Bayesian modelling of shared gene function

MOTIVATION: Biological assays are often carried out on tissues that contain many cell lineages and active pathways. Microarray data produced using such material therefore reflect superimpositions of biological processes. Analysing such data for shared gene function by means of well-matched assays may help to provide a better focus on specific cell types and processes. The identification of genes that behave similarly in different biological systems also has the potential to reveal new insights into preserved biological mechanisms. RESULTS: In this article, we propose a hierarchical Bayesian model allowing integrated analysis of several microarray data sets for shared gene function. Each gene is associated with an indicator variable that selects whether binary class labels are predicted from expression values or by a classifier which is common to all genes. Each indicator selects the component models for all involved data sets simultaneously. A quantitative measure of shared gene function is obtained by inferring a probability measure over these indicators. Through experiments on synthetic data, we illustrate potential advantages of this Bayesian approach over a standard method. A shared analysis of matched microarray experiments covering (a) a cycle of mouse mammary gland development and (b) the process of in vitro endothelial cell apoptosis is proposed as a biological gold standard. Several useful sanity checks are introduced during data analysis, and we confirm the prior biological belief that shared apoptosis events occur in both systems. We conclude that a Bayesian analysis for shared gene function has the potential to reveal new biological insights, unobtainable by other means. AVAILABILITY: An online supplement and MatLab code are available at http://www.sykacek.net/research.html#mcabf     

Bayesian estimation of NMR restraint potential and weight: a validation on a representative set of protein structures

The classical procedure for nuclear magnetic resonance structure calculation allocates empirical distance ranges and uses historical values for weighting factors. However, Bayesian analysis suggests that there are more optimal choices for potential shape (bounds-free log-harmonic shape) and restraints weights. We compare the classical protocol with the Bayesian approach for more than 300 protein structures. We analyze the conformation similarity to the corresponding X-ray crystal structure, the distribution of the conformations around their average, and independent validation criteria. On average, the log-harmonic potential reduces the difference to the X-ray crystal structure. If the log-harmonic potential is used, the constant weighting tightens the distribution around the average conformation, with respect to the distributions obtained with Bayesian weighting. Conversely, the structure quality is improved by the Bayesian weighting over the classical procedure, whereas constant weighting worsens some criteria. The quality improvement obtained with the log-harmonic potential coupled to Bayesian weighting validates this approach on a representative set of protein structures.     

Constraint-based probabilistic learning of metabolic pathways from tomato volatiles

Clustering and correlation analysis techniques have become popular tools for the analysis of data produced by metabolomics experiments. The results obtained from these approaches provide an overview of the interactions between objects of interest. Often in these experiments, one is more interested in information about the nature of these relationships, e.g., cause-effect relationships, than in the actual strength of the interactions. Finding such relationships is of crucial importance as most biological processes can only be understood in this way. Bayesian networks allow representation of these cause-effect relationships among variables of interest in terms of whether and how they influence each other given that a third, possibly empty, group of variables is known. This technique also allows the incorporation of prior knowledge as established from the literature or from biologists. The representation as a directed graph of these relationship is highly intuitive and helps to understand these processes. This paper describes how constraint-based Bayesian networks can be applied to metabolomics data and can be used to uncover the important pathways which play a significant role in the ripening of fresh tomatoes. We also show here how this methods of reconstructing pathways is intuitive and performs better than classical techniques. Methods for learning Bayesian network models are powerful tools for the analysis of data of the magnitude as generated by metabolomics experiments. It allows one to model cause-effect relationships and helps in understanding the underlying processes.     

基于极大似然准则的生物序列模体的贝叶斯假设检验

针对生物信息学中序列模体的显著性检验问题,提出了一种基于极大似然准则的贝叶斯假设检验方法.将模体的显著性检验转化为多项分布的拟合优度检验问题,选取Dirichlet分布作为多项分布的先验分布并采用Newton-Raphson算法估计Dirichlet分布的超参数,使得数据的预测分布达到最大.应用贝叶斯定理得到贝叶斯因子进行模型选择,用于评价模体检验的统计显著性,这种方法克服了传统多项分布检验中构造检验统计量并计算其在零假设下确切分布的困难.选择JASPAR数据库中107个转录因子结合位点和100组随机模拟数据进行实验,采用皮尔逊积矩相关系数作为评价检验质量的一个标准,发现实验结果好于传统的模体检验的一些方法.     

Factors influencing soay sheep survival: a Bayesian analysis

This article presents a Bayesian analysis of mark-recapture-recovery data on Soay sheep. A reversible jump Markov chain Monte Carlo technique is used to determine age classes of common survival, and to model the survival probabilities in those classes using logistic regression. This involves environmental and individual covariates, as well as random effects. Auxiliary variables are used to impute missing covariates measured on individual sheep. The Bayesian approach suggests different models from those previously obtained using classical statistical methods. Following model averaging, features that were not previously detected, and which are of ecological importance, are identified.     

The art and design of genetic screens: RNA interference

The remarkable gene knockdown technique of RNAi has opened exciting new avenues for genetic screens in model organisms and human cells. Here we describe the current state of the art for RNAi screening, and stress the importance of well-designed assays and of analytical approaches for large-scale screening experiments, from high-throughput screens using simplified homogenous assays to microscopy and whole-animal experiments. Like classical genetic screens in the past, the success of large-scale RNAi surveys depends on a careful development of phenotypic assays and their interpretation in a relevant biological context.     

On the Bayesian analysis of ring-recovery data

Vounatsou and Smith (1995, Biometrics 51, 687-708) describe the modern Bayesian analysis of ring-recovery data. Here we discuss and extend their work. We draw different conclusions from two major data analyses. We emphasize the extreme sensitivity of certain parameter estimates to the choice of prior distribution and conclude that naive use of Bayesian methods in this area can be misleading. Additionally, we explain the discrepancy between the Bayesian and classical analyses when the likelihood surface has a flat ridge. In this case, when there is no unique maximum likelihood estimate, the Bayesian estimators are remarkably precise.     

H-CORE: enabling genome-scale Bayesian analysis of biological systems without prior knowledge

The Bayesian network is a popular tool for describing relationships between data entities by representing probabilistic (in)dependencies with a directed acyclic graph (DAG) structure. Relationships have been inferred between biological entities using the Bayesian network model with high-throughput data from biological systems in diverse fields. However, the scalability of those approaches is seriously restricted because of the huge search space for finding an optimal DAG structure in the process of Bayesian network learning. For this reason, most previous approaches limit the number of target entities or use additional knowledge to restrict the search space. In this paper, we use the hierarchical clustering and order restriction (H-CORE) method for the learning of large Bayesian networks by clustering entities and restricting edge directions between those clusters, with the aim of overcoming the scalability problem and thus making it possible to perform genome-scale Bayesian network analysis without additional biological knowledge. We use simulations to show that H-CORE is much faster than the widely used sparse candidate method, whilst being of comparable quality. We have also applied H-CORE to retrieving gene-to-gene relationships in a biological system (The 'Rosetta compendium'). By evaluating learned information through literature mining, we demonstrate that H-CORE enables the genome-scale Bayesian analysis of biological systems without any prior knowledge.     

Molecular discrimination of phytoseiids associated with the red palm mite Raoiella indica (Acari: Tenuipalpidae) from Mauritius and South Florida

Phytoseiid populations imported from Mauritius for evaluation for a classical biological control program in Florida, USA, were morphologically identified as Amblyseius largoensis Muma, a species associated with the red palm mite in south Florida and the Caribbean. Bayesian analysis and sequence divergences of the mitochondrial 12S rRNA and nuclear Elongation factor--I alpha (EF-Iα) genes and Neighbor-Joining analysis of High-fidelity-RAPD-PCR markers were used to discriminate between the south Florida and Mauritius populations. High-fidelity-RAPD-PCR markers in addition to Bayesian and sequence divergence analyses of the 12S rRNA sequences suggest that the Mauritius and south Florida populations are genetically different but whether these are species or population differences is unknown. The degenerate EF-Iα primers used to survey the phytoseiids amplified two different elongation factor sequences with distinct amino acid translations, the putative EF-Iα and an unknown elongation factor. Variability within the 12S gene was used to develop population-specific primers for identifying the Mauritius phytoseiids in the event they are released in south Florida.     

Discriminant analysis: methods and program.

Because of the disparity in the results obtained using different methods of discriminant analysis, we have written and used a unique program to test them. These methods can be divided into two groups corresponding to a classical probabilistic approach (i.e., Bayesian methods) or a topological approach (i.e., Sebestyen method).     

Bayesian Inference for Gene Expression and Proteomics.

A text that has a systematic account of Bayesian analysis incomputational biology has been needed for a long time. Thisbook is a timely publication entirely devoted to cutting-edgeBayesian methods in genomics and proteomics research and manyof its contributors are leading authorities in the field. Itis thus an indispensable reference for researchers who are interestedin applying Bayesian techniques in their own biological research.Moreover, the book calls for more methodological and theoreticalresearch to     

High-throughput RNAi screening to dissect cellular pathways: A how-to guide

RNA interference (RNAi) has become a powerful tool to dissect cellular pathways and characterize gene functions. The availability of genome-wide RNAi libraries for various model organisms and mammalian cells has enabled high-throughput RNAi screenings. These RNAi screens successfully identified key components that had previously been missed in classical forward genetic screening approaches and allowed the assessment of combined loss-of-function phenotypes. Crucially, the quality of RNAi screening results depends on quantitative assays and the choice of the right biological context. In this review, we provide an overview on the design and application of high-throughput RNAi screens as well as data analysis and candidate validation strategies.     

Dual polarization interferometry characterization of carbohydrate-protein interactions

Dual polarization interferometry (DPI) is an analytical technique that allows the simultaneous determination of thickness, density, and mass of a biological layer on a sensing waveguide surface in real time. The technique was applied to the analysis of carbohydrate-protein interactions. The selected system involved a 12-kDa recombinant fragment of collagen V (HepV) and heparin, a complex polysaccharide. Here we report on the analysis of thickness, density, and mass of surface structures obtained during the binding of HepV to heparin, which is a useful model compound for the sulfated, protein-binding regions of heparan sulfate. This system, which was initially studied for its biological relevance, displayed anomalous behavior in kinetic studies using surface plasmon resonance (SPR) assays that has been attributed to putative conformational changes. It was this putative conformational change that prompted us to investigate the binding using an alternative analytical approach. While using DPI to monitor binding events, a streptavidin layer (surface coverage 2.105 ng mm(-2)) was bound to the sensor surface (92% coverage), which captured 0.105 ng mm(-2) of biotinylated heparin (a stoichiometric ratio of 1:6 heparin-streptavidin). The heparin inserted into the streptavidin layer but was still found to be capable of binding 0.154 ng mm(-2) of HepV, which was also observed to insert into the streptavidin layer. This allowed the reliable calculation of the stoichiometric ratio for the HepV-heparin complex ( approximately 1.7:1.0), which has proved to be difficult to evaluate by SPR assays. Furthermore, real-time analysis of the heparin-HepV interaction by DPI suggested that there was some surface loss (probably of streptavidin) while the binding was occurring rather than the putative conformational change that has been suggested on the basis of kinetic data alone. This gives further insight into the binding mechanism of HepV to heparin.     

Interval estimation of genetic susceptibility for retrospective case-control studies

Background

This article describes classical and Bayesian interval estimation of genetic susceptibility based on random samples with pre-specified numbers of unrelated cases and controls.

Results

Frequencies of genotypes in cases and controls can be estimated directly from retrospective case-control data. On the other hand, genetic susceptibility defined as the expected proportion of cases among individuals with a particular genotype depends on the population proportion of cases (prevalence). Given this design, prevalence is an external parameter and hence the susceptibility cannot be estimated based on only the observed data. Interval estimation of susceptibility that can incorporate uncertainty in prevalence values is explored from both classical and Bayesian perspective. Similarity between classical and Bayesian interval estimates in terms of frequentist coverage probabilities for this problem allows an appealing interpretation of classical intervals as bounds for genetic susceptibility. In addition, it is observed that both the asymptotic classical and Bayesian interval estimates have comparable average length. These interval estimates serve as a very good approximation to the "exact" (finite sample) Bayesian interval estimates. Extension from genotypic to allelic susceptibility intervals shows dependency on phenotype-induced deviations from Hardy-Weinberg equilibrium.

Conclusions

The suggested classical and Bayesian interval estimates appear to perform reasonably well. Generally, the use of exact Bayesian interval estimation method is recommended for genetic susceptibility, however the asymptotic classical and approximate Bayesian methods are adequate for sample sizes of at least 50 cases and controls.     

Quantitative classification of life-style types in predaceous phytoseiid mites

Classification of species into different functional groups based on biological criteria has been a difficult problem in ecology. The difficulty mainly arises because natural classification patterns are not necessarily mutually exclusive. The more group characteristics overlap, the more difficult it is to identify the membership of a species in the overlapping portions of any two groups. In this paper, we present an application of discriminant analysis by creating classification models from life history and morphological data for two specialist and two generalist life-styles type of predaceous phytoseiid mites. Two stages can be distinguished in our method: life-style group membership assignment and trait variable evaluation. We use a Bayesian framework to create a classifier system to locate or assign species within a mixture of trait distributions. The method assumes that a mixture of trait distributions can represent the multiple dimensions of biological data. The mixture is most evident near the boundaries between groups. Because of the complexity of analytical solution, an iterative method is used to estimate the unknown means, variances, and mixing proportion between groups. We also developed a criterion based on information theory to evaluate model performance with different combinations of input variables and different hypotheses. We present a working example of our proposed methods. We apply these methods to the problem of selecting key species for inoculative release and for classical introductions of biological pest control agents.     

Systematic replacement of amides by 1,4-disubstituted[1,2,3]triazoles in Leu-enkephalin and the impact on the delta opioid receptor activity

Using Cu(I)-catalyzed azide–alkyne cycloaddition in a mixed classical organic phase and solid phase peptide synthesis approach, we synthesized four analogs of Leu-enkephalin to systematically replace amides by 1,4-disubstituted[1,2,3]triazoles. The peptidomimetics obtained were characterized by competitive binding, contractility assays and ERK1/2 phosphorylation. The present study reveals that the analog bearing a triazole between Phe and Leu retains some potency, more than all the others, suggesting that the hydrogen bond acceptor capacity of the last amide of Leu-enkephalin is essential for the biological activity of the peptide.     

Bayesian approach to searching for susceptibility genes: Gc2 and EsD1 alleles and multiple sclerosis

Multiple sclerosis (MS) is one of the most common causes of neurological disability in early adulthood. The current literature is interested in identifying biological or DNA markers associated with genetic susceptibility to MS. The aim of this study is to investigate, by means of Bayesian statistical inference, whether the presence of Gc2 (Gc = group-specific component) and/or EsD1 (EsD = esterase D) alleles affects MS susceptibility. Gc and EsD are two classical genetic markers, being the first a serum protein polymorphism, the latter an isoenzyme polymorphism. The interest of the proposed statistical approach of searching for MS susceptibility genes relies on the analysis of two different functions, one function being inferred from our results on 56 unrelated patients from central Italy affected by MS, the other one from Italian and worldwide epidemiological data. The graphical analysis suggests that MS susceptibility is influenced by both Gc2 and EsD1 alleles; and EsD1 allele is more informative than Gc2. These results point out the advantages of the Bayesian approach in searching for susceptibility genes. Furthermore, the significant association between the considered alleles and the susceptibility to MS suggests possible hypotheses about the pathogenesis of the disease.     

Mapping genome-wide QTL of ratio traits with Bayesian shrinkage analysis for its component traits

The ratio trait is defined as a ratio of two regular quantitative traits with normal distribution, which is distinguished from regular quantitative traits in the genetic analysis because it does not follow the normal distribution. On the basis of maximum likelihood method that uses a special linear combination of the two component traits, we develop a Bayesian mapping strategy for ratio traits, which firstly analyzes the two component traits by Bayesian shrinkage method, and then generates a new posterior sample of genetic effects for a ratio trait from ones of population means and genetic effects for the two component traits, finally, infers QTL for the ratio trait via post MCMC analysis for the new posterior sample. A simulation study demonstrates that the new method has higher detecting power of the QTL than maximum likelihood method. An application is illustrated to map genome-wide QTL for relative growth rate of height on soybean.