on the production of unrelated proteins.IMPORTANCE Bacteria adjust the phospholipid composition of their membranes to the changing environment. In addition to enzymatic regulation, stress response regulators control specific steps of the phospholipid synthesis pathway. We wanted to identify a potential regulator controlling the expression of the phosphatidylserine synthase gene. We showed that it was not the previously suggested hdfR gene and instead that a mutation in the anti-Shine-Dalgarno sequence of 16S RNA was responsible for an increase in pssA translation. This example underlines the fact that gene expression can be modulated by means other than specific regulatory processes.KEYWORDS Escherichia coli, hdfR, maoP, phosphatidylserine synthase, phospholipid synthesis, pssA, pssR, ribosomal mutations, rrnC, yifE P hospholipids, which are the building blocks of bacterial membranes, are synthesized by a series of enzymes localized in the inner membrane. The biochemistry of the phospholipid synthesis pathway has been well deciphered, especially in the Escherichia coli model bacterium (1). Phospholipid composition is very tightly controlled and is modulated by various parameters, such as growth conditions, growth rate, and stresses. Most of this control is thought to occur at the enzymatic activity level (1). However, transcriptomic data have recently uncovered the possibility that several signaling pathways control phospholipid synthesis through a regulation of enzyme expression levels in E. coli. Indeed, the expression of all the genes for phospholipid

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Section: Methodsmentioning

confidence: 99%

The Long Hunt for pssR —Looking for a Phospholipid Synthesis Transcriptional Regulator, Finding the Ribosome

Bartoli

Belmudes

et al. 2017

J Bacteriol

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“…batch effects or missing values (see for instance Wieczorek and others (2016)), methods for differential analysis of proteomic datasets can be divided in two main families: peptide-based and aggregation-based methods, also referred to as summarization-based in Goeminne and others (2015). In the latter ones, peptide-level information is first aggregated at the protein level and proteins are then tested for differential abundance using these summaries.…”

Section: State-of-the-artmentioning

confidence: 99%

PEPA test: fast and powerful differential analysis from relative quantitative proteomics data using shared peptides

Jacob

Combes

Bürger

2017

Preprint

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We propose a new hypothesis test for the differential abundance of proteins in mass-spectrometry based relative quantification. An important feature of this type of high-throughput analyses is that it involves an enzymatic digestion of the sample proteins into peptides prior to identification and quantification. Due to numerous homology sequences, different proteins can lead to peptides with identical amino acid chains, so that their parent protein is ambiguous. These so-called shared peptides make the protein-level statistical analysis a challenge, so that they are often not accounted for. In this article, we use a linear model describing peptide-protein relationships to build a likelihood ratio test of differential abundance for proteins. We show that the likelihood ratio statistic can be computed in linear time with the number of peptides. We also provide the asymptotic null distribution of a regularized version of our statistic. Experiments on both real and simulated datasets show that our procedures outperforms state-of-the-art methods. The procedures are available via the pepa.test function of the DAPAR Bioconductor R package.

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“…Although several comprehensive analysis pipelines, such as OCAP (Wang, Yang & Yang, 2012), ProSightPC (http://proteinaceous.net/software/), TopPIC (Kou, Xun & Liu, 2016), MSstats (Choi et al, 2014), Skyline (MacLean et al, 2010), MaxQuant & Perseus (Tyanova et al, 2016) and DAPAR & ProStaR (Wieczorek et al, 2017), have been developed for the downstream data analysis, to the best of our knowledge there are no tools specifically designed to facilitate chemoproteomics data analysis for scientists with a limited computational background and available as a public server application.…”

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confidence: 99%

DOSCHEDA: a web application for interactive chemoproteomics data analysis

Contrino

Miele

Tomlinson

et al. 2017

PeerJ Computer Science

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Background. Mass Spectrometry (MS) based chemoproteomics has recently become a main tool to identify and quantify cellular target protein interactions with ligands/drugs in drug discovery. The complexity associated with these new types of data requires scientists with a limited computational background to perform systematic data quality controls as well as to visualize the results derived from the analysis to enable rapid decision making. To date, there are no readily accessible platforms specifically designed for chemoproteomics data analysis. Results. We developed a Shiny-based web application named DOSCHEDA (Down Stream Chemoproteomics Data Analysis) to assess the quality of chemoproteomics experiments, to filter peptide intensities based on linear correlations between replicates, and to perform statistical analysis based on the experimental design. In order to increase its accessibility, DOSCHEDA is designed to be used with minimal user input and it does not require programming knowledge. Typical inputs can be protein fold changes or peptide intensities obtained from Proteome Discover, MaxQuant or other similar software. DOSCHEDA aggregates results from bioinformatics analyses performed on the input dataset into a dynamic interface, it encompasses interactive graphics and enables customized output reports. Conclusions. DOSCHEDA is implemented entirely in R language. It can be launched by any system with R installed, including Windows, Mac OS and Linux distributions. DOSCHEDA is hosted on a shiny-server at https://doscheda.shinyapps.io/doscheda and is also available as a Bioconductor package (http://www.bioconductor.org/).

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DAPAR & ProStaR: software to perform statistical analyses in quantitative discovery proteomics

Cited by 278 publications

References 8 publications

The Long Hunt for pssR —Looking for a Phospholipid Synthesis Transcriptional Regulator, Finding the Ribosome

The Long Hunt for pssR —Looking for a Phospholipid Synthesis Transcriptional Regulator, Finding the Ribosome

PEPA test: fast and powerful differential analysis from relative quantitative proteomics data using shared peptides

DOSCHEDA: a web application for interactive chemoproteomics data analysis

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