Dataset Information

EBprotV2: A Perseus Plugin for Differential Protein Abundance Analysis of Labeling-Based Quantitative Proteomics Data.

ABSTRACT: We present EBprotV2, a Perseus plugin for peptide-ratio-based differential protein abundance analysis in labeling-based proteomics experiments. The original version of EBprot models the distribution of log-transformed peptide-level ratios as a Gaussian mixture of differentially abundant proteins and nondifferentially abundant proteins and computes the probability score of differential abundance for each protein based on the reproducible magnitude of peptide ratios. However, the fully parametric model can be inflexible, and its R implementation is time-consuming for data sets containing a large number of peptides (e.g., >100?000). The new tool built in the C++ language is not only faster in computation time but also equipped with a flexible semiparametric model that handles skewed ratio distributions better. We have also developed a Perseus plugin for EBprotV2 for easy access to the tool. In addition, the tool now offers a new submodule (MakeGrpData) to transform label-free peptide intensity data into peptide ratio data for group comparisons and performs differential abundance analysis using mixture modeling. This approach is especially useful when the label-free data have many missing peptide intensity data points.

SUBMITTER: Koh HWL

PROVIDER: S-EPMC6433620 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

EBprotV2: A Perseus Plugin for Differential Protein Abundance Analysis of Labeling-Based Quantitative Proteomics Data.

Koh Hiromi W L HWL Zhang Yunbin Y Vogel Christine C Choi Hyungwon H

Journal of proteome research 20181119 2

We present EBprotV2, a Perseus plugin for peptide-ratio-based differential protein abundance analysis in labeling-based proteomics experiments. The original version of EBprot models the distribution of log-transformed peptide-level ratios as a Gaussian mixture of differentially abundant proteins and nondifferentially abundant proteins and computes the probability score of differential abundance for each protein based on the reproducible magnitude of peptide ratios. However, the fully parametric ...[more]

PMID: 30411623

Similar Datasets

Project description:Comparative proteomics is a powerful analytical method for learning about the responses of biological systems to changes in growth parameters. To make confident inferences about biological responses, proteomics approaches must incorporate appropriate statistical measures of quantitative data. In the present work we applied microarray-based normalization and statistical analysis (significance testing) methods to analyze quantitative proteomics data generated from the metabolic labeling of a marine bacterium (Sphingopyxis alaskensis). Quantitative data were generated for 1,172 proteins, representing 1,736 high confidence protein identifications (54% genome coverage). To test approaches for normalization, cells were grown at a single temperature, metabolically labeled with (14)N or (15)N, and combined in different ratios to give an artificially skewed data set. Inspection of ratio versus average (MA) plots determined that a fixed value median normalization was most suitable for the data. To determine an appropriate statistical method for assessing differential abundance, a -fold change approach, Student's t test, unmoderated t test, and empirical Bayes moderated t test were applied to proteomics data from cells grown at two temperatures. Inverse metabolic labeling was used with multiple technical and biological replicates, and proteomics was performed on cells that were combined based on equal optical density of cultures (providing skewed data) or on cell extracts that were combined to give equal amounts of protein (no skew). To account for arbitrarily complex experiment-specific parameters, a linear modeling approach was used to analyze the data using the limma package in R/Bioconductor. A high quality list of statistically significant differentially abundant proteins was obtained by using lowess normalization (after inspection of MA plots) and applying the empirical Bayes moderated t test. The approach also effectively controlled for the number of false discoveries and corrected for the multiple testing problem using the Storey-Tibshirani false discovery rate (Storey, J. D., and Tibshirani, R. (2003) Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. U.S.A. 100, 9440-9445). The approach we have developed is generally applicable to quantitative proteomics analyses of diverse biological systems.

Project description:Protein import into the endoplasmic reticulum (ER) is the first step in the biogenesis of around 10,000 different soluble and membrane proteins in humans. It involves the co- or post-translational targeting of precursor polypeptides to the ER, and their subsequent membrane insertion or translocation. So far, three pathways for the ER targeting of precursor polypeptides and four pathways for the ER targeting of mRNAs have been described. Typically, these pathways deliver their substrates to the Sec61 polypeptide-conducting channel in the ER membrane. Next, the precursor polypeptides are inserted into the ER membrane or translocated into the ER lumen, which may involve auxiliary translocation components, such as the TRAP and Sec62/Sec63 complexes, or auxiliary membrane protein insertases, such as EMC and the TMCO1 complex. Recently, the PEX19/PEX3-dependent pathway, which has a well-known function in targeting and inserting various peroxisomal membrane proteins into pre-existent peroxisomal membranes, was also found to act in the targeting and, putatively, insertion of monotopic hairpin proteins into the ER. These either remain in the ER as resident ER membrane proteins, or are pinched off from the ER as components of new lipid droplets. Therefore, the question arose as to whether this pathway may play a more general role in ER protein targeting, i.e., whether it represents a fourth pathway for the ER targeting of precursor polypeptides. Thus, we addressed the client spectrum of the PEX19/PEX3-dependent pathway in both PEX3-depleted HeLa cells and PEX3-deficient Zellweger patient fibroblasts by an established approach which involved the label-free quantitative mass spectrometry of the total proteome of depleted or deficient cells, as well as differential protein abundance analysis. The negatively affected proteins included twelve peroxisomal proteins and two hairpin proteins of the ER, thus confirming two previously identified classes of putative PEX19/PEX3 clients in human cells. Interestingly, fourteen collagen-related proteins with signal peptides or N-terminal transmembrane helices belonging to the secretory pathway were also negatively affected by PEX3 deficiency, which may suggest compromised collagen biogenesis as a hitherto-unknown contributor to organ failures in the respective Zellweger patients.

Dataset Information

EBprotV2: A Perseus Plugin for Differential Protein Abundance Analysis of Labeling-Based Quantitative Proteomics Data.

Publications

EBprotV2: A Perseus Plugin for Differential Protein Abundance Analysis of Labeling-Based Quantitative Proteomics Data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets