Bioinformatic analysis of proteomics data

Most biochemical reactions in a cell are regulated by highly specialized proteins, which are the prime mediators of the cellular phenotype. Therefore the identification, quantitation and characterization of all proteins in a cell are of utmost importance to understand the molecular processes that mediate cellular physiology. With the advent of robust and reliable mass spectrometers that are able to analyze complex protein mixtures within a reasonable timeframe, the systematic analysis of all proteins in a cell becomes feasible. Besides the ongoing improvements of analytical hardware, standardized methods to analyze and study all proteins have to be developed that allow the generation of testable new hypothesis based on the enormous pre-existing amount of biological information. Here we discuss current strategies on how to gather, filter and analyze proteomic data sates using available software packages.     

Computational analysis of shotgun proteomics data

Proteomics technology is progressing at an incredible rate. The latest generation of tandem mass spectrometers can now acquire tens of thousands of fragmentation spectra in a matter of hours. Furthermore, quantitative proteomics methods have been developed that incorporate a stable isotope-labeled internal standard for every peptide within a complex protein mixture for the measurement of relative protein abundances. These developments have opened the doors for 'shotgun' proteomics, yet have also placed a burden on the computational approaches that manage the data. With each new method that is developed, the quantity of data that can be derived from a single experiment increases. To deal with this increase, new computational approaches are being developed to manage the data and assess false positives. This review discusses current approaches for analyzing proteomics data by mass spectrometry and identifies present computational limitations and bottlenecks.     

Bioinformatics analysis of mass spectrometry-based proteomics data sets

Proteomics has made tremendous progress, attaining throughput and comprehensiveness so far only seen in genomics technologies. The consequent avalanche of proteome level data poses great analytical challenges for downstream interpretation. We review bioinformatic analysis of qualitative and quantitative proteomic data, focusing on current and emerging paradigms employed for functional analysis, data mining and knowledge discovery from high resolution quantitative mass spectrometric data. Many bioinformatics tools developed for microarrays can be reused in proteomics, however, the uniquely quantitative nature of proteomics data also offers entirely novel analysis possibilities, which directly suggest and illuminate biological mechanisms.     

Statistics for proteomics: a review of tools for analyzing experimental data

Most proteomics experiments make use of 'high throughput' technologies such as 2-DE, MS or protein arrays to measure simultaneously the expression levels of thousands of proteins. Such experiments yield large, high-dimensional data sets which usually reflect not only the biological but also technical and experimental factors. Statistical tools are essential for evaluating these data and preventing false conclusions. Here, an overview is given of some typical statistical tools for proteomics experiments. In particular, we present methods for data preprocessing (e.g. calibration, missing values estimation and outlier detection), comparison of protein expression in different groups (e.g. detection of differentially expressed proteins or classification of new observations) as well as the detection of dependencies between proteins (e.g. protein clusters or networks). We also discuss questions of sample size planning for some of these methods.     

Megavariate data analysis of mass spectrometric proteomics data using latent variable projection method

There are many data mining techniques for processing and general learning of multivariate data. However, we believe the wavelet transformation and latent variable projection method are particularly useful for spectroscopic and chromatographic data. Projection based methods are designed to handle hugely multivariate nature of such data effectively. For the actual analysis of the data we have used latent variable projection methods such as principal component analysis (PCA) and partial least squares projection to latent structures based discriminant analysis (PLS-DA) to analyze the raw data presented to the participants of the First Duke Proteomics Data Mining Conference. PCA was used to solve problem #1 (clustering problem) and the PLS-DA was used to solve problem #2 (classification problem). The idea of internal and external cross-validation was used to validate the model obtained from the classification analysis. The simple two-component PLS-DA model obtained from the analysis performed well. The model has completely separated the two groups from all the data. The same model applied on two-thirds of the data showed good performance by external validation with independent test set of remaining 13 specimens obtained by setting aside the spectra of every third specimen (accuracy of 85%).     

Significance analysis of spectral count data in label-free shotgun proteomics

Spectral counting has become a commonly used approach for measuring protein abundance in label-free shotgun proteomics. At the same time, the development of data analysis methods has lagged behind. Currently most studies utilizing spectral counts rely on simple data transforms and posthoc corrections of conventional signal-to-noise ratio statistics. However, these adjustments can neither handle the bias toward high abundance proteins nor deal with the drawbacks due to the limited number of replicates. We present a novel statistical framework (QSpec) for the significance analysis of differential expression with extensions to a variety of experimental design factors and adjustments for protein properties. Using synthetic and real experimental data sets, we show that the proposed method outperforms conventional statistical methods that search for differential expression for individual proteins. We illustrate the flexibility of the model by analyzing a data set with a complicated experimental design involving cellular localization and time course.     

遥感影像空间分辨率及粒度变化对城市景观格局分析的影响

尺度是景观生态学和遥感领域的重要问题,已有研究主要考虑景观的粒度效应,很少涉及遥感影像空间分辨率对景观格局,尤其是对城市景观格局的影响.基于2000年和2002年分别由ETM 和IRS-PAN影像解译得到的土地利用图,从景观和类型两个层次分析了不同粒度下上海市一城市化样带的景观格局.结果表明,城市景观有明显的尺度效应,空间分辨率和粒度变化都会影响城市景观格局,而道路等线性廊道对尺度变化的敏感则是造成这一影响的主要原因.斑块密度、边界密度、平均斑块分维度、景观聚集度和最大斑块指数的粒度效应比较明显,而景观比例、Shannon多样性指数对粒度变化并不敏感.高分辨率影像适用于城市景观格局,尤其是破碎度的分析,其用于分析的合适粒度是5～10m.     

Detailed interrogation of trypanosome cell biology via differential organelle staining and automated image analysis

Background  

Many trypanosomatid protozoa are important human or animal pathogens. The well defined morphology and precisely choreographed division of trypanosomatid cells makes morphological analysis a powerful tool for analyzing the effect of mutations, chemical insults and changes between lifecycle stages. High-throughput image analysis of micrographs has the potential to accelerate collection of quantitative morphological data. Trypanosomatid cells have two large DNA-containing organelles, the kinetoplast (mitochondrial DNA) and nucleus, which provide useful markers for morphometric analysis; however they need to be accurately identified and often lie in close proximity. This presents a technical challenge. Accurate identification and quantitation of the DNA content of these organelles is a central requirement of any automated analysis method.     

Point perturbation analysis of experimental data

A new method of data analysis is proposed. The method is based on discrete perturbation of experimental data points, which is used to probe the metric of the parameter hyperspace. Perturbation-induced fluctuations in the residual values are analysed by discrete Fourier transform to yield the autocorrelation function and a relaxation length for each experimental point. This parameter provides a quantitative measure of correlation and hence nonrandomness of residuals. The method is applied to the analysis of measurements of the shear viscosity of a 2,6-lutidine/water mixture near the critical point, and to the oxygen and carbon monoxide binding reactions to human hemoglobin. Relaxation profiles are constructed for several experimental data sets. Departure from random behavior in the residuals is discussed in connection with the theoretical interpretations of the phenomenon under consideration.     

Diffprot - software for non-parametric statistical analysis of differential proteomics data

Mass spectrometry-based global proteomics experiments generate large sets of data that can be converted into useful information only with an appropriate statistical approach. We present Diffprot - a software tool for statistical analysis of MS-derived quantitative data. With implemented resampling-based statistical test and local variance estimate, Diffprot allows to draw significant results from small scale experiments and effectively eliminates false positive results. To demonstrate the advantages of this software, we performed two spike-in tests with complex biological matrices, one label-free and one based on iTRAQ quantification; in addition, we performed an iTRAQ experiment on bacterial samples. In the spike-in tests, protein ratios were estimated and were in good agreement with theoretical values; statistical significance was assigned to spiked proteins and single or no false positive results were obtained with Diffprot. We compared the performance of Diffprot with other statistical tests - widely used t-test and non-parametric Wilcoxon test. In contrast to Diffprot, both generated many false positive hits in the spike-in experiment. This proved the superiority of the resampling-based method in terms of specificity, making Diffprot a rational choice for small scale high-throughput experiments, when the need to control the false positive rate is particularly pressing.     

Single-base resolution analysis of DNA epigenome via high-throughput sequencing

Epigenetic changes caused by DNA methylation and histone modifications play important roles in the regulation of various cellular processes and development. Recent discoveries of 5-methylcytosine (5mC) oxidation derivatives including 5-hydroxymethylcytosine (5hmC), 5-formylcytsine (5fC) and 5-carboxycytosine (5caC) in mammalian genome further expand our understanding of the epigenetic regulation. Analysis of DNA modification patterns relies increasingly on sequencing-based profiling methods. A number of different approaches have been established to map the DNA epigenomes with single-base resolution, as represented by the bisulfite-based methods, such as classical bisulfite sequencing (BS-seq), TAB-seq (TET-assisted bisulfite sequencing), oxBS-seq (oxidative bisulfite sequencing) and etc. These methods have been used to generate base-resolution maps of 5mC and its oxidation derivatives in genomic samples. The focus of this review will be to discuss the chemical methodologies that have been developed to detect the cytosine derivatives in the genomic DNA.     

Multi-omics at single-cell resolution: comparison of experimental and data fusion approaches

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Analysing variation in Drosophila aging across independent experimental studies: a meta‐analysis of survival data

Survival records of longevity experiments are a key component in research on aging. However, surprisingly there have been very few cross‐study analyses, besides comparisons of median lifespans or similar summary information. Here, we use a large set of full survival data from various studies to address questions in aging, which are beyond the scope of individual studies. We characterize survival differences between female and male flies of different genetic Drosophila strains, showing significant differences between strains. We further analyse the variation in survival of control cohorts recorded under highly similar conditions within different Drosophila strains. We found that overall transgenic constructs of the UAS/GAL4 expression system which should have no effect (e.g. a GAL4 construct alone) extend lifespan significantly in the w1118 strain. Using a large data set comprised of various studies, we found no evidence for larger lifespan extensions being associated with shorter lifespans of the control in Drosophila. This demonstrates that lifespan extending treatments are not purely rescuing weak backgrounds.     

Improved metabolites of pharmaceutical ingredient grade Ginkgo biloba and the correlated proteomics analysis

Ginkgo biloba is an attractive and traditional medicinal plant, and has been widely used as a phytomedicine in the prevention and treatment of cardiovascular and cerebrovascular diseases. Flavonoids and terpene lactones are the major bioactive components of Ginkgo, whereas the ginkgolic acids (GAs) with strong allergenic properties are strictly controlled. In this study, we tested the content of flavonoids and GAs under ultraviolet‐B (UV‐B) treatment and performed comparative proteomic analyses to determine the differential proteins that occur upon UV‐B radiation. That might play a crucial role in producing flavonoids and GAs. Our phytochemical analyses demonstrated that UV‐B irradiation significantly increased the content of active flavonoids, and decreased the content of toxic GAs. We conducted comparative proteomic analysis of both whole leaf and chloroplasts proteins. In total, 27 differential proteins in the whole leaf and 43 differential proteins in the chloroplast were positively identified and functionally annotated. The proteomic data suggested that enhanced UV‐B radiation exposure activated antioxidants and stress‐responsive proteins as well as reduced the rate of photosynthesis. We demonstrate that UV‐B irradiation pharmaceutically improved the metabolic ingredients of Ginkgo, particularly in terms of reducing GAs. With high UV absorption properties, and antioxidant activities, the flavonoids were likely highly induced as protective molecules following UV‐B irradiation.     

Treatment of missing values for multivariate statistical analysis of gel-based proteomics data

The presence of missing values in gel-based proteomics data represents a real challenge if an objective statistical analysis is pursued. Different methods to handle missing values were evaluated and their influence is discussed on the selection of important proteins through multivariate techniques. The evaluated methods consisted of directly dealing with them during the multivariate analysis with the nonlinear estimation by iterative partial least squares (NIPALS) algorithm or imputing them by using either k-nearest neighbor or Bayesian principal component analysis (BPCA) before carrying out the multivariate analysis. These techniques were applied to data obtained from gels stained with classical postrunning dyes and from DIGE gels. Before applying the multivariate techniques, the normality and homoscedasticity assumptions on which parametric tests are based on were tested in order to perform a sound statistical analysis. From the three tested methods to handle missing values in our datasets, BPCA imputation of missing values showed to be the most consistent method.     

Neural network-based analysis of thiol proteomics data in identifying potential selenium targets

Generation of a monomethylated selenium metabolite is critical for the anticancer activity of selenium. Because of its strong nucleophilicity, the metabolite can react directly with protein thiols to cause redox modification. Here, we report a neural network-based analysis to identify potential selenium targets. A reactive thiol specific reagent, BIAM, was used to monitor thiol proteome changes on 2D gel. We constructed a dynamic model and evaluated the relative importance of proteins mediating the cellular responses to selenium. Information from this study will provide new clues to unravel mechanisms of anticancer action of selenium. High impact selenium targets could also serve as biomarkers to gauge the efficacy of selenium chemoprevention.     

EBprot: Statistical analysis of labeling‐based quantitative proteomics data

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/ ).     

Towards simultaneous analysis of morphological and molecular data in Hymenoptera

Principles and methods of simultaneous analysis in cladistics are reviewed, and the first, preliminary, analysis of combined molecular and morphological data on higher level relationships in Hymenoptera is presented to exemplify these principles. The morphological data from Ronquist et al . (1999) matrix, derived from the character diagnoses of the phylogenetic tree of Rasnitsyn (1988) , are combined with new molecular data for representatives of 10 superfamilies of Hymenoptera by means of optimization alignment. The resulting cladogram supports Apocrita and Aculeata as groups, and the superfamly Chrysidoidea, but not Chalcidoidea, Evanioidea, Vespoidea and Apoidea.