Chemical discovery and global gene expression analysis in zebrafish

The zebrafish (Danio rerio) provides an excellent model for studying vertebrate development and human disease because of its ex utero, optically transparent embryogenesis and amenability to in vivo manipulation. The rapid embryonic developmental cycle, large clutch sizes and ease of maintenance at large numbers also add to the appeal of this species. Considerable genomic data has recently become publicly available that is aiding the construction of zebrafish microarrays, thus permitting global gene expression analysis. The zebrafish is also suitable for chemical genomics, in part as a result of the permeability of its embryos to small molecules and consequent avoidance of external confounding maternal effects. Finally, there is increasing characterization and analysis of zebrafish models of human disease. Thus, the zebrafish offers a high-quality, high-throughput bioassay tool for determining the biological effect of small molecules as well as for dissecting biological pathways.     

Class discovery analysis of the lung cancer gene expression data

Traditional histological classification of lung cancer subtypes is informative, but incomplete. Recent studies of gene expression suggest that molecular classification can be used for effective diagnostic and prediction of the treatment outcome. We attempt to build a molecular classification based on the public data available from a few independent sources. The data is reanalyzed with a new cluster analysis algorithm. This algorithm allows us to preserve the high dimensionality of data and produce the cluster structure without preliminary selection of significant genes or any other presumption about the relation between different cancer and normal tissue samples. The resulting clusters are generally consistent with the histological classification. However, our analysis reveals many additional details and subtypes of previously defined types of lung cancer. Large histological cancer types can be further divided into subclasses with different patterns of gene expression. These subtypes should be taken into account in diagnostics, drug testing, and treatment development for lung cancer patients.     

Issues in association analysis: error control in case-control association studies for disease gene discovery

Several sources of errors are discussed. While genotyping errors have little effect on power in case-control association studies, they tend to strongly increase false positive results in TDT type tests unless occurrence of errors is allowed for in the analysis (e.g., TDTae test). Disregarding non-genetic risk factors is shown to lead to a form of hidden heterogeneity, which can strongly reduce power. Stratification of data into more homogeneous subgroups is advocated as a simple solution to allowing for non-genetic risk factors such as socio-economic status and food preferences.     

GenMiner: mining non-redundant association rules from integrated gene expression data and annotations

GenMiner is an implementation of association rule discovery dedicated to the analysis of genomic data. It allows the analysis of datasets integrating multiple sources of biological data represented as both discrete values, such as gene annotations, and continuous values, such as gene expression measures. GenMiner implements the new NorDi (normal discretization) algorithm for normalizing and discretizing continuous values and takes advantage of the Close algorithm to efficiently generate minimal non-redundant association rules. Experiments show that execution time and memory usage of GenMiner are significantly smaller than those of the standard Apriori-based approach, as well as the number of extracted association rules. AVAILABILITY: The GenMiner software and supplementary materials are available at http://bioinfo.unice.fr/publications/genminer_article/ and http://keia.i3s.unice.fr/?Implementations:GenMiner SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Inferring adaptive regulation thresholds and association rules from gene expression data through combinatorial optimization learning

There is a need to design computational methods to support the prediction of gene regulatory networks. Such models should offer both biologically-meaningful and computationally-accurate predictions, which in combination with other techniques may improve large-scale, integrative studies. This paper presents a new machine learning method for the prediction of putative regulatory associations from expression data, which exhibit properties never or only partially addressed by other techniques recently published. The method was tested on a Saccharomyces cerevisiae gene expression dataset. The results were statistically validated and compared with the relationships inferred by two machine learning approaches to gene regulatory network prediction. Furthermore, the resulting predictions were assessed using domain knowledge. The proposed algorithm may be able to accurately predict relevant biological associations between genes. One of the most relevant features of this new method is the prediction of adaptive regulation thresholds for the discretization of gene expression values, which is required prior to the rule association learning process. Moreover, an important advantage consists of its low computational cost to infer association rules. The proposed system may significantly support exploratory, large-scale studies of automated identification of potentially-relevant gene expression associations.     

Subtraction-coupled custom microarray analysis for gene discovery and gene expression studies in the CNS

The revolution in our knowledge about the genomes of organisms gives rise to the question, what do we do with this information? The development of techniques allowing high throughput analysis of RNA and protein expression, such as cDNA microarrays, provide for genome-wide analysis of gene expression. These analyses will help bridge the gap between systems and molecular neuroscience. This review discusses the advantages of using a subtractive hybridization technique, such as a representational difference analysis, to generate a custom cDNA microarray enriched for genes relevant to investigating complex, heterogeneous tissues such as those involved in the chemical senses. Real and hypothetical examples of these experiments are discussed. Benefits of this approach over traditional microarray techniques include having a more relevant clone set, the potential for gene discovery and the creation of a new tool to investigate similar systems. Potential pitfalls may include PCR artifacts and the need for sequencing. However, these disadvantages can be overcome so that the coupling of subtraction techniques to microarray screening can be a fruitful approach to a variety of experimental systems.     

Integrated gene expression profiling and linkage analysis in the rat

The combined application of genome-wide expression profiling from microarray experiments with genetic linkage analysis enables the mapping of expression quantitative trait loci (eQTLs) which are primary control points for gene expression across the genome. This approach allows for the dissection of primary and secondary genetic determinants of gene expression. The cis-acting eQTLs in practice are easier to investigate than the trans-regulated eQTLs because they are under simpler genetic control and are likely to be due to sequence variants within the gene itself or its neighboring regulatory elements. These genes are therefore candidates both for variation in gene expression and for contributions to whole-body phenotypes, particularly when these are located within known and relevant physiologic QTLs. Multiple trans-acting eQTLs tend to cluster to the same genetic location, implying shared regulatory control mechanisms that may be amenable to network analysis to identify gene clusters within the same metabolic pathway. Such clusters may ultimately underlie development of individual complex, whole-body phenotypes. The combined expression and linkage approach has been applied successfully in several mammalian species, including the rat which has specific features that demonstrate its value as a model for studying complex traits.     

Integrated analysis of gene expression and copy number data on gene shaving using independent component analysis

DNA microarray gene expression and microarray-based comparative genomic hybridization (aCGH) have been widely used for biomedical discovery. Because of the large number of genes and the complex nature of biological networks, various analysis methods have been proposed. One such method is "gene shaving," a procedure which identifies subsets of the genes with coherent expression patterns and large variation across samples. Since combining genomic information from multiple sources can improve classification and prediction of diseases, in this paper we proposed a new method, "ICA gene shaving" (ICA, independent component analysis), for jointly analyzing gene expression and copy number data. First we used ICA to analyze joint measurements, gene expression and copy number, of a biological system and project the data onto statistically independent biological processes. Next, we used these results to identify patterns of variation in the data and then applied an iterative shaving method. We investigated the properties of our proposed method by analyzing both simulated and real data. We demonstrated that the robustness of our method to noise using simulated data. Using breast cancer data, we showed that our method is superior to the Generalized Singular Value Decomposition (GSVD) gene shaving method for identifying genes associated with breast cancer.     

Evolving fuzzy rules to model gene expression

This paper develops an algorithm that extracts explanatory rules from microarray data, which we treat as time series, using genetic programming (GP) and fuzzy logic. Reverse polish notation is used (RPN) to describe the rules and to facilitate the GP approach. The algorithm also allows for the insertion of prior knowledge, making it possible to find sets of rules that include the relationships between genes already known. The algorithm proposed is applied to problems arising in the construction of gene regulatory networks, using two different sets of real data from biological experiments on the Arabidopsis thaliana cold response and the rat central nervous system, respectively. The results show that the proposed technique can fit data to a pre-defined precision even in situations where the data set has thousands of features but only a limited number of points in time are available, a situation in which traditional statistical alternatives encounter difficulties, due to the scarcity of time points.     

Integrated analysis of DNA copy number and gene expression microarray data using gene sets

Background  

Genes that play an important role in tumorigenesis are expected to show association between DNA copy number and RNA expression. Optimal power to find such associations can only be achieved if analysing copy number and gene expression jointly. Furthermore, some copy number changes extend over larger chromosomal regions affecting the expression levels of multiple resident genes.     

Integrated expressional analysis: application to the drug discovery process

Microarray technology enables high-throughput testing of gene expression to investigate various neuroscience related questions. This in turn creates a demand for scalable methods to confirm microarray results and the opportunity to use this information to discover and test novel pathways and therapeutic applications. Discovery of new central nervous system (CNS) treatments requires a comprehensive understanding of multiple aspects including the biology of a target, the pathophysiology of a disease/disorder, and the selection of successful lead compounds as well as efficient biomarker and drug disposition strategies such as absorption (how a drug is absorbed), distribution (how a drug spreads through an organism), metabolism (chemical conversion of a drug, if any, and into which substances), and elimination (how is a drug eliminated) (ADME). Understanding of the toxicity is also of paramount importance. These approaches, in turn, require novel high-content integrative assay technologies that provide thorough information about changes in cell biology. To increase efficiency of profiling, characterization, and validation, we established a new screening strategy that combines high-content image-based testing on Array Scan (Cellomics) with a confocal system and the multiplexed TaqMan RT-PCR method for quantitative mRNA expression analysis. This approach could serve as an interface between high-throughput microarray testing and specific application of markers discovered in the course of a microarray experiment. Markers could pinpoint activation or inhibition of a molecular pathway related, for instance, to neuronal viability. We demonstrate the successful testing of the same cell population in an image-based translocational assay followed by poly(A) mRNA capture and multiplexed single tube RT-PCR. In addition, Ciphergen ProteinChip analysis can be performed on the supernatant, thus allowing significant complementarity in the data output and interpretation by also including the capture and initial analysis of proteins in the integrative approach presented. We have determined various conditions including the number of cells, RT and PCR optimization, which are necessary for successful detection and consequent assay integration. We also show the successful convergence of various different approaches and multiplexing of different targets within a single real-time PCR tube. This novel integrative technological approach has utility for CNS drug discovery, target and biomarker identification, selection and characterization as well as for the study of toxicity- and adverse event-associated molecular mechanisms.     

Global gene expression analysis by combinatorial optimization

Generally, there is a trade-off between methods of gene expression analysis that are precise but labor-intensive, e.g. RT-PCR, and methods that scale up to global coverage but are not quite as quantitative, e.g. microarrays. In the present paper, we show how how a known method of gene expression profiling (K. Kato, Nucleic Acids Res. 23, 3685-3690 (1995)), which relies on a fairly small number of steps, can be turned into a global gene expression measurement by advanced data post-processing, with potentially little loss of accuracy. Post-processing here entails solving an ancillary combinatorial optimization problem. Validation is performed on in silico experiments generated from the FANTOM data base of full-length mouse cDNA. We present two variants of the method. One uses state-of-the-art commercial software for solving problems of this kind, the other a code developed by us specifically for this purpose, released in the public domain under GPL license.