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1.
Quantification of LC-MS peak intensities assigned during peptide identification in a typical comparative proteomics experiment will deviate from run-to-run of the instrument due to both technical and biological variation. Thus, normalization of peak intensities across an LC-MS proteomics dataset is a fundamental step in pre-processing. However, the downstream analysis of LC-MS proteomics data can be dramatically affected by the normalization method selected. Current normalization procedures for LC-MS proteomics data are presented in the context of normalization values derived from subsets of the full collection of identified peptides. The distribution of these normalization values is unknown a priori. If they are not independent from the biological factors associated with the experiment the normalization process can introduce bias into the data, possibly affecting downstream statistical biomarker discovery. We present a novel approach to evaluate normalization strategies, which includes the peptide selection component associated with the derivation of normalization values. Our approach evaluates the effect of normalization on the between-group variance structure in order to identify the most appropriate normalization methods that improve the structure of the data without introducing bias into the normalized peak intensities. 相似文献
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Marcello Manfredi Jessica Brandi Claudia Di Carlo Virginia Vita Vanella Elettra Barberis Emilio Marengo 《Expert review of proteomics》2013,10(9):733-747
ABSTRACTIntroduction: Discovery proteomics for cancer research generates complex datasets of diagnostic, prognostic, and therapeutic significance in human cancer. With the advent of high-resolution mass spectrometers, able to identify thousands of proteins in complex biological samples, only the application of bioinformatics can lead to the interpretation of data which can be relevant for cancer research.Areas covered: Here, we give an overview of the current bioinformatic tools used in cancer proteomics. Moreover, we describe their applications in cancer proteomics studies of cell lines, serum, and tissues, highlighting recent results and critically evaluating their outcomes.Expert opinion: The use of bioinformatic tools is a fundamental step in order to manage the large amount of proteins (from hundreds to thousands) that can be identified and quantified in a cancer biological samples by proteomics. To handle this challenge and obtain useful data for translational medicine, it is important the combined use of different bioinformatic tools. Moreover, a particular attention to the global experimental design, and the integration of multidisciplinary skills are essential for best setting of tool parameters and best interpretation of bioinformatics output. 相似文献
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J. Alberto Medina‐Aunon Alberto Paradela Marcus Macht Herbert Thiele Garry Corthals Juan Pablo Albar 《Proteomics》2010,10(18):3262-3271
One of the main goals in proteomics is to solve biological and molecular questions regarding a set of identified proteins. In order to achieve this goal, one has to extract and collect the existing biological data from public repositories for every protein and afterward, analyze and organize the collected data. Due to the complexity of this task and the huge amount of data available, it is not possible to gather this information by hand, making it necessary to find automatic methods of data collection. Within a proteomic context, we have developed Protein Information and Knowledge Extractor (PIKE) which solves this problem by automatically accessing several public information systems and databases across the Internet. PIKE bioinformatics tool starts with a set of identified proteins, listed as the most common protein databases accession codes, and retrieves all relevant and updated information from the most relevant databases. Once the search is complete, PIKE summarizes the information for every single protein using several file formats that share and exchange the information with other software tools. It is our opinion that PIKE represents a great step forward for information procurement and drastically reduces manual database validation for large proteomic studies. It is available at http://proteo.cnb.csic.es/pike . 相似文献
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In order to assess the biological function of proteins and their modifications for understanding signaling mechanisms within cells as well as specific biomarkers to disease, it is important that quantitative information be obtained under different experimental conditions. Stable isotope labeling is a powerful method for accurately determining changes in the levels of proteins and PTMs; however, isotope labeling experiments suffer from limited dynamic range resulting in signal change ratios of less than approximately 20:1 using most commercial mass spectrometers. Label-free approaches to relative quantification in proteomics such as spectral counting have gained popularity since no additional chemistries are needed. Here, we show a label-free method for relative quantification based on the TIC from peptide MS/MS spectra collected from data-dependent runs can be used effectively as a quantitative measure and expands the dynamic range over isotope labeling experiments allowing for abundance differences up to approximately 60:1 in a screen for proteins that bind to phosphotyrosine residues. 相似文献
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基因的差异化表达由多种因素共同导致,并且与许多疾病的发生和发展有密切联系,对差异化表达的基因进行生物信息学以及生物统计学的分析对于研究细胞调节机制和疾病机理有着重要意义。目前,对差异化表达的基因有以下几种主流的研究方法：DNA微阵列（DNA microarray）,抑制性消减杂交（SSH）,基因表达连续性分析（SAGE）,代表性差异分析（RDA）,以及mRNA差异显示PCR（mRNA DDRT-PCR）。目前许多基因差异化表达数据是建立在时段（time series）基础上,因此对基于时间变化的基因差异化表达分析变得尤为重要。本文将对差异化表达基因的几种主流方法进行详细阐述,并介绍一种基于傅里叶函数的时段基因差异化表达分析。 相似文献
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Ryan T. Fellers Joseph B. Greer Bryan P. Early Xiang Yu Richard D. LeDuc Neil L. Kelleher Paul M. Thomas 《Proteomics》2015,15(7):1235-1238
Many top‐down proteomics experiments focus on identifying and localizing PTMs and other potential sources of “mass shift” on a known protein sequence. A simple application to match ion masses and facilitate the iterative hypothesis testing of PTM presence and location would assist with the data analysis in these experiments. ProSight Lite is a free software tool for matching a single candidate sequence against a set of mass spectrometric observations. Fixed or variable modifications, including both PTMs and a select number of glycosylations, can be applied to the amino acid sequence. The application reports multiple scores and a matching fragment list. Fragmentation maps can be exported for publication in either portable network graphic (PNG) or scalable vector graphic (SVG) format. ProSight Lite can be freely downloaded from http://prosightlite.northwestern.edu , installs and updates from the web, and requires Windows 7 or a higher version. 相似文献
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The identification of new diagnostic or prognostic biomarkers is one of the main aims of clinical cancer research. In recent years, there has been a growing interest in using mass spectrometry for the detection of such biomarkers. The MS signal resulting from MALDI‐TOF measurements is contaminated by different sources of technical variations that can be removed by a prior pre‐processing step. In particular, denoising makes it possible to remove the random noise contained in the signal. Wavelet methodology associated with thresholding is usually used for this purpose. In this study, we adapted two multivariate denoising methods that combine wavelets and PCA to MS data. The objective was to obtain better denoising of the data so as to extract the meaningful proteomic biological information from the raw spectra and reach meaningful clinical conclusions. The proposed methods were evaluated and compared with the classical soft thresholding denoising method using both real and simulated data sets. It was shown that taking into account common structures of the signals by adding a dimension reduction step on approximation coefficients through PCA provided more effective denoising when combined with soft thresholding on detail coefficients. 相似文献
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James C. Wright Chaoqin Du Qiang Feng Xun Xu Jyoti S. Choudhary Jun Wang 《Proteomics》2014,14(9):1011-1014
Protein identification by MS/MS is an important technique in proteome studies. The Open Mass Spectrometry Search Algorithm (OMSSA) is an open‐source search engine that can be used to identify MS/MS spectra acquired in these experiments. Here, we present a software tool, termed OMSSAPercolator, which interfaces OMSSA with Percolator, a post‐search machine learning method for rescoring database search results. We demonstrate that it outperforms the standard OMSSA scoring scheme, and provides reliable significant measurements. OMSSAPercolator is programmed using JAVA and can be readily used as a standalone tool or integrated into existing data analysis pipelines. OMSSAPercolator is freely available and can be downloaded at http://sourceforge.net/projects/omssapercolator/ . 相似文献
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Shaan Chugh Maral Ouzounian Zhen Lu Shanas Mohamed Wenping Li Nicolas Bousette Peter P. Liu Anthony O. Gramolini 《Proteomics》2013,13(15):2324-2334
In‐depth proteomic analyses offer a systematic way to investigate protein alterations in disease and, as such, can be a powerful tool for the identification of novel biomarkers. Here, we analyzed proteomic data from a transgenic mouse model with cardiac‐specific overexpression of activated calcineurin (CnA), which results in severe cardiac hypertrophy. We applied statistically filtering and false discovery rate correction methods to identify 52 proteins that were significantly different in the CnA hearts compared to controls. Subsequent informatic analysis consisted of comparison of these 52 CnA proteins to another proteomic dataset of heart failure, three available independent microarray datasets, and correlation of their expression with the human plasma and urine proteome. Following this filtering strategy, four proteins passed these selection criteria, including myosin heavy chain 7, insulin‐like growth factor‐binding protein 7, annexin A2, and desmin. We assessed expression levels of these proteins in mouse plasma by immunoblotting, and observed significantly different levels of expression between healthy and failing mice for all four proteins. We verified antibody cross‐reactivity by examining human cardiac explant tissue by immunoblotting. Finally, we assessed protein levels in plasma samples obtained from four unaffected and four heart failure patients and demonstrated that all four proteins increased between twofold and 150‐fold in heart failure. We conclude that MYH7, IGFBP7, ANXA2, and DESM are all excellent candidate plasma biomarkers of heart failure in mouse and human. 相似文献
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Changes in the rat heart proteome induced by exercise training: Increased abundance of heat shock protein hsp20 总被引：3，自引：0，他引：3
Chronic exercise training elicits adaptations in the heart that improve pump function and confer cardioprotection. To identify molecular mechanisms by which exercise training stimulates this favorable phenotype, a proteomic approach was employed to detect rat cardiac proteins that were differentially expressed or modified after exercise training. Exercise-trained rats underwent six weeks of progressive treadmill training five days/week, 0% grade, using an interval training protocol. Sedentary control rats were age- and weight-matched to the exercise-trained rats. Hearts were harvested at various times (0-72 h) after the last bout of exercise and were used to generate 2-D electrophoretic proteome maps and immunoblots. Compared with hearts of sedentary rats, 26 protein spot intensities were significantly altered in hypertrophied hearts of exercise-trained rats (p <0.05), and 12 spots appeared exclusively on gels from hearts of exercise-trained rats. Immunoblotting confirmed that chronic exercise training, but not a single bout of exercise, elicited a 2.5-fold increase in the abundance of one of the candidate proteins in the heart, a 20 kDa heat shock protein (hsp20) that persisted for at least 72 h of detraining. Thus, exercise training alters the cardiac proteome of the rat heart; the changes include a marked increase in the expression of hsp20. 相似文献
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Martinez A Traverso JA Valot B Ferro M Espagne C Ephritikhine G Zivy M Giglione C Meinnel T 《Proteomics》2008,8(14):2809-2831
Most proteins in all organisms undergo crucial N-terminal modifications involving N-terminal methionine excision, N-alpha-acetylation or N-myristoylation (N-Myr), or S-palmitoylation. We investigated the occurrence of these poorly annotated but essential modifications in proteomes, focusing on eukaryotes. Experimental data for the N-terminal sequences of animal, fungi, and archaeal proteins, were used to build dedicated predictive modules in a new software. In vitro N-Myr experiments were performed with both plant and animal N-myristoyltransferases, for accurate prediction of the modification. N-terminal modifications from the fully sequenced genome of Arabidopsis thaliana were determined by MS. We identified 105 new modified protein N-termini, which were used to check the accuracy of predictive data. An accuracy of more than 95% was achieved, demonstrating (i) overall conservation of the specificity of the modification machinery in higher eukaryotes and (ii) robustness of the prediction tool. Predictions were made for various proteomes. Proteins that had undergone both N-terminal methionine (Met) cleavage and N-acetylation were found to be strongly overrepresented among the most abundant proteins, in contrast to those retaining their genuine unblocked Met. Here we propose that the nature of the second residue of an ORF is a key marker of the abundance of the mature protein in eukaryotes. 相似文献
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Xiao-jun Li Lik Wee Lee Clive Hayward Mi-Youn Brusniak Pui-Yee Fong Matthew McLean JoAnne Mulligan Douglas Spicer Kenneth C Fang Stephen W Hunsucker Paul Kearney 《Clinical proteomics》2015,12(1)
Background
Current quantification methods for mass spectrometry (MS)-based proteomics either do not provide sufficient control of variability or are difficult to implement for routine clinical testing.Results
We present here an integrated quantification (InteQuan) method that better controls pre-analytical and analytical variability than the popular quantification method using stable isotope-labeled standard peptides (SISQuan). We quantified 16 lung cancer biomarker candidates in human plasma samples in three assessment studies, using immunoaffinity depletion coupled with multiple reaction monitoring (MRM) MS. InteQuan outperformed SISQuan in precision in all three studies and tolerated a two-fold difference in sample loading. The three studies lasted over six months and encountered major changes in experimental settings. Nevertheless, plasma proteins in low ng/ml to low μg/ml concentrations were measured with a median technical coefficient of variation (CV) of 11.9% using InteQuan. The corresponding median CV using SISQuan was 15.3% after linear fitting. Furthermore, InteQuan surpassed SISQuan in measuring biological difference among clinical samples and in distinguishing benign versus cancer plasma samples.Conclusions
We demonstrated that InteQuan is a simple yet robust quantification method for MS-based quantitative proteomics, especially for applications in biomarker research and in routine clinical testing.Electronic supplementary material
The online version of this article (doi:10.1186/1559-0275-12-3) contains supplementary material, which is available to authorized users. 相似文献15.
Hiromi W. L. Koh Hannah L. F. Swa Damian Fermin Siok Ghee Ler Jayantha Gunaratne Hyungwon Choi 《Proteomics》2015,15(15):2580-2591
Labeling‐based proteomics is a powerful method for detection of differentially expressed proteins (DEPs). The current data analysis platform typically relies on protein‐level ratios, which is obtained by summarizing peptide‐level ratios for each protein. In shotgun proteomics, however, some proteins are quantified with more peptides than others, and this reproducibility information is not incorporated into the differential expression (DE) analysis. Here, we propose a novel probabilistic framework EBprot that directly models the peptide‐protein hierarchy and rewards the proteins with reproducible evidence of DE over multiple peptides. To evaluate its performance with known DE states, we conducted a simulation study to show that the peptide‐level analysis of EBprot provides better receiver‐operating characteristic and more accurate estimation of the false discovery rates than the methods based on protein‐level ratios. We also demonstrate superior classification performance of peptide‐level EBprot analysis in a spike‐in dataset. To illustrate the wide applicability of EBprot in different experimental designs, we applied EBprot to a dataset for lung cancer subtype analysis with biological replicates and another dataset for time course phosphoproteome analysis of EGF‐stimulated HeLa cells with multiplexed labeling. Through these examples, we show that the peptide‐level analysis of EBprot is a robust alternative to the existing statistical methods for the DE analysis of labeling‐based quantitative datasets. The software suite is freely available on the Sourceforge website http://ebprot.sourceforge.net/ . All MS data have been deposited in the ProteomeXchange with identifier PXD001426 ( http://proteomecentral.proteomexchange.org/dataset/PXD001426/ ). 相似文献
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A central issue in ecology is that of the factors determining the relative abundance of species within a natural community. The proper application of the principles of statistical physics to species abundance distributions (SADs) shows that simple ecological properties could account for the near universal features observed. These properties are (i) a limit on the number of individuals in an ecological guild and (ii) per capita birth and death rates. They underpin the neutral theory of Hubbell (2001), the master equation approach of
[Volkov et?al., 2003] and [Volkov et?al., 2005] and the idiosyncratic (extreme niche) theory of Pueyo et al. (2007); they result in an underlying log series SAD, regardless of neutral or niche dynamics. The success of statistical mechanics in this application implies that communities are in dynamic equilibrium and hence that niches must be flexible and that temporal fluctuations on all sorts of scales are likely to be important in community structure. 相似文献
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Although mass spectrometers are capable of providing high mass accuracy data, assignment of true monoisotopic precursor ion mass is complicated during data-dependent ion selection for LC-MS/MS analysis of complex mixtures. The complication arises when chromatographic peak widths for a given analyte exceed the time required to acquire a precursor ion mass spectrum. The result is that many measured monoisotopic masses are misassigned due to calculation from a single mass spectrum with poor ion statistics based on only a fraction of the total available ions for a given analyte. Such data in turn produces errors in automated database searches, where precursor m/z value is one search parameter. We propose here a postacquisition approach to correct misassigned monoisotopic m/z values that involves peak detection over the entire elution profile and correction of the precursor ion monoisotopic mass. As a result of using this approach to reprocess shotgun proteomic data we increased peptide sequence assignments by 10% while reducing the estimated false positive ratio from 1 to 0.2%. We also show that 4% of the salvaged identifications may be accounted for by correction of mixed tandem mass spectra resulting from fragmentation of multiple peptides simultaneously, a situation which we refer to as accidental CID. 相似文献
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Recent improvements in proteomic technologies have collectively yielded data sets that far exceed the capabilities of typical low‐throughput interpretation strategies. Unfortunately, tools designed to leverage the “peptide‐centric” content of MS‐based proteomics lag the current rate of data production. Here, we describe Pathway Palette ( http://blaispathways.dfci.harvard.edu ), a freely accessible internet application that enables researchers to easily transition from peptides to biological pathways, while simultaneously retaining the qualitative and quantitative aspects of the underlying MS data. 相似文献
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Claudia Landi Alfonso Carleo Giuseppe Cillis Paola Rottoli 《Expert review of proteomics》2013,10(10):829-835