From microarrays to mechanisms of brain development and function

Microarray technology provides a powerful approach to understand complex biological systems. The most common application of microarray technology is to document gene expression profiles of all genes within a genome in response to specific conditions such as disease, drug application, or genotype. One result of this technology is the ability to ascribe activities to genes with unknown functions - such rationale is the basis behind ‘functional genomics’. This approach is particularly well-suited to studies of the brain because roughly one third to one half of all genes in vertebrate genomes are expressed in the brain. However, less than half of such genes have any defined function. While a large number of studies have applied microarray technology to the brain, few studies have followed up the expression profiling approach with functional characterization of the genes identified. In this review, I highlight recent research that reflects the initial promise of functional genomics in the brain. I focus on neural differentiation with particular emphasis on synapse development.     

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     

Integrated analysis of gene expression by association rules discovery

Background  

Microarray technology is generating huge amounts of data about the expression level of thousands of genes, or even whole genomes, across different experimental conditions. To extract biological knowledge, and to fully understand such datasets, it is essential to include external biological information about genes and gene products to the analysis of expression data. However, most of the current approaches to analyze microarray datasets are mainly focused on the analysis of experimental data, and external biological information is incorporated as a posterior process.     

Computational strategies for analyzing data in gene expression microarray experiments

Microarray analysis has become a widely used method for generating gene expression data on a genomic scale. Microarrays have been enthusiastically applied in many fields of biological research, even though several open questions remain about the analysis of such data. A wide range of approaches are available for computational analysis, but no general consensus exists as to standard for microarray data analysis protocol. Consequently, the choice of data analysis technique is a crucial element depending both on the data and on the goals of the experiment. Therefore, basic understanding of bioinformatics is required for optimal experimental design and meaningful interpretation of the results. This review summarizes some of the common themes in DNA microarray data analysis, including data normalization and detection of differential expression. Algorithms are demonstrated by analyzing cDNA microarray data from an experiment monitoring gene expression in T helper cells. Several computational biology strategies, along with their relative merits, are overviewed and potential areas for additional research discussed. The goal of the review is to provide a computational framework for applying and evaluating such bioinformatics strategies. Solid knowledge of microarray informatics contributes to the implementation of more efficient computational protocols for the given data obtained through microarray experiments.     

Bioinformatics databases and tools in virology research: an overview

Viruses are major factors of human infectious diseases. Understanding of the structure-function correlation in viruses is important for the identification of potential anti-viral inhibitors and vaccine targets. In virology research, virus-related databases and bioinformatic analysis tools are essential for discerning relationships within complex datasets about viruses and host-virus interactions. Bioinformatic analyses on viruses include the identification of open reading frames, gene prediction, homology searching, sequence alignment, and motif and epitope recognition. The predictions of features such as transmembrane domains, glycosylation sites, and protein secondary and tertiary structure are important for analyzing the structure-function relationship of proteins encoded in viral genomes. Biochemical pathway analysis can help elucidate information at the biological systems level. Microarray analysis provides methods for high throughput screening and gene expression profiling. Virus-related bioinformatics databases include those concerned with viral sequences, taxonomy, homologous protein families, structures, or dedicated to specific viruses such as influenza and herpes simplex virus (HSV). This review provides a guide and overview of computational programs for these analyses as a resource for genomics and proteomics studies in virology research. These resources are useful for understanding viral diseases, as well as for the design and development of anti-viral agents.     

An application of gene comparative image for predicting the effect on replication ratio by HBV virus gene missense mutation

Hepatitis B viruses (HBVs) show instantaneous and high-ratio mutations when they are replicated, some sorts of which significantly affect the efficiency of virus replication through enhancing or depressing the viral replication, while others have no influence at all. The mechanism of gene expression is closely correlated with its gene sequence. With the rapid increase in the number of newly found sequences entering into data banks, it is highly desirable to develop an automated method for simulating the gene regulating function. The establishment of such a predictor will no doubt expedite the process of prioritizing genes and proteins identified by genomics efforts as potential molecular targets for drug design. Based on the power of cellular automata (CA) in treating complex systems with simple rules, a novel method to present HBV gene image has been introduced. The results show that the images thus obtained can very efficiently simulate the effects of the gene missense mutation on the virus replication. It is anticipated that CA may also serve as a useful vehicle for many other studies on complicated biological systems.     

CLUM: A cluster program for analyzing microarray data

Microarray technology is increasingly being applied in biological and medical research to address a wide range of problems. Cluster analysis has proven to be a very useful tool for investigating the structure of microarray data. This paper presents a program for clustering microarray data, which is based on the so-called path-distance. The algorithm gives in each step a partition in two clusters and no prior assumptions on the structure of clusters are required. It assigns each object (gene or sample) to only one cluster and gives the global optimum for the function that quantifies the adequacy of a given partition of the sample into k clusters. The program was tested on experimental data sets, showing the robustness of the algorithm.     

CLUM: a cluster program for analyzing microarray data

Microarray technology is increasingly being applied in biological and medical research to address a wide range of problems. Cluster analysis has proven to be a very useful tool for investigating the structure of microarray data. This paper presents a program for clustering microarray data, which is based on the so call path-distance. The algorithm gives in each step a partition in two clusters and no prior assumptions on the structure of clusters are required. It assigns each object (gene or sample) to only one cluster and gives the global optimum for the function that quantifies the adequacy of a given partition of the sample into k clusters. The program was tested on experimental data sets, showing the robustness of the algorithm.     

植物功能基因组学研究进展

植物功能基因组学是从整体水平研究基因的功能及表达规律的科学。对植物功能基因组学的研究将助于我们对基因功能的理解和对植物性状的定性改造和利用。本文简要介绍了植物功能基因组学的概念、研究方法和最新研究进展。     

Using formal concept analysis for microarray data comparison

Microarray technologies, which can measure tens of thousands of gene expression values simultaneously in a single experiment, have become a common research method for biomedical researchers. Computational tools to analyze microarray data for biological discovery are needed. In this paper, we investigate the feasibility of using formal concept analysis (FCA) as a tool for microarray data analysis. The method of FCA builds a (concept) lattice from the experimental data together with additional biological information. For microarray data, each vertex of the lattice corresponds to a subset of genes that are grouped together according to their expression values and some biological information related to gene function. The lattice structure of these gene sets might reflect biological relationships in the dataset. Similarities and differences between experiments can then be investigated by comparing their corresponding lattices according to various graph measures. We apply our method to microarray data derived from influenza-infected mouse lung tissue and healthy controls. Our preliminary results show the promise of our method as a tool for microarray data analysis.     

植物功能基因组学研究进展

植物基因组研究已经由以全基因组测序为目标的结构基因组学转向以基因功能鉴定为目标的功能基因组学研究.本简要介绍了植物功能基因组的主要研究方法,如基因表达系列分析法、表达序列标签法、差异表达谱基因芯片法、蛋白质组学分析法以及生物信息学等及其研究现状,并展望了植物功能基因组学的应用前景.     

Coupled analysis of gene expression and chromosomal location

Microarray technology can be used to assess simultaneously global changes in expression of mRNA or genomic DNA copy number among thousands of genes in different biological states. In many cases, it is desirable to determine if altered patterns of gene expression correlate with chromosomal abnormalities or assess expression of genes that are contiguous in the genome. We describe a method, differential gene locus mapping (DIGMAP), which aligns the known chromosomal location of a gene to its expression value deduced by microarray analysis. The method partitions microarray data into subsets by chromosomal location for each gene interrogated by an array. Microarray data in an individual subset can then be clustered by physical location of genes at a subchromosomal level based upon ordered alignment in genome sequence. A graphical display is generated by representing each genomic locus with a colored cell that quantitatively reflects its differential expression value. The clustered patterns can be viewed and compared based on their expression signatures as defined by differential values between control and experimental samples. In this study, DIGMAP was tested using previously published studies of breast cancer analyzed by comparative genomic hybridization (CGH) and prostate cancer gene expression profiles assessed by cDNA microarray experiments. Analysis of the breast cancer CGH data demonstrated the ability of DIGMAP to deduce gene amplifications and deletions. Application of the DIGMAP method to the prostate data revealed several carcinoma-related loci, including one at 16q13 with marked differential expression encompassing 19 known genes including 9 encoding metallothionein proteins. We conclude that DIGMAP is a powerful computational tool enabling the coupled analysis of microarray data with genome location.     

Integrating external biological knowledge in the construction of regulatory networks from time-series expression data

ABSTRACT: BACKGROUND: Inference about regulatory networks from high-throughput genomics data is of great interest in systems biology. We present a Bayesian approach to infer gene regulatory networks from time series expression data by integrating various types of biological knowledge. RESULTS: We formulate network construction as a series of variable selection problems and use linear regression to model the data. Our method summarizes additional data sources with an informative prior probability distribution over candidate regression models. We extend the Bayesian model averaging (BMA) variable selection method to select regulators in the regression framework. We summarize the external biological knowledge by an informative prior probability distribution over the candidate regression models. CONCLUSIONS: We demonstrate our method on simulated data and a set of time-series microarray experiments measuring the effect of a drug perturbation on gene expression levels, and show that it outperforms leading regression-based methods in the literature.     

Bayesian mapping of genotype x expression interactions in quantitative and qualitative traits

A novel Bayesian gene mapping method, which can simultaneously utilize both molecular marker and gene expression data, is introduced. The approach enables a quantitative or qualitative phenotype to be expressed as a linear combination of the marker genotypes, gene expression levels, and possible genotype x gene expression interactions. The interaction data, given as marker-gene pairs, contains possible in cis and in trans effects obtained from earlier allelic expression studies, genetical genomics studies, biological hypotheses, or known pathways. The method is presented for an inbred line cross design and can be easily generalized to handle other types of populations and designs. The model selection is based on the use of effect-specific variance components combined with Jeffreys' non-informative prior--the method operates by adaptively shrinking marker, expression, and interaction effects toward zero so that non-negligible effects are expected to occur only at very few positions. The estimation of the model parameters and the handling of missing genotype or expression data is performed via Markov chain Monte Carlo sampling. The potential of the method including heritability estimation is presented using simulated examples and novel summary statistics. The method is also applied to a real yeast data set with known pathways.