In vitro and in silico processes to identify differentially expressed proteins

Allet, Nadia; Barrillat, Nicolas; Baussant, Thierry; Boiteau, Celia; Botti, Paolo; Bougueleret, Lydie; Budin, Nicolas; Canet, Denis; Carraud, Stéphanie; Chiappe, Diego; Christmann, Nicolas; Colinge, Jacques; Cusin, Isabelle; Dafflon, Nicolas; Depresle, Benoît; Fasso, Irène; Frauchiger, Pascal; Gaertner, Hubert; Gleizes, Anne; González-Couto, Eduardo; Jeandenans, Catherine; Karmime, Abderrahim; Kowall, Thomas; Lagache, Sophie Braga; Mahé, Eve; Masselot, Alexandre; Mattou, Hassan; Moniatte, Marc; Niknejad, Anne; Paolini, Moreno; Perret, Fréderic; Pinaud, Nicolas; Ranno, Frederic; Raimondi, Sylvain; Reffas, Samia; Regamey, Pierre-Olivier; Rey, Pierre-Antoine; Rodriguez-Tomé, Patricia; Rose, Keith; Rossellat, Gérald; Saudrais, Cédric; Schmidt, Camille; Villain, Matteo; Zwahlen, Catherine

doi:10.1002/pmic.200300840

Cited by 61 publications

(58 citation statements)

References 41 publications

(37 reference statements)

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“…However, it does not provide any statistical confidence measures for protein and peptide identifications, and its approach for assembling peptides into proteins in the presence of shared peptides has not been fully described. In addition, new tools are being developed at increasing speed, including commercial programs that combine the process of peptide identification and the subsequent assembly of peptides into proteins (40). 8 This diversity of computational tools, a positive development reflecting the increased used of shotgun proteomics, nevertheless presents a significant challenge for developing any kind of standards for the analysis and journal publication of proteomic datasets 8 SpectrumMill (www.chem.agilent.com).…”

Section: Computational Toolsmentioning

confidence: 99%

Interpretation of Shotgun Proteomic Data

Nesvizhskii

Aebersold

2005

Molecular & Cellular Proteomics

912

888

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The shotgun proteomic strategy based on digesting proteins into peptides and sequencing them using tandem mass spectrometry and automated database searching has become the method of choice for identifying proteins in most large scale studies. However, the peptide-centric nature of shotgun proteomics complicates the analysis and biological interpretation of the data especially in the case of higher eukaryote organisms. The same peptide sequence can be present in multiple different proteins or protein isoforms. Such shared peptides therefore can lead to ambiguities in determining the identities of sample proteins. In this article we illustrate the difficulties of interpreting shotgun proteomic data and discuss the need for common nomenclature and transparent informatic approaches. We also discuss related issues such as the state of protein sequence databases and their role in shotgun proteomic analysis, interpretation of relative peptide quantification data in the presence of multiple protein isoforms, the integration of proteomic and transcriptional data, and the development of a computational infrastructure for the integration of multiple diverse datasets. Molecular & Cellular Proteomics 4:1419 -1440, 2005.An explicit goal of proteomics is the identification and quantification of all the proteins expressed in a cell or tissue (1).Although not yet at the levels of data throughput and automation achieved in other genomic analyses such as DNA sequencing or microarray gene expression analysis, global protein profiling methods are rapidly evolving. This has been possible because of recent improvements in MS instrumentation, protein and peptide separation techniques, computational data analysis tools, and the availability of complete sequence databases for many species. As a result, analysis of complex protein mixtures using shotgun proteomics, a strategy based on the combination of protein digestion and MS/ MS-based peptide sequencing (2-4), has become widely adopted. The method allows protein identifications and, when combined with stable isotope labeling, quantification of the changes in the protein expression levels for hundreds of proteins in a single experiment (1).Compared with other MS-based proteomic technologies such as intact proteins sequencing (5, 6) or 2D 1 gel-based protein analysis (7), shotgun proteomic analysis has achieved a relatively high throughput. This is the result of a combination of several factors. Proteolytic digestion of proteins into shorter peptides simplifies MS/MS sequencing (peptides are easier to fragment in the mass spectrometer than intact proteins), whereas elimination of the 2D gel-based separation at the protein level simplifies sample handling and increases the overall data throughput. At the same time, computational analysis and interpretation of the data become more challenging (8 -13). The first and foremost computational challenge is the need to process large volumes of acquired MS/MS data with the purpose of identifying peptides that gave rise to observed spectra. This ch...

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Section: Computational Toolsmentioning

confidence: 99%

Interpretation of Shotgun Proteomic Data

Nesvizhskii

Aebersold

2005

Molecular & Cellular Proteomics

912

888

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“…However, the number of peptides identified per protein is growing in use for quantitative analysis [13][14][15][16][17][18][19][20]. The most straightforward implementation of this approach is spectrum counting which is the total number of peptides used to identify a protein [13,16,19,20].…”

Section: Introductionmentioning

confidence: 99%

Analyzing chromatin remodeling complexes using shotgun proteomics and normalized spectral abundance factors

Florens

Carozza

Swanson

et al. 2006

Methods

305

279

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Mass spectrometry based approaches are commonly used to identify proteins from multiprotein complexes, typically with the goal of identifying new complex members or identifying post translational modifications. However, with the recent demonstration that spectral counting is a powerful quantitative proteomic approach, the analysis of multiprotein complexes by mass spectrometry can be reconsidered in certain cases. Using the chromatography based approach named multidimensional protein identification technology, multiprotein complexes may be analyzed quantitatively using the normalized spectral abundance factor that allows comparison of multiple independent analyses of samples. This study describes an approach to visualize multiprotein complex datasets that provides structure function information that is superior to tabular lists of data. In this method review, we describe a reanalysis of the Rpd3/Sin3 small and large histone deacetylase complexes previously described in a tabular form to demonstrate the normalized spectral abundance factor approach.

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“…This approach has also been successfully applied to another proteomics study in determining the differences between plasma control and disease samples by parallel analysis of unlabeled protein samples analyzed by triplicate LC-MS/MS data [12]. In our current study, explicitly considering the correlative characteristics of pair-labeled MS data, we further refine the concept of the LPE method and introduce a weighted pooled test, L w , for the analysis of paired isotope-labeled samples.…”

Section: Introductionmentioning

confidence: 99%

Statistical identification of differentially labeled peptides from liquid chromatography tandem mass spectrometry

et al. 2007

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LC-MS/MS with certain labeling techniques such as isotope-coded affinity tag (ICAT) enables quantitative analysis of paired protein samples. However, current identification and quantification of differentially expressed peptides (and proteins) are not reliable for large proteomics screening of complex biological samples. The number of replicates is often limited because of the high cost of experiments and the limited supply of samples. Traditionally, a simple fold change cutoff is used, which results in a high rate of false positives. Standard statistical methods such as the two-sample ttest are unreliable and severely underpowered due to high variability in LC-MS/MS data, especially when only a small number of replicates are available. Using an advanced error pooling technique, we propose a novel statistical method that can reliably identify differentially expressed proteins while maintaining a high sensitivity, particularly with a small number of replicates. The proposed method was applied both to an extensive simulation study and a proteomics comparison between microparticles (MPs) generated from platelet (platelet MPs) and MPs isolated from plasma (plasma MPs). In these studies, we show a significant improvement of our statistical analysis in the identification of proteins that are differentially expressed but not detected by other statistical methods. In particular, several important proteins -two peptides for b-globin and three peptides for von Willebrand Factor (vWF) -were identified with very small false discovery rates (FDRs) by our method, while none was significant when other conventional methods were used. These proteins have been reported with their important roles in microparticles in human blood cells: vWF is a platelet and endothelial cell product that binds to P-selectin, GP1b, and GP IIb/IIIa, and b-globin is one of the peptides of hemoglobin involved in the transportation of oxygen by red blood cells.

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In vitro and in silico processes to identify differentially expressed proteins

Cited by 61 publications

References 41 publications

Interpretation of Shotgun Proteomic Data

Interpretation of Shotgun Proteomic Data

Analyzing chromatin remodeling complexes using shotgun proteomics and normalized spectral abundance factors

Statistical identification of differentially labeled peptides from liquid chromatography tandem mass spectrometry

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