CiteULike: maryee's library [78 articles]

Yeast two-hybrid contributions to interactome mapping

Jodi Parrish — 2006-08-08T09:50:22-00:00

Current Opinion in Biotechnology, Vol. 17, No. 4. (August 2006), pp. 387-393.

Interactome mapping, the systematic identification of protein interactions within an organism, promises to facilitate systems-level studies of biological processes. Using in vitro technologies that measure specific protein interactions, static maps are being generated that include many of the protein networks that occur in vivo. Most of the binary protein interaction data currently available was generated by large-scale yeast two-hybrid screens. Recent efforts to map interactions in model organisms and in humans illustrate the promise and some of the limitations of the two-hybrid approach. Although these maps are incomplete and include false positives, they are proving useful as a framework around which to elaborate and model the in vivo interactome.

Assessing reliability of gene clusters from gene expression data.

K Zhang — 2006-12-05T22:53:11-00:00

Funct Integr Genomics, Vol. 1, No. 3. (November 2000), pp. 156-173.

The rapid development of microarray technologies has raised many challenging problems in experiment design and data analysis. Although many numerical algorithms have been successfully applied to analyze gene expression data, the effects of variations and uncertainties in measured gene expression levels across samples and experiments have been largely ignored in the literature. In this article, in the context of hierarchical clustering algorithms, we introduce a statistical resampling method to assess the reliability of gene clusters identified from any hierarchical clustering method. Using the clustering trees constructed from the resampled data, we can evaluate the confidence value for each node in the observed clustering tree. A majority-rule consensus tree can be obtained, showing clusters that only occur in a majority of the resampled trees. We illustrate our proposed methods with applications to two published data sets. Although the methods are discussed in the context of hierarchical clustering methods, they can be applied with other cluster-identification methods for gene expression data to assess the reliability of any gene cluster of interest.

The elusive yeast interactome.

J Goll — 2006-11-21T19:51:13-00:00

Genome Biol, Vol. 7, No. 6. (2006)

Simple eukaryotic cells such as yeast could contain around 800 protein complexes, as two new comprehensive studies show. But slightly different approaches resulted in surprising differences between the two datasets, showing that more work is required to get a complete picture of the yeast interactome.

A Crossed Random Effects Model for Unbalanced Data with Applications in Cross-Sectional and Longitudinal Research

Stephen Raudenbush — 2006-10-24T23:06:28-00:00

Journal of Educational Statistics, Vol. 18, No. 4. (1993), pp. 321-349.

Hierarchical linear models have found widespread application when the data have a nested structure-for example, when students are nested within classrooms (a two-level nested structure) or students are nested within classrooms and classrooms are nested within schools (a three-level nested structure). Often, however, the data will have a more complex nested structure. In Example 1, students are nested within both neighborhoods and schools; however, a school can draw students from multiple neighborhoods, and a neighborhood can send students to multiple schools. In Example 2, children are nested within classrooms during the first year of the study; however, each child finds himself or herself with a new teacher and a new set of classmates during each subsequent year of the study. By combining Lindley and Smith's (1972) concepts of exchangeability between and within regressions, this article formulates a "crossed random effects" model that applies to such data, provides maximum likelihood estimates via the EM algorithm, and illustrates application to study (a) neighborhood and school effects on educational attainment in Scotland and (b) classroom effects on mathematics learning during the primary years in the United States.

Learning to predict protein-protein interactions from protein sequences.

SM Gomez — 2006-10-18T14:16:47-00:00

Bioinformatics, Vol. 19, No. 15. (12 October 2003), pp. 1875-1881.

In order to understand the molecular machinery of the cell, we need to know about the multitude of protein-protein interactions that allow the cell to function. High-throughput technologies provide some data about these interactions, but so far that data is fairly noisy. Therefore, computational techniques for predicting protein-protein interactions could be of significant value. One approach to predicting interactions in silico is to produce from first principles a detailed model of a candidate interaction. We take an alternative approach, employing a relatively simple model that learns dynamically from a large collection of data. In this work, we describe an attraction-repulsion model, in which the interaction between a pair of proteins is represented as the sum of attractive and repulsive forces associated with small, domain- or motif-sized features along the length of each protein. The model is discriminative, learning simultaneously from known interactions and from pairs of proteins that are known (or suspected) not to interact. The model is efficient to compute and scales well to very large collections of data. In a cross-validated comparison using known yeast interactions, the attraction-repulsion method performs better than several competing techniques.

Correlated sequence-signatures as markers of protein-protein interaction.

E Sprinzak — 2006-06-06T19:02:36-00:00

J Mol Biol, Vol. 311, No. 4. (24 August 2001), pp. 681-692.

As protein-protein interaction is intrinsic to most cellular processes, the ability to predict which proteins in the cell interact can aid significantly in identifying the function of newly discovered proteins, and in understanding the molecular networks they participate in. Here we demonstrate that characteristic pairs of sequence-signatures can be learned from a database of experimentally determined interacting proteins, where one protein contains the one sequence-signature and its interacting partner contains the other sequence-signature. The sequence-signatures that recur in concert in various pairs of interacting proteins are termed correlated sequence-signatures, and it is proposed that they can be used for predicting putative pairs of interacting partners in the cell. We demonstrate the potential of this approach on a comprehensive database of experimentally determined pairs of interacting proteins in the yeast Saccharomyces cerevisiae. The proteins in this database have been characterized by their sequence-signatures, as defined by the InterPro classification. A statistical analysis performed on all possible combinations of sequence-signature pairs has identified those pairs that are over-represented in the database of yeast interacting proteins. It is demonstrated how the use of the correlated sequence-signatures as identifiers of interacting proteins can reduce significantly the search space, and enable directed experimental interaction screens.

Pvclust: an R package for assessing the uncertainty in hierarchical clustering.

R Suzuki — 2006-09-02T10:32:29-00:00

Bioinformatics, Vol. 22, No. 12. (15 June 2006), pp. 1540-1542.

SUMMARY: Pvclust is an add-on package for a statistical software R to assess the uncertainty in hierarchical cluster analysis. Pvclust can be used easily for general statistical problems, such as DNA microarray analysis, to perform the bootstrap analysis of clustering, which has been popular in phylogenetic analysis. Pvclust calculates probability values (p-values) for each cluster using bootstrap resampling techniques. Two types of p-values are available: approximately unbiased (AU) p-value and bootstrap probability (BP) value. Multiscale bootstrap resampling is used for the calculation of AU p-value, which has superiority in bias over BP value calculated by the ordinary bootstrap resampling. In addition the computation time can be enormously decreased with parallel computing option.

A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules

Joshua Stuart — 2005-09-24T15:56:37-00:00

Science, Vol. 302, No. 5643. (10 October 2003), pp. 249-255.

To elucidate gene function on a global scale, we identified pairs of genes that are coexpressed over 3182 DNA microarrays from humans, flies, worms, and yeast. We found 22,163 such coexpression relationships, each of which has been conserved across evolution. This conservation implies that the coexpression of these gene pairs confers a selective advantage and therefore that these genes are functionally related. Manyof these relationships provide strong evidence for the involvement of new genes in core biological functions such as the cell cycle, secretion, and protein expression. We experimentallyconfirmed the predictions implied bysome of these links and identified cell proliferation functions for several genes. By assembling these links into a gene-coexpression network, we found several components that were animal-specific as well as interrelationships between newly evolved and ancient modules.

Coexpression analysis of human genes across many microarray data sets.

HK Lee — 2006-09-08T15:43:38-00:00

Genome Res, Vol. 14, No. 6. (June 2004), pp. 1085-1094.

We present a large-scale analysis of mRNA coexpression based on 60 large human data sets containing a total of 3924 microarrays. We sought pairs of genes that were reliably coexpressed (based on the correlation of their expression profiles) in multiple data sets, establishing a high-confidence network of 8805 genes connected by 220,649 "coexpression links" that are observed in at least three data sets. Confirmed positive correlations between genes were much more common than confirmed negative correlations. We show that confirmation of coexpression in multiple data sets is correlated with functional relatedness, and show how cluster analysis of the network can reveal functionally coherent groups of genes. Our findings demonstrate how the large body of accumulated microarray data can be exploited to increase the reliability of inferences about gene function.

A systematic statistical linear modeling approach to oligonucleotide array experiments

Tzu-Ming Chu — 2006-09-08T00:09:59-00:00

Mathematical Biosciences, Vol. 176, No. 1. (March 2002), pp. 35-51.

Assessing Gene Significance from cDNA Microarray Expression Data via Mixed Models

Wolfinger — 2006-09-08T00:07:51-00:00

J Comput Biol., Vol. 8, No. 6. (2001), pp. 625-637.

Application of a priori established gene sets to discover biologically important differential expression in microarray data

Andrea Bild — 2006-07-26T15:13:39-00:00

PNAS, Vol. 102, No. 43. (25 October 2005), pp. 15278-15279.

10.1073/pnas.0507477102

Linear models and empirical bayes methods for assessing differential expression in microarray experiments.

GK Smyth — 2006-05-25T23:00:23-00:00

Stat Appl Genet Mol Biol, Vol. 3, No. 1. (2004)

The problem of identifying differentially expressed genes in designed microarray experiments is considered. Lonnstedt and Speed (2002) derived an expression for the posterior odds of differential expression in a replicated two-color experiment using a simple hierarchical parametric model. The purpose of this paper is to develop the hierarchical model of Lonnstedt and Speed (2002) into a practical approach for general microarray experiments with arbitrary numbers of treatments and RNA samples. The model is reset in the context of general linear models with arbitrary coefficients and contrasts of interest. The approach applies equally well to both single channel and two color microarray experiments. Consistent, closed form estimators are derived for the hyperparameters in the model. The estimators proposed have robust behavior even for small numbers of arrays and allow for incomplete data arising from spot filtering or spot quality weights. The posterior odds statistic is reformulated in terms of a moderated t-statistic in which posterior residual standard deviations are used in place of ordinary standard deviations. The empirical Bayes approach is equivalent to shrinkage of the estimated sample variances towards a pooled estimate, resulting in far more stable inference when the number of arrays is small. The use of moderated t-statistics has the advantage over the posterior odds that the number of hyperparameters which need to estimated is reduced; in particular, knowledge of the non-null prior for the fold changes are not required. The moderated t-statistic is shown to follow a t-distribution with augmented degrees of freedom. The moderated t inferential approach extends to accommodate tests of composite null hypotheses through the use of moderated F-statistics. The performance of the methods is demonstrated in a simulation study. Results are presented for two publicly available data sets.

Use of within-array replicate spots for assessing differential expression in microarray experiments.

Gordon K Smyth — 2005-04-19T15:32:06-00:00

Bioinformatics (18 January 2005)

MOTIVATION: Spotted arrays are often printed with probes in duplicate or triplicate, but current methods for assessing differential expression are not able to make full use of the resulting information. Usual practice is to average the duplicate or triplicate results for each probe before assessing differential expression. This loses valuable information about gene-wise variability. RESULTS: A method is proposed for extracting more information from within-array replicate spots in microarray experiments by estimating the strength of the correlation between them. The method involves fitting separate linear models to the expression data for each gene but with a common value for the between-replicate correlation. The method greatly improves the precision with which the genewise variances are estimated and thereby improves inference methods designed to identify differentially expressed genes. The method may be combined with empirical Bayes methods for moderating the genewise variances between genes. The method is validated using data from a microarray experiment involving calibration and ratio control spots in conjunction with spiked-in RNA. Comparing results for calibration and ratio control spots shows that the common correlation method results in substantially better discrimination of differentially expressed genes from those which are not. The spike-in experiment also confirms that the results may be further improved by empirical Bayes smoothing of the variances when the sample size is small. AVAILABILITY: The methodology is implemented in the limma software package for R, available from the CRAN repository http://www.r-project.org.

Correspondence analysis of microarray time-course data in case-control design.

Q Tan — 2006-07-13T23:17:17-00:00

J Biomed Inform, Vol. 37, No. 5. (October 2004), pp. 358-365.

Although different statistical approaches have been proposed for analyzing microarray time-course data, method for analyzing such data collected using the popular case-control design in clinical investigations has not been proposed perhaps due to the increased complexity for the existing parametric or non-parametric approaches. In this paper, we introduce a new multivariate data analyzing technique, the correspondence analysis, to analyze the high dimensional microarray time-course data in case-control design. We show, through an example on type 2 diabetes, how the nice features of the correspondence analysis can be use to explore the various time-course gene expression profiles that exist in the data. By coordinating and examining the projections on the reduced dimensions by both the genes and the time-course experiments, we are able to identify important genes and time-course patterns and make inferences on their biological relevance. Using the sample replicates, we propose a bootstrap procedure for inferring the significance of contributions on the leading dimensions by both the time-course experiments and the genes. Striking differences in the time-course patterns in the normal controls and diabetes patients have been revealed. In addition, the method also identifies genes that display similar or comparable time-course expression patterns shared by both the cases and the controls. We conclude that our correspondence analysis based approach can be a useful tool for analyzing high dimensional microarray data collected in clinical investigations.

A new closed multiple testing procedure for hierarchical families of hypotheses

Dror Rom — 2006-06-15T00:39:50-00:00

Journal of Statistical Planning and Inference, Vol. 46, No. 3. (15 August 1995), pp. 265-275.

A new closed testing procedure is presented for testing hierarchical families of hypotheses of equality of population means. The procedure is an improved power enhancement of the Shaffer (J. Amer. Statist. Assoc. 81 (1986) 826-831) algorithm and is based on an approach due to Peritz (unpublished manuscript 1970). Its use is illustrated with the reanalysis of a well-known data set.

The Control of the False Discovery Rate in Multiple Testing under Dependency

Y Benjamini — 2006-06-15T00:27:06-00:00

Annals of Statistics (2001)

Identifying differentially expressed genes using false discovery rate controlling procedures.

A Reiner — 2006-06-03T01:11:05-00:00

Bioinformatics, Vol. 19, No. 3. (12 February 2003), pp. 368-375.

MOTIVATION: DNA microarrays have recently been used for the purpose of monitoring expression levels of thousands of genes simultaneously and identifying those genes that are differentially expressed. The probability that a false identification (type I error) is committed can increase sharply when the number of tested genes gets large. Correlation between the test statistics attributed to gene co-regulation and dependency in the measurement errors of the gene expression levels further complicates the problem. In this paper we address this very large multiplicity problem by adopting the false discovery rate (FDR) controlling approach. In order to address the dependency problem, we present three resampling-based FDR controlling procedures, that account for the test statistics distribution, and compare their performance to that of the naïve application of the linear step-up procedure in Benjamini and Hochberg (1995). The procedures are studied using simulated microarray data, and their performance is examined relative to their ease of implementation. RESULTS: Comparative simulation analysis shows that all four FDR controlling procedures control the FDR at the desired level, and retain substantially more power then the family-wise error rate controlling procedures. In terms of power, using resampling of the marginal distribution of each test statistics substantially improves the performance over the naïve one. The highest power is achieved, at the expense of a more sophisticated algorithm, by the resampling-based procedures that resample the joint distribution of the test statistics and estimate the level of FDR control. AVAILABILITY: An R program that adjusts p-values using FDR controlling procedures is freely available over the Internet at www.math.tau.ac.il/~ybenja.

Markov modelling of immunological and virological states in HIV-1 infected patients.

E Mathieu — 2006-06-14T19:24:18-00:00

Biom J, Vol. 47, No. 6. (December 2005), pp. 834-846.

The purpose of this study was to evaluate the evolution of HIV infected patients and to bring out some significant factors associated with this pathology. The main criteria revealing the State of illness is viral load measurement (VL). However the CD4 lymphocytes also represent an important marker as these reflect the State of the immune reservoir. Many studies have been carried out in this field and different models have been proposed with a view to a better understanding of this disease. Multi State Markov models defined in terms of CD4 counts, or in terms of viral load, have proved to be very useful tools for modelling HIV disease progression. The model we have developed in this study is based on both the CD4 lymphocytes counts and VL. Markov models are characterized by transition intensities. In this paper we explored several structures in succession. First, we used a homogeneous continuous time Markov process with four states defined by crossed values of CD4 and VL in a given patient at a given time. Then, the effect of certain covariates on the infection process was introduced into the model via the transition intensity functions, as with a Cox regression model. Since the hypothesis of homogeneity may be unrealistic in certain cases, we also considered piecewise homogeneous Markov models. Finally, the effects of covariates and time were combined in a piecewise homogeneous model with a covariate. We applied these methods to data from 1313 HIV-infected patients included in the NADIS cohort.

Microarray analysis and tumor classification.

J Quackenbush — 2006-06-14T14:22:18-00:00

N Engl J Med, Vol. 354, No. 23. (8 June 2006), pp. 2463-2472.

Clustering microarray gene expression data using weighted Chinese restaurant process.

Zhaohui Qin — 2006-06-14T14:21:19-00:00

Bioinformatics (9 June 2006)

MOTIVATION: Clustering microarray gene expression data is a powerful tool for elucidating co-regulatory relationships among genes. Many different clustering techniques have been successfully applied and the results are promising. However, substantial fluctuation contained in microarray data, lack of knowledge on the number of clusters, and complex regulatory mechanisms underlying biological systems make the clustering problems tremendously challenging. RESULTS: We devised an improved model-based, Bayesian approach to cluster microarray gene expression data. Cluster assignment is carried out by an iterative weighted Chinese restaurant seating scheme such that the optimal number of clusters can be determined simultaneously with cluster assignment. The predictive updating technique was applied to improve the efficiency of the Gibbs sampler. An additional step is added during reassignment to allow genes that display complex correlation relationships such as time-shifted and/or inverted to be clustered together. Analysis done on a real dataset showed that as much as 30% of significant genes clustered in the same group display complex relationships with the consensus pattern of the cluster. Other notable features including automatic handling of missing data, quantitative measures of cluster strength and assignment confidence. Synthetic and real microarray gene expression datasets were analyzed to demonstrate its performance. AVAILABILITY: A computer program named Chinese restaurant cluster (CRC) has been developed based on this algorithm. The program can be downloaded at http://www.sph.umich.edu/csg/qin/CRC/. SUPPLEMENTARY INFORMATION: http://www.sph.umich.edu/csg/qin/CRC/.

Visualization methods for statistical analysis of microarray clusters.

MA Hibbs — 2006-06-14T14:19:45-00:00

BMC Bioinformatics, Vol. 6 (2005)

BACKGROUND: The most common method of identifying groups of functionally related genes in microarray data is to apply a clustering algorithm. However, it is impossible to determine which clustering algorithm is most appropriate to apply, and it is difficult to verify the results of any algorithm due to the lack of a gold-standard. Appropriate data visualization tools can aid this analysis process, but existing visualization methods do not specifically address this issue. RESULTS: We present several visualization techniques that incorporate meaningful statistics that are noise-robust for the purpose of analyzing the results of clustering algorithms on microarray data. This includes a rank-based visualization method that is more robust to noise, a difference display method to aid assessments of cluster quality and detection of outliers, and a projection of high dimensional data into a three dimensional space in order to examine relationships between clusters. Our methods are interactive and are dynamically linked together for comprehensive analysis. Further, our approach applies to both protein and gene expression microarrays, and our architecture is scalable for use on both desktop/laptop screens and large-scale display devices. This methodology is implemented in GeneVAnD (Genomic Visual ANalysis of Datasets) and is available at http://function.princeton.edu/GeneVAnD. CONCLUSION: Incorporating relevant statistical information into data visualizations is key for analysis of large biological datasets, particularly because of high levels of noise and the lack of a gold-standard for comparisons. We developed several new visualization techniques and demonstrated their effectiveness for evaluating cluster quality and relationships between clusters.

Towards precise classification of cancers based on robust gene functional expression profiles

Zheng Guo — 2006-06-14T14:11:32-00:00

BMC Bioinformatics, Vol. 6, No. 1. (2005)

BACKGROUND:Development of robust and efficient methods for analyzing and interpreting high dimension gene expression profiles continues to be a focus in computational biology. The accumulated experiment evidence supports the assumption that genes express and perform their functions in modular fashions in cells. Therefore, there is an open space for development of the timely and relevant computational algorithms that use robust functional expression profiles towards precise classification of complex human diseases at the modular level.RESULTS:Inspired by the insight that genes act as a module to carry out a highly integrated cellular function, we thus define a low dimension functional expression profile for data reduction. After annotating each individual gene to functional categories defined in a proper gene function classification system such as Gene Ontology applied in this study, we identify those functional categories enriched with differentially expressed genes. For each functional category or functional module, we compute a summary measure (s) for the raw expression values of the annotated genes to capture the overall activity level of the module. In this way, we can treat the gene expressions within a functional module as an integrative data point to replace the multiple values of individual genes. We compare the classification performance of decision trees based on functional expression profiles with the conventional gene expression profiles using four publicly available datasets, which indicates that precise classification of tumour types and improved interpretation can be achieved with the reduced functional expression profiles.CONCLUSION:This modular approach is demonstrated to be a powerful alternative approach to analyzing high dimension microarray data and is robust to high measurement noise and intrinsic biological variance inherent in microarray data. Furthermore, efficient integration with current biological knowledge has facilitated the interpretation of the underlying molecular mechanisms for complex human diseases at the modular level.

Mathematical modeling of the interrelationship of CD4 lymphocyte count and viral load changes induced by the protease inhibitor indinavir.

GL Drusano — 2006-06-13T03:31:58-00:00

Antimicrob Agents Chemother, Vol. 42, No. 2. (February 1998), pp. 358-361.

While CD4 cell counts are widely used to predict disease progression in human immunodeficiency virus (HIV)-infected patients, they are poorly explanatory of the progression to AIDS or death after the introduction of chemotherapy. Changes in HIV load (as measured by RNA PCR) have been shown to be a much better predictor of the risk of disease progression. Since the interrelationship of these markers is of great clinical interest, we modeled the time-averaged return of CD4 cell count and change in viral load subsequent to therapy with the HIV protease inhibitor indinavir. We found that CD4 cell return was significantly related to both the baseline CD4 count (r2 = 0.86, P < 0.001) and the decline in HIV RNA PCR-determined viral load (also referred to in this work as the HIV RNA PCR decline) (r2 = 0.60, P < 0.01). Simultaneously modeling both influences in a linked nonlinear model (r2 = 0.93, P < 0.001) demonstrated that (i) the starting number of CD4 cells accounted for the majority of the change in CD4 cell return and (ii) the return of CD4 cells attributable to viral load decrease was 50% of maximal with only a decrease of approximately 0.2 log of HIV RNA as modeled from the first 12 weeks of therapy. Much greater viral inhibition beyond that necessary for maximal CD4 cell return is possible. Given that HIV RNA PCR decline is more strongly linked to ultimate clinical course in HIV disease, our findings indicate that CD4 return is potentially misleading as an indicator of antiviral effect, since it is determined more by the starting CD4 value than by viral load decline and since near-maximal changes occur with minimal antiviral effect.

Cost-effectiveness of screening for HIV in the era of highly active antiretroviral therapy.

GD Sanders — 2006-06-07T17:08:07-00:00

N Engl J Med, Vol. 352, No. 6. (10 February 2005), pp. 570-585.

BACKGROUND: The costs, benefits, and cost-effectiveness of screening for human immunodeficiency virus (HIV) in health care settings during the era of highly active antiretroviral therapy (HAART) have not been determined. METHODS: We developed a Markov model of costs, quality of life, and survival associated with an HIV-screening program as compared with current practice. In both strategies, symptomatic patients were identified through symptom-based case finding. Identified patients started treatment when their CD4 count dropped to 350 cells per cubic millimeter. Disease progression was defined on the basis of CD4 levels and viral load. The likelihood of sexual transmission was based on viral load, knowledge of HIV status, and efficacy of counseling. RESULTS: Given a 1 percent prevalence of unidentified HIV infection, screening increased life expectancy by 5.48 days, or 4.70 quality-adjusted days, at an estimated cost of 194 dollars per screened patient, for a cost-effectiveness ratio of 15,078 dollars per quality-adjusted life-year. Screening cost less than 50,000 dollars per quality-adjusted life-year if the prevalence of unidentified HIV infection exceeded 0.05 percent. Excluding HIV transmission, the cost-effectiveness of screening was 41,736 dollars per quality-adjusted life-year. Screening every five years, as compared with a one-time screening program, cost 57,138 dollars per quality-adjusted life-year, but was more attractive in settings with a high incidence of infection. Our results were sensitive to the efficacy of behavior modification, the benefit of early identification and therapy, and the prevalence and incidence of HIV infection. CONCLUSIONS: The cost-effectiveness of routine HIV screening in health care settings, even in relatively low-prevalence populations, is similar to that of commonly accepted interventions, and such programs should be expanded.

Cluster analysis and display of genome-wide expression patterns.

MB Eisen — 2005-03-28T00:36:23-00:00

Proc Natl Acad Sci U S A, Vol. 95, No. 25. (8 December 1998), pp. 14863-14868.

A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.

PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes.

VK Mootha — 2006-06-04T00:12:05-00:00

Nat Genet, Vol. 34, No. 3. (July 2003), pp. 267-273.

DNA microarrays can be used to identify gene expression changes characteristic of human disease. This is challenging, however, when relevant differences are subtle at the level of individual genes. We introduce an analytical strategy, Gene Set Enrichment Analysis, designed to detect modest but coordinate changes in the expression of groups of functionally related genes. Using this approach, we identify a set of genes involved in oxidative phosphorylation whose expression is coordinately decreased in human diabetic muscle. Expression of these genes is high at sites of insulin-mediated glucose disposal, activated by PGC-1alpha and correlated with total-body aerobic capacity. Our results associate this gene set with clinically important variation in human metabolism and illustrate the value of pathway relationships in the analysis of genomic profiling experiments.

From the Cover: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

Aravind Subramanian — 2006-03-01T14:37:52-00:00

PNAS, Vol. 102, No. 43. (25 October 2005), pp. 15545-15550.

Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

Using the Gene Ontology for Microarray Data Mining: A Comparison of Methods and Application to Age Effects in Human Prefrontal Cortex

Paul — 2006-06-04T00:07:19-00:00

Neurochemical Research, Vol. 29, No. 6. (June 2004)

A network-based analysis of systemic inflammation in humans

Steve Calvano — 2005-09-01T04:22:44-00:00

Nature (31 August 2005)

An integrative genomics approach to the reconstruction of gene networks in segregating populations.

J Zhu — 2006-06-03T23:57:47-00:00

Cytogenet Genome Res, Vol. 105, No. 2-4. (2004), pp. 363-374.

The reconstruction of genetic networks in mammalian systems is one of the primary goals in biological research, especially as such reconstructions relate to elucidating not only common, polygenic human diseases, but living systems more generally. Here we propose a novel gene network reconstruction algorithm, derived from classic Bayesian network methods, that utilizes naturally occurring genetic variations as a source of perturbations to elucidate the network. This algorithm incorporates relative transcript abundance and genotypic data from segregating populations by employing a generalized scoring function of maximum likelihood commonly used in Bayesian network reconstruction problems. The utility of this novel algorithm is demonstrated via application to liver gene expression data from a segregating mouse population. We demonstrate that the network derived from these data using our novel network reconstruction algorithm is able to capture causal associations between genes that result in increased predictive power, compared to more classically reconstructed networks derived from the same data.

Oncogenic pathway signatures in human cancers as a guide to targeted therapies

Andrea Bild — 2005-11-07T03:42:41-00:00

Nature (06 November 2005)

Quantifying the relationship between co-expression, co-regulation and gene function.

DJ Allocco — 2006-06-03T23:54:40-00:00

BMC Bioinformatics, Vol. 5 (25 February 2004)

BACKGROUND: It is thought that genes with similar patterns of mRNA expression and genes with similar functions are likely to be regulated via the same mechanisms. It has been difficult to quantitatively test these hypotheses on a large scale because there has been no general way of determining whether genes share a common regulatory mechanism. Here we use data from a recent genome wide binding analysis in combination with mRNA expression data and existing functional annotations to quantify the likelihood that genes with varying degrees of similarity in mRNA expression profile or function will be bound by a common transcription factor. RESULTS: Genes with strongly correlated mRNA expression profiles are more likely to have their promoter regions bound by a common transcription factor. This effect is present only at relatively high levels of expression similarity. In order for two genes to have a greater than 50% chance of sharing a common transcription factor binder, the correlation between their expression profiles (across the 611 microarrays used in our study) must be greater than 0.84. Genes with similar functional annotations are also more likely to be bound by a common transcription factor. Combining mRNA expression data with functional annotation results in a better predictive model than using either data source alone. CONCLUSIONS: We demonstrate how mRNA expression data and functional annotations can be used together to estimate the probability that genes share a common regulatory mechanism. Existing microarray data and known functional annotations are sufficient to identify only a relatively small percentage of co-regulated genes.

Systematic survey reveals general applicability of "guilt-by-association" within gene coexpression networks.

CJ Wolfe — 2006-06-03T23:53:59-00:00

BMC Bioinformatics, Vol. 6 (2005)

BACKGROUND: Biological processes are carried out by coordinated modules of interacting molecules. As clustering methods demonstrate that genes with similar expression display increased likelihood of being associated with a common functional module, networks of coexpressed genes provide one framework for assigning gene function. This has informed the guilt-by-association (GBA) heuristic, widely invoked in functional genomics. Yet although the idea of GBA is accepted, the breadth of GBA applicability is uncertain. RESULTS: We developed methods to systematically explore the breadth of GBA across a large and varied corpus of expression data to answer the following question: To what extent is the GBA heuristic broadly applicable to the transcriptome and conversely how broadly is GBA captured by a priori knowledge represented in the Gene Ontology (GO)? Our study provides an investigation of the functional organization of five coexpression networks using data from three mammalian organisms. Our method calculates a probabilistic score between each gene and each Gene Ontology category that reflects coexpression enrichment of a GO module. For each GO category we use Receiver Operating Curves to assess whether these probabilistic scores reflect GBA. This methodology applied to five different coexpression networks demonstrates that the signature of guilt-by-association is ubiquitous and reproducible and that the GBA heuristic is broadly applicable across the population of nine hundred Gene Ontology categories. We also demonstrate the existence of highly reproducible patterns of coexpression between some pairs of GO categories. CONCLUSION: We conclude that GBA has universal value and that transcriptional control may be more modular than previously realized. Our analyses also suggest that methodologies combining coexpression measurements across multiple genes in a biologically-defined module can aid in characterizing gene function or in characterizing whether pairs of functions operate together.

Discovering statistically significant pathways in expression profiling studies.

L Tian — 2006-06-03T23:52:58-00:00

Proc Natl Acad Sci U S A, Vol. 102, No. 38. (20 September 2005), pp. 13544-13549.

Accurate and rapid identification of perturbed pathways through the analysis of genome-wide expression profiles facilitates the generation of biological hypotheses. We propose a statistical framework for determining whether a specified group of genes for a pathway has a coordinated association with a phenotype of interest. Several issues on proper hypothesis-testing procedures are clarified. In particular, it is shown that the differences in the correlation structure of each set of genes can lead to a biased comparison among gene sets unless a normalization procedure is applied. We propose statistical tests for two important but different aspects of association for each group of genes. This approach has more statistical power than currently available methods and can result in the discovery of statistically significant pathways that are not detected by other methods. This method is applied to data sets involving diabetes, inflammatory myopathies, and Alzheimer's disease, using gene sets we compiled from various public databases. In the case of inflammatory myopathies, we have correctly identified the known cytotoxic T lymphocyte-mediated autoimmunity in inclusion body myositis. Furthermore, we predicted the presence of dendritic cells in inclusion body myositis and of an IFN-alpha/beta response in dermatomyositis, neither of which was previously described. These predictions have been subsequently corroborated by immunohistochemistry.

Using functional domain composition and support vector machines for prediction of protein subcellular location.

KC Chou — 2006-05-07T18:00:21-00:00

J Biol Chem, Vol. 277, No. 48. (29 November 2002), pp. 45765-45769.

Proteins are generally classified into the following 12 subcellular locations: 1) chloroplast, 2) cytoplasm, 3) cytoskeleton, 4) endoplasmic reticulum, 5) extracellular, 6) Golgi apparatus, 7) lysosome, 8) mitochondria, 9) nucleus, 10) peroxisome, 11) plasma membrane, and 12) vacuole. Because the function of a protein is closely correlated with its subcellular location, with the rapid increase in new protein sequences entering into databanks, it is vitally important for both basic research and pharmaceutical industry to establish a high throughput tool for predicting protein subcellular location. In this paper, a new concept, the so-called "functional domain composition" is introduced. Based on the novel concept, the representation for a protein can be defined as a vector in a high-dimensional space, where each of the clustered functional domains derived from the protein universe serves as a vector base. With such a novel representation for a protein, the support vector machine (SVM) algorithm is introduced for predicting protein subcellular location. High success rates are obtained by the self-consistency test, jackknife test, and independent dataset test, respectively. The current approach not only can play an important complementary role to the powerful covariant discriminant algorithm based on the pseudo amino acid composition representation (Chou, K. C. (2001) Proteins Struct. Funct. Genet. 43, 246-255; Correction (2001) Proteins Struct. Funct. Genet. 44, 60), but also may greatly stimulate the development of this area.

Predicting protein--protein interactions from primary structure.

JR Bock — 2006-05-07T17:51:12-00:00

Bioinformatics, Vol. 17, No. 5. (May 2001), pp. 455-460.

MOTIVATION: An ambitious goal of proteomics is to elucidate the structure, interactions and functions of all proteins within cells and organisms. The expectation is that this will provide a fuller appreciation of cellular processes and networks at the protein level, ultimately leading to a better understanding of disease mechanisms and suggesting new means for intervention. This paper addresses the question: can protein-protein interactions be predicted directly from primary structure and associated data? Using a diverse database of known protein interactions, a Support Vector Machine (SVM) learning system was trained to recognize and predict interactions based solely on primary structure and associated physicochemical properties. RESULTS: Inductive accuracy of the trained system, defined here as the percentage of correct protein interaction predictions for previously unseen test sets, averaged 80% for the ensemble of statistical experiments. Future proteomics studies may benefit from this research by proceeding directly from the automated identification of a cell's gene products to prediction of protein interaction pairs.

A protein interaction map of Drosophila melanogaster.

L Giot — 2005-06-14T01:08:12-00:00

Science, Vol. 302, No. 5651. (5 December 2003), pp. 1727-1736.

Drosophila melanogaster is a proven model system for many aspects of human biology. Here we present a two-hybrid-based protein-interaction map of the fly proteome. A total of 10,623 predicted transcripts were isolated and screened against standard and normalized complementary DNA libraries to produce a draft map of 7048 proteins and 20,405 interactions. A computational method of rating two-hybrid interaction confidence was developed to refine this draft map to a higher confidence map of 4679 proteins and 4780 interactions. Statistical modeling of the network showed two levels of organization: a short-range organization, presumably corresponding to multiprotein complexes, and a more global organization, presumably corresponding to intercomplex connections. The network recapitulated known pathways, extended pathways, and uncovered previously unknown pathway components. This map serves as a starting point for a systems biology modeling of multicellular organisms, including humans.

A Drosophila protein-interaction map centered on cell-cycle regulators.

CA Stanyon — 2006-03-15T10:31:21-00:00

Genome Biol, Vol. 5, No. 12. (2004)

BACKGROUND: Maps depicting binary interactions between proteins can be powerful starting points for understanding biological systems. A proven technology for generating such maps is high-throughput yeast two-hybrid screening. In the most extensive screen to date, a Gal4-based two-hybrid system was used recently to detect over 20,000 interactions among Drosophila proteins. Although these data are a valuable resource for insights into protein networks, they cover only a fraction of the expected number of interactions. RESULTS: To complement the Gal4-based interaction data, we used the same set of Drosophila open reading frames to construct arrays for a LexA-based two-hybrid system. We screened the arrays using a novel pooled mating approach, initially focusing on proteins related to cell-cycle regulators. We detected 1,814 reproducible interactions among 488 proteins. The map includes a large number of novel interactions with potential biological significance. Informative regions of the map could be highlighted by searching for paralogous interactions and by clustering proteins on the basis of their interaction profiles. Surprisingly, only 28 interactions were found in common between the LexA- and Gal4-based screens, even though they had similar rates of true positives. CONCLUSIONS: The substantial number of new interactions discovered here supports the conclusion that previous interaction mapping studies were far from complete and that many more interactions remain to be found. Our results indicate that different two-hybrid systems and screening approaches applied to the same proteome can generate more comprehensive datasets with more cross-validated interactions. The cell-cycle map provides a guide for further defining important regulatory networks in Drosophila and other organisms.

Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets.

Gandhi — 2006-04-25T22:52:14-00:00

Nature Genetics, Vol. 38, No. 3., pp. 285-293.

We present the first analysis of the human proteome with regard to interactions between proteins. We also compare the human interactome with the available interaction datasets from yeast (Saccharomyces cerevisiae), worm (Caenorhabditis elegans) and fly (Drosophila melanogaster). Of >70,000 binary interactions, only 42 were common to human, worm and fly, and only 16 were common to all four datasets. An additional 36 interactions were common to fly and worm but were not observed in humans, although a coimmunoprecipitation assay showed that 9 of the interactions do occur in humans. A re-examination of the connectivity of essential genes in yeast and humans indicated that the available data do not support the presumption that the number of interaction partners can accurately predict whether a gene is essential. Finally, we found that proteins encoded by genes mutated in inherited genetic disorders are likely to interact with proteins known to cause similar disorders, suggesting the existence of disease subnetworks. The human interaction map constructed from our analysis should facilitate an integrative systems biology approach to elucidating the cellular networks that contribute to health and disease states.

C. elegans ORFeome version 1.1: experimental verification of the genome annotation and resource for proteome-scale protein expression.

J Reboul — 2005-11-18T11:32:26-00:00

Nat Genet, Vol. 34, No. 1. (May 2003), pp. 35-41.

To verify the genome annotation and to create a resource to functionally characterize the proteome, we attempted to Gateway-clone all predicted protein-encoding open reading frames (ORFs), or the 'ORFeome,' of Caenorhabditis elegans. We successfully cloned approximately 12,000 ORFs (ORFeome 1.1), of which roughly 4,000 correspond to genes that are untouched by any cDNA or expressed-sequence tag (EST). More than 50% of predicted genes needed corrections in their intron-exon structures. Notably, approximately 11,000 C. elegans proteins can now be expressed under many conditions and characterized using various high-throughput strategies, including large-scale interactome mapping. We suggest that similar ORFeome projects will be valuable for other organisms, including humans.

WormBase: a comprehensive data resource for Caenorhabditis biology and genomics.

Nansheng Chen — 2004-12-25T04:53:12-00:00

Nucleic Acids Res, Vol. 33, No. Database Issue. (1 January 2005)

WormBase (http://www.wormbase.org), the model organism database for information about Caenorhabditis elegans and related nematodes, continues to expand in breadth and depth. Over the past year, WormBase has added multiple large-scale datasets including SAGE, interactome, 3D protein structure datasets and NCBI KOGs. To accommodate this growth, the International WormBase Consortium has improved the user interface by adding new features to aid in navigation, visualization of large-scale datasets, advanced searching and data mining. Internally, we have restructured the database models to rationalize the representation of genes and to prepare the system to accept the genome sequences of three additional Caenorhabditis species over the coming year.

A Map of the Interactome Network of the Metazoan C. elegans

Siming Li — 2006-04-25T21:16:07-00:00

Science, Vol. 303, No. 5657. (23 January 2004), pp. 540-543.

To initiate studies on how protein-protein interaction (or "interactome") networks relate to multicellular functions, we have mapped a large fraction of the Caenorhabditis elegans interactome network. Starting with a subset of metazoan-specific proteins, more than 4000 interactions were identified from high-throughput, yeast two-hybrid (HT=Y2H) screens. Independent coaffinity purification assays experimentally validated the overall quality of this Y2H data set. Together with already described Y2H interactions and interologs predicted in silico, the current version of the Worm Interactome (WI5) map contains [~]5500 interactions. Topological and biological features of this interactome network, as well as its integration with phenome and transcriptome data sets, lead to numerous biological hypotheses. 10.1126/science.1091403

FDI and the Capital Intensity of Dirty Sectors: A Missing Piece of the Pollution Haven Puzzle

Matthew Cole — 2005-11-10T05:46:58-00:00

Review of Development Economics, Vol. 9, No. 4. (November 2005), pp. 530-548.

In an increasingly integrated world,falling trade barriers mean that the role environmental regulations play in shaping a country’s comparative advantage is greater than ever.This has lead to fears that “dirty”indus- tries will relocate to developing regions where environmental regulations may be less stringent.A number of reasons have been offered to explain why,despite anecdotal evidence and the predictions of theoretical studies, little empirical verification for the existence of pollution havens has been found. Little attention, however,has been paid to the capital intensity of pollution intensive sectors.We investigate the relationship between US outward FDI and factor endowments across sectors to two developing countries.We highlight the role of capital and believe it partially explains why pollution havens are not more widespread. Our approach also highlights those countries that are likeliest to become pollution havens.A multivariate analy- sis reveals some evidence of pollution haven consistent behavior.

Statistical properties of the volatility of price fluctuations

Yanhui — 2006-03-20T20:19:44-00:00

Physical Review Letters, Vol. 60, No. 2.

We study the statistical properties of volatility, measured by locally averaging over a time window T, the absolute value of price changes over a short time interval Δt. We analyze the S&P 500 stock index for the 13-year period Jan. 1984 to Dec. 1996. We find that the cumulative distribution of the volatility is consistent with a power-law asymptotic behavior, characterized by an exponent μ≈3, similar to what is found for the distribution of price changes. The volatility distribution retains the same functional form for a range of values of T. Further, we study the volatility correlations by using the power spectrum analysis. Both methods support a power law decay of the correlation function and give consistent estimates of the relevant scaling exponents. Also, both methods show the presence of a crossover at approximately 1.5 days. In addition, we extend these results to the volatility of individual companies by analyzing a data base comprising all trades for the largest 500 U.S. companies over the two-year period Jan. 1994 to Dec. 1995.

CDD: a curated Entrez database of conserved domain alignments.

A Marchler-Bauer — 2006-03-19T17:04:56-00:00

Nucleic Acids Res, Vol. 31, No. 1. (1 January 2003), pp. 383-387.

The Conserved Domain Database (CDD) is now indexed as a separate database within the Entrez system and linked to other Entrez databases such as MEDLINE(R). This allows users to search for domain types by name, for example, or to view the domain architecture of any protein in Entrez's sequence database. CDD can be accessed on the WorldWideWeb at http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=cdd. Users may also employ the CD-Search service to identify conserved domains in new sequences, at http://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi. CD-Search results, and pre-computed links from Entrez's protein database, are calculated using the RPS-BLAST algorithm and Position Specific Score Matrices (PSSMs) derived from CDD alignments. CD-Searches are also run by default for protein-protein queries submitted to BLAST(R) at http://www.ncbi.nlm.nih.gov/BLAST. CDD mirrors the publicly available domain alignment collections SMART and PFAM, and now also contains alignment models curated at NCBI. Structure information is used to identify the core substructure likely to be present in all family members, and to produce sequence alignments consistent with structure conservation. This alignment model allows NCBI curators to annotate 'columns' corresponding to functional sites conserved among family members.

What do we learn from high-throughput protein interaction data?

B Titz — 2006-03-04T23:55:05-00:00

Expert Rev Proteomics, Vol. 1, No. 1. (June 2004), pp. 111-121.

The biological significance of protein interactions, their method of generation and reliability is briefly reviewed. Protein interaction networks adopt a scale-free topology that explains their error tolerance or vulnerability, depending on whether hubs or peripheral proteins are attacked. Networks also allow the prediction of protein function from their interaction partners and therefore, the formulation of analytical hypotheses. Comparative network analysis predicts interactions for distantly related species based on conserved interactions, even if sequences are only weakly conserved. Finally, the medical relevance of protein interaction analysis is discussed and the necessity for data integration is emphasized.

Interactome: gateway into systems biology.

ME Cusick — 2006-01-21T21:47:31-00:00

Hum Mol Genet, Vol. 14 Spec No. 2 (15 October 2005)

Protein-protein interactions are fundamental to all biological processes, and a comprehensive determination of all protein-protein interactions that can take place in an organism provides a framework for understanding biology as an integrated system. The availability of genome-scale sets of cloned open reading frames has facilitated systematic efforts at creating proteome-scale data sets of protein-protein interactions, which are represented as complex networks or 'interactome' maps. Protein-protein interaction mapping projects that follow stringent criteria, coupled with experimental validation in orthogonal systems, provide high-confidence data sets immanently useful for interrogating developmental and disease mechanisms at a system level as well as elucidating individual protein function and interactome network topology. Although far from complete, currently available maps provide insight into how biochemical properties of proteins and protein complexes are integrated into biological systems. Such maps are also a useful resource to predict the function(s) of thousands of genes.

Intronic sequences flanking alternatively spliced exons are conserved between human and mouse.

R Sorek — 2005-09-12T15:19:44-00:00

Genome Res, Vol. 13, No. 7. (July 2003), pp. 1631-1637.

Comparison of the sequences of mouse and human genomes revealed a surprising number of nonexonic, nonexpressed conserved sequences, for which no function could be assigned. To study the possible correlation between these conserved intronic sequences and alternative splicing regulation, we developed a method to identify exons that are alternatively spliced in both human and mouse. We compiled two exon sets: one of alternatively spliced conserved exons and another of constitutively spliced conserved exons. We found that 77% of the conserved alternatively spliced exons were flanked on both sides by long conserved intronic sequences. In comparison, only 17% of the conserved constitutively spliced exons were flanked by such conserved intronic sequences. The average length of the conserved intronic sequences was 103 bases in the upstream intron and 94 bases in the downstream intron. The average identity levels in the immediately flanking intronic sequences were 88% and 80% for the upstream and downstream introns, respectively, higher than the conservation levels of 77% that were measured in promoter regions. Our results suggest that the function of many of the intronic sequence blocks that are conserved between human and mouse is the regulation of alternative splicing.

Conserved sequence elements associated with exon skipping.

E Miriami — 2006-02-13T22:37:19-00:00

Nucleic Acids Res, Vol. 31, No. 7. (1 April 2003), pp. 1974-1983.

One of the major forms of alternative splicing, which generates multiple mRNA isoforms differing in the precise combinations of their exon sequences, is exon skipping. While in constitutive splicing all exons are included, in the skipped pattern(s) one or more exons are skipped. The regulation of this process is still not well understood; so far, cis- regulatory elements (such as exonic splicing enhancers) were identified in individual cases. We therefore set to investigate the possibility that exon skipping is controlled by sequences in the adjacent introns. We employed a computer analysis on 54 sequences documented as undergoing exon skipping, and identified two motifs both in the upstream and downstream introns of the skipped exons. One motif is highly enriched in pyrimidines (mostly C residues), and the other motif is highly enriched in purines (mostly G residues). The two motifs differ from the known cis-elements present at the 5' and 3' splice site. Interestingly, the two motifs are complementary, and their relative positional order is conserved in the flanking introns. These suggest that base pairing interactions can underlie a mechanism that involves secondary structure to regulate exon skipping. Remarkably, the two motifs are conserved in mouse orthologous genes that undergo exon skipping.