Informatics and data management in proteomics

Proteomics has become dominated by large amounts of experimental data and interpreted results. This experimental data cannot be effectively used without understanding the fundamental structure of its information content and representing that information in such a way that knowledge can be extracted from it. This review explores the structure of this information with regard to three fundamental issues: the extraction of relevant information from raw data, the scale of the projects involved and the statistical significance of protein identification results.     

Predicting biological networks from genomic data

Continuing improvements in DNA sequencing technologies are providing us with vast amounts of genomic data from an ever-widening range of organisms. The resulting challenge for bioinformatics is to interpret this deluge of data and place it back into its biological context. Biological networks provide a conceptual framework with which we can describe part of this context, namely the different interactions that occur between the molecular components of a cell. Here, we review the computational methods available to predict biological networks from genomic sequence data and discuss how they relate to high-throughput experimental methods.     

A unified framework for unraveling the functional interaction structure of a biomolecular network based on stimulus-response experimental data

We propose a unified framework for the identification of functional interaction structures of biomolecular networks in a way that leads to a new experimental design procedure. In developing our approach, we have built upon previous work. Thus we begin by pointing out some of the restrictions associated with existing structure identification methods and point out how these restrictions may be eased. In particular, existing methods use specific forms of experimental algebraic equations with which to identify the functional interaction structure of a biomolecular network. In our work, we employ an extended form of these experimental algebraic equations which, while retaining their merits, also overcome some of their disadvantages. Experimental data are required in order to estimate the coefficients of the experimental algebraic equation set associated with the structure identification task. However, experimentalists are rarely provided with guidance on which parameters to perturb, and to what extent, to perturb them. When a model of network dynamics is required then there is also the vexed question of sample rate and sample time selection to be resolved. Supplying some answers to these questions is the main motivation of this paper. The approach is based on stationary and/or temporal data obtained from parameter perturbations, and unifies the previous approaches of Kholodenko et al. (PNAS 99 (2002) 12841-12846) and Sontag et al. (Bioinformatics 20 (2004) 1877-1886). By way of demonstration, we apply our unified approach to a network model which cannot be properly identified by existing methods. Finally, we propose an experiment design methodology, which is not limited by the amount of parameter perturbations, and illustrate its use with an in numero example.     

The Wilcoxon signed rank test for paired comparisons of clustered data

The Wilcoxon signed rank test is a frequently used nonparametric test for paired data (e.g., consisting of pre- and posttreatment measurements) based on independent units of analysis. This test cannot be used for paired comparisons arising from clustered data (e.g., if paired comparisons are available for each of two eyes of an individual). To incorporate clustering, a generalization of the randomization test formulation for the signed rank test is proposed, where the unit of randomization is at the cluster level (e.g., person), while the individual paired units of analysis are at the subunit within cluster level (e.g., eye within person). An adjusted variance estimate of the signed rank test statistic is then derived, which can be used for either balanced (same number of subunits per cluster) or unbalanced (different number of subunits per cluster) data, with an exchangeable correlation structure, with or without tied values. The resulting test statistic is shown to be asymptotically normal as the number of clusters becomes large, if the cluster size is bounded. Simulation studies are performed based on simulating correlated ranked data from a signed log-normal distribution. These studies indicate appropriate type I error for data sets with > or =20 clusters and a superior power profile compared with either the ordinary signed rank test based on the average cluster difference score or the multivariate signed rank test of Puri and Sen. Finally, the methods are illustrated with two data sets, (i) an ophthalmologic data set involving a comparison of electroretinogram (ERG) data in retinitis pigmentosa (RP) patients before and after undergoing an experimental surgical procedure, and (ii) a nutritional data set based on a randomized prospective study of nutritional supplements in RP patients where vitamin E intake outside of study capsules is compared before and after randomization to monitor compliance with nutritional protocols.     

Functional data analysis for longitudinal data with informative observation times

In functional data analysis for longitudinal data, the observation process is typically assumed to be noninformative, which is often violated in real applications. Thus, methods that fail to account for the dependence between observation times and longitudinal outcomes may result in biased estimation. For longitudinal data with informative observation times, we find that under a general class of shared random effect models, a commonly used functional data method may lead to inconsistent model estimation while another functional data method results in consistent and even rate-optimal estimation. Indeed, we show that the mean function can be estimated appropriately via penalized splines and that the covariance function can be estimated appropriately via penalized tensor-product splines, both with specific choices of parameters. For the proposed method, theoretical results are provided, and simulation studies and a real data analysis are conducted to demonstrate its performance.     

A transitional model for longitudinal binary data subject to nonignorable missing data

Binary longitudinal data are often collected in clinical trials when interest is on assessing the effect of a treatment over time. Our application is a recent study of opiate addiction that examined the effect of a new treatment on repeated urine tests to assess opiate use over an extended follow-up. Drug addiction is episodic, and a new treatment may affect various features of the opiate-use process such as the proportion of positive urine tests over follow-up and the time to the first occurrence of a positive test. Complications in this trial were the large amounts of dropout and intermittent missing data and the large number of observations on each subject. We develop a transitional model for longitudinal binary data subject to nonignorable missing data and propose an EM algorithm for parameter estimation. We use the transitional model to derive summary measures of the opiate-use process that can be compared across treatment groups to assess treatment effect. Through analyses and simulations, we show the importance of properly accounting for the missing data mechanism when assessing the treatment effect in our example.     

Integrating vegetation field surveys with remotely sensed data

Summary   This paper explores data compatibility issues arising from the assessment of remnant native vegetation condition using satellite remote sensing and field-based data. Space-borne passive remote sensing is increasingly used as a way of providing a total sample and synoptic overview of the spectral and spatial characteristics of native vegetation canopies at a regional scale. However, integrating field-collected data often not designed for integration with remotely sensed data can lead to data compatibility issues. Subsequent problems associated with the integration of unsuited datasets can contribute to data uncertainty and result in inconclusive findings. It is these types of problems (and potential solutions) that form the basis of this paper. In other words, how can field surveys be designed to support and improve compatibility with remotely sensed total surveys? Key criteria were identified for consideration when designing field-based surveys of native vegetation condition (and other similar applications) with the intent to incorporate remotely sensed data. The criteria include recommendations for the siting of plots, the need for reference location plots, the number of sample sites and plot size and distribution, within a study area. The difficulties associated with successfully integrating these data are illustrated using real examples taken from a study of the vegetation in the Little River Catchment, New South Wales, Australia.     

Exploratory data analysis of DNA microarrays by multivariate curve resolution

In this work, the application of a multivariate curve resolution procedure based on alternating least squares optimization (MCR-ALS) for the analysis of data from DNA microarrays is proposed. For this purpose, simulated and publicly available experimental data sets have been analyzed. Application of MCR-ALS, a method that operates without the use of any training set, has enabled the resolution of the relevant information about different cancer lines classification using a set of few components; each of these defined by a sample and a pure gene expression profile. From resolved sample profiles, a classification of samples according to their origin is proposed. From the resolved pure gene expression profiles, a set of over- or underexpressed genes that could be related to the development of cancer diseases has been selected. Advantages of the MCR-ALS procedure in relation to other previously proposed procedures such as principal component analysis are discussed.     

Statistical methods suitable for the analysis of plant tissue culture data

Statistical analyses are an essential part of biological research. Statistical methods are available to biological researchers that range from very simple to extremely complex. Therefore, caution should be used when selecting a statistical method. When possible it is best to avoid complicated statistical procedures that are difficult to interpret and may hinder the researcher's ability to make treatment comparisons. Instead a method should be chosen that compliments a logical and practical treatment design. Statistics should be used as a tool to compare treatments of interest and should not dictate the treatments. Experimental designs should take into account the eventual analysis, otherwise one could conceive of a design that could not be analyzed or, when analyzed, would not answer the desired questions. Therefore, time should be spent before conducting an experiment to plan an experimental design and analysis that best compliments the treatment scheme and questions to be answered. The purpose of this paper is to present examples of experimental designs, means separation procedures, data transformations and presentation methods suitable for plant cell and tissue culture data.Abbreviations ANOVA analysis of variance - BA benzyladenine - CV coefficient of variation - DF degrees of freedom - IAA indole-3-acetic acid - IBA indole-3-butyric acid - LOF lack-of-fit - MSE mean square error - P-ITB phenyl indole-3-thiolobutyrate - S standard deviation - SE standard error of the mean - TDZ thidiazuron     

Sharing digital micrographs and other data files between computers

It ought to be easy to exchange digital micrographs and other computer data files with a colleague even on another continent. In practice, this often is not the case. The advantages and disadvantages of various methods that are available for exchanging data files between computers are discussed. When possible, data should be transferred through computer networking. When data are to be exchanged locally between computers with similar operating systems, the use of a local area network is recommended. For computers in commercial or academic environments that have dissimilar operating systems or are more widely spaced, the use of FTPs is recommended. Failing this, posting the data on a website and transferring by hypertext transfer protocol is suggested. If peer to peer exchange between computers in domestic environments is needed, the use of Messenger services such as Microsoft Messenger or Yahoo Messenger is the method of choice. When it is not possible to transfer the data files over the internet, single use, writable CD ROMs are the best media for transferring data. If for some reason this is not possible, DVD-R/RW, DVD+R/RW, 100 MB ZIP disks and USB flash media are potentially useful media for exchanging data files.     

Sharing digital micrographs and other data files between computers

It ought to be easy to exchange digital micrographs and other computer data files with a colleague even on another continent. In practice, this often is not the case. The advantages and disadvantages of various methods that are available for exchanging data files between computers are discussed. When possible, data should be transferred through computer networking. When data are to be exchanged locally between computers with similar operating systems, the use of a local area network is recommended. For computers in commercial or academic environments that have dissimilar operating systems or are more widely spaced, the use of FTPs is recommended. Failing this, posting the data on a website and transferring by hypertext transfer protocol is suggested. If peer to peer exchange between computers in domestic environments is needed, the use of Messenger services such as Microsoft Messenger or Yahoo Messenger is the method of choice. When it is not possible to transfer the data files over the internet, single use, writable CD ROMs are the best media for transferring data. If for some reason this is not possible, DVD-R/RW, DVD+R/RW, 100 MB ZIP disks and USB flash media are potentially useful media for exchanging data files.     

Application of Bayesian networks on large-scale biological data

The investigation of the interplay between genes, proteins, metabolites and diseases plays a central role in molecular and cellular biology. Whole genome sequencing has made it possible to examine the behavior of all the genes in a genome by high-throughput experimental techniques and to pinpoint molecular interactions on a genome-wide scale, which form the backbone of systems biology. In particular, Bayesian network (BN) is a powerful tool for the ab-initial identification of causal and non-causal relationships between biological factors directly from experimental data. However, scalability is a crucial issue when we try to apply BNs to infer such interactions. In this paper, we not only introduce the Bayesian network formalism and its applications in systems biology, but also review recent technical developments for scaling up or speeding up the structural learning of BNs, which is important for the discovery of causal knowledge from large-scale biological datasets. Specifically, we highlight the basic idea, relative pros and cons of each technique and discuss possible ways to combine different algorithms towards making BN learning more accurate and much faster.     

TOUCHSTONEX: protein structure prediction with sparse NMR data

TOUCHSTONEX, a new method for folding proteins that uses a small number of long-range contact restraints derived from NMR experimental NOE (nuclear Overhauser enhancement) data, is described. The method employs a new lattice-based, reduced model of proteins that explicitly represents C(alpha), C(beta), and the sidechain centers of mass. The force field consists of knowledge-based terms to produce protein-like behavior, including various short-range interactions, hydrogen bonding, and one-body, pairwise, and multibody long-range interactions. Contact restraints were incorporated into the force field as an NOE-specific pairwise potential. We evaluated the algorithm using a set of 125 proteins of various secondary structure types and lengths up to 174 residues. Using N/8 simulated, long-range sidechain contact restraints, where N is the number of residues, 108 proteins were folded to a C(alpha)-root-mean-square deviation (RMSD) from native below 6.5 A. The average RMSD of the lowest RMSD structures for all 125 proteins (folded and unfolded) was 4.4 A. The algorithm was also applied to limited experimental NOE data generated for three proteins. Using very few experimental sidechain contact restraints, and a small number of sidechain-main chain and main chain-main chain contact restraints, we folded all three proteins to low-to-medium resolution structures. The algorithm can be applied to the NMR structure determination process or other experimental methods that can provide tertiary restraint information, especially in the early stage of structure determination, when only limited data are available.     

A new dimension in combining data? The use of morphology and phylogenomic data in metazoan systematics

Giribet, G. 2010. A new dimension in combining data? The use of morphology and phylogenomic data in metazoan systematics. —Acta Zoologica (Stockholm) 91 : 11–19 Animal phylogenies have been traditionally inferred by using the character state information derived from the observation of a diverse array of morphological and anatomical features, but the incorporation of molecular data into the toolkit of phylogenetic characters has shifted drastically the way researchers infer phylogenies. A main reason for this is the ease at which molecular data can be obtained, compared to, e.g., traditional histological and microscopical techniques. Researchers now routinely use genomic data for reconstructing relationships among animal phyla (using whole genomes or Expressed Sequence Tags) but the amount of morphological data available to study the same phylogenetic patterns has not grown accordingly. Given the disparity between the amounts of molecular and morphological data, some authors have questioned entire morphological programs. In this review I discuss issues related to the combinability of genomic and morphological data, the informativeness of each set of characters, and conclude with a discussion of how morphology could be made scalable by utilizing new techniques that allow for non‐intrusive examination of large amounts of preserved museum specimens. Morphology should therefore remains a strong field in evolutionary and comparative biology, as it continues to provide information for inferring phylogenetic patterns, is an important complement for the patterns derived from the molecular data, and it is the common nexus that allows studying fossil taxa with large data sets of molecular data.     

Multiply imputing missing values arising by design in transplant survival data

In this article, we address a missing data problem that occurs in transplant survival studies. Recipients of organ transplants are followed up from transplantation and their survival times recorded, together with various explanatory variables. Due to differences in data collection procedures in different centers or over time, a particular explanatory variable (or set of variables) may only be recorded for certain recipients, which results in this variable being missing for a substantial number of records in the data. The variable may also turn out to be an important predictor of survival and so it is important to handle this missing-by-design problem appropriately. Consensus in the literature is to handle this problem with complete case analysis, as the missing data are assumed to arise under an appropriate missing at random mechanism that gives consistent estimates here. Specifically, the missing values can reasonably be assumed not to be related to the survival time. In this article, we investigate the potential for multiple imputation to handle this problem in a relevant study on survival after kidney transplantation, and show that it comprehensively outperforms complete case analysis on a range of measures. This is a particularly important finding in the medical context as imputing large amounts of missing data is often viewed with scepticism.     

Statistical mechanics analysis of sparse data

Inferential structure determination uses Bayesian theory to combine experimental data with prior structural knowledge into a posterior probability distribution over protein conformational space. The posterior distribution encodes everything one can say objectively about the native structure in the light of the available data and additional prior assumptions and can be searched for structural representatives. Here an analogy is drawn between the posterior distribution and the canonical ensemble of statistical physics. A statistical mechanics analysis assesses the complexity of a structure calculation globally in terms of ensemble properties. Analogs of the free energy and density of states are introduced; partition functions evaluate the consistency of prior assumptions with data. Critical behavior is observed with dwindling restraint density, which impairs structure determination with too sparse data. However, prior distributions with improved realism ameliorate the situation by lowering the critical number of observations. An in-depth analysis of various experimentally accessible structural parameters and force field terms will facilitate a statistical approach to protein structure determination with sparse data that avoids bias as much as possible.     

Semiparametric count data regression for self-reported mental health

‘‘For how many days during the past 30 days was your mental health not good?” The responses to this question measure self-reported mental health and can be linked to important covariates in the National Health and Nutrition Examination Survey (NHANES). However, these count variables present major distributional challenges: The data are overdispersed, zero-inflated, bounded by 30, and heaped in 5- and 7-day increments. To address these challenges—which are especially common for health questionnaire data—we design a semiparametric estimation and inference framework for count data regression. The data-generating process is defined by simultaneously transforming and rounding (star ) a latent Gaussian regression model. The transformation is estimated nonparametrically and the rounding operator ensures the correct support for the discrete and bounded data. Maximum likelihood estimators are computed using an expectation-maximization (EM) algorithm that is compatible with any continuous data model estimable by least squares. star regression includes asymptotic hypothesis testing and confidence intervals, variable selection via information criteria, and customized diagnostics. Simulation studies validate the utility of this framework. Using star regression, we identify key factors associated with self-reported mental health and demonstrate substantial improvements in goodness-of-fit compared to existing count data regression models.     

Fuzzy clustering of fuzzy ecological data

Besides the problem of searching for effective methods for data analysis there are some additional problems with handling data of high uncertainty. Uncertainty problems often arise in an analysis of ecological data, e.g. in the cluster analysis of ecological data. Conventional clustering methods based on Boolean logic ignore the continuous nature of ecological variables and the uncertainty of ecological data. That can result in misclassification or misinterpretation of the data structure. Clusters with fuzzy boundaries reflect better the continuous character of ecological features. But the problem is, that the common clustering methods (like the fuzzy c-means method) are only designed for treating crisp data, that means they provide a fuzzy partition only for crisp data (e.g. exact measurement data). This paper presents the extension and implementation of the method of fuzzy clustering of fuzzy data proposed by Yang and Liu [Yang, M.-S. and Liu, H-H, 1999. Fuzzy clustering procedures for conical fuzzy vector data. Fuzzy Sets and Systems, 106, 189-200.]. The imprecise data can be defined as multidimensional fuzzy sets with not sharply formed boundaries (in the form of the so-called conical fuzzy vectors). They can then be used for the fuzzy clustering together with crisp data. That can be particularly useful when information is not available about the variances which describe the accuracy of the data and probabilistic approaches are impossible. The method proposed by Yang has been extended and implemented for the Fuzzy Clustering System EcoFucs developed at the University of Kiel. As an example, the paper presents the fuzzy cluster analysis of chemicals according to their ecotoxicological properties. The uncertainty and imprecision of ecotoxicological data are very high because of the use of various data sources, various investigation tests and the difficulty of comparing these data. The implemented method can be very helpful in searching for an adequate partition of ecological data into clusters with similar properties.     

异源蛋白质相互作用数据整合算法的进展

蛋白质相互作用在生物学过程和细胞功能行使中起核心作用。高通量技术的应用结合计算机预测方法的发展,使得直接和间接来源的蛋白质相互作用数据得到了大规模的增加。如何系统地整合这些数据并从中提取有用的信息是一项挑战,这也促使了许多整合算法应运而生。本文综述了八种整合蛋白质相互作用数据源的方法：投票、支持向量机、朴素贝叶斯、逻辑斯蒂回归、决策树、随机森林、基于随机森林的k-近邻法以及混合属性分类等方法。