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

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.     

Epigenetic chromatin modifiers in barley: IV. The study of barley Polycomb group (PcG) genes during seed development and in response to external ABA

Background  

Epigenetic phenomena have been associated with the regulation of active and silent chromatin states achieved by modifications of chromatin structure through DNA methylation, and histone post-translational modifications. The latter is accomplished, in part, through the action of PcG (Polycomb group) protein complexes which methylate nucleosomal histone tails at specific sites, ultimately leading to chromatin compaction and gene silencing. Different PcG complex variants operating during different developmental stages have been described in plants. In particular, the so-called FIE/MEA/FIS2 complex governs the expression of genes important in embryo and endosperm development in Arabidopsis. In our effort to understand the epigenetic mechanisms regulating seed development in barley (Hordeum vulgare), an agronomically important monocot plant cultivated for its endosperm, we set out to characterize the genes encoding barley PcG proteins.     

Identifying set-wise differential co-expression in gene expression microarray data

Background  

Previous differential coexpression analyses focused on identification of differentially coexpressed gene pairs, revealing many insightful biological hypotheses. However, this method could not detect coexpression relationships between pairs of gene sets. Considering the success of many set-wise analysis methods for microarray data, a coexpression analysis based on gene sets may elucidate underlying biological processes provoked by the conditional changes. Here, we propose a differentially coexpressed gene sets (dCoxS) algorithm that identifies the differentially coexpressed gene set pairs between conditions.     

Development-dependent changes in the tight DNA-protein complexes of barley on chromosome and gene level

Background  

The tightly bound to DNA proteins (TBPs) is a protein group that remains attached to DNA with covalent or non-covalent bonds after its deproteinisation. The functional role of this group is as yet not completely understood. The main goal of this study was to evaluate tissue specific changes in the TBP distribution in barley genes and chromosomes in different phases of shoot and seed development. We have: 1. investigated the TBP distribution along Amy32b and Bmy1 genes encoding low pI α-amylase A and endosperm specific β-amylase correspondingly using oligonucleotide DNA arrays; 2. characterized the polypeptide spectrum of TBP and proteins with affinity to TBP-associated DNA; 3. localized the distribution of DNA complexes with TBP (TBP-DNA) on barley 1H and 7H chromosomes using mapped markers; 4. compared the chromosomal distribution of TBP-DNA complexes to the distribution of the nuclear matrix attachment sites.     

Differential gene expression during seed germination in barley (Hordeum vulgare L.)

A barley cDNA macroarray comprising 1,440 unique genes was used to analyze the spatial and temporal patterns of gene expression in embryo, scutellum and endosperm tissue during different stages of germination. Among the set of expressed genes, 69 displayed the highest mRNA level in endosperm tissue, 58 were up-regulated in both embryo and scutellum, 11 were specifically expressed in the embryo and 16 in scutellum tissue. Based on Blast X analyses, 70% of the differentially expressed genes could be assigned a putative function. One set of genes, expressed in both embryo and scutellum tissue, included functions in cell division, protein translation, nucleotide metabolism, carbohydrate metabolism and some transporters. The other set of genes expressed in endosperm encodes several metabolic pathways including carbohydrate and amino acid metabolism as well as protease inhibitors and storage proteins. As shown for a storage protein and a trypsin inhibitor, the endosperm of the germinating barley grain contains a considerable amount of residual mRNA which was produced during seed development and which is degraded during early stages of germination. Based on similar expression patterns in the endosperm tissue, we identified 29 genes which may undergo the same degradation process. Electronic Publication     

Finding biological process modifications in cancer tissues by mining gene expression correlations

Background  

Through the use of DNA microarrays it is now possible to obtain quantitative measurements of the expression of thousands of genes from a biological sample. This technology yields a global view of gene expression that can be used in several ways. Functional insight into expression profiles is routinely obtained by using Gene Ontology terms associated to the cellular genes. In this paper, we deal with functional data mining from expression profiles, proposing a novel approach that studies the correlations between genes and their relations to Gene Ontology (GO). By using this "functional correlations comparison" we explore all possible pairs of genes identifying the affected biological processes by analyzing in a pair-wise manner gene expression patterns and linking correlated pairs with Gene Ontology terms.     

Extracting gene expression patterns and identifying co-expressed genes from microarray data reveals biologically responsive processes

Background  

A common observation in the analysis of gene expression data is that many genes display similarity in their expression patterns and therefore appear to be co-regulated. However, the variation associated with microarray data and the complexity of the experimental designs make the acquisition of co-expressed genes a challenge. We developed a novel method for Extracting microarray gene expression Patterns and Identifying co-expressed Genes, designated as EPIG. The approach utilizes the underlying structure of gene expression data to extract patterns and identify co-expressed genes that are responsive to experimental conditions.     

DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules

Background  

Large microarray datasets have enabled gene regulation to be studied through coexpression analysis. While numerous methods have been developed for identifying differentially expressed genes between two conditions, the field of differential coexpression analysis is still relatively new. More specifically, there is so far no sensitive and untargeted method to identify gene modules (also known as gene sets or clusters) that are differentially coexpressed between two conditions. Here, sensitive and untargeted means that the method should be able to construct de novo modules by grouping genes based on shared, but subtle, differential correlation patterns.     

Motif-guided sparse decomposition of gene expression data for regulatory module identification

Background  

Genes work coordinately as gene modules or gene networks. Various computational approaches have been proposed to find gene modules based on gene expression data; for example, gene clustering is a popular method for grouping genes with similar gene expression patterns. However, traditional gene clustering often yields unsatisfactory results for regulatory module identification because the resulting gene clusters are co-expressed but not necessarily co-regulated.     

Large scale comparison of global gene expression patterns in human and mouse

Background  

It is widely accepted that orthologous genes between species are conserved at the sequence level and perform similar functions in different organisms. However, the level of conservation of gene expression patterns of the orthologous genes in different species has been unclear. To address the issue, we compared gene expression of orthologous genes based on 2,557 human and 1,267 mouse samples with high quality gene expression data, selected from experiments stored in the public microarray repository ArrayExpress.     

Comparison of scores for bimodality of gene expression distributions and genome-wide evaluation of the prognostic relevance of high-scoring genes

Background  

A major goal of the analysis of high-dimensional RNA expression data from tumor tissue is to identify prognostic signatures for discriminating patient subgroups. For this purpose genome-wide identification of bimodally expressed genes from gene array data is relevant because distinguishability of high and low expression groups is easier compared to genes with unimodal expression distributions.     

Validation and functional annotation of expression-based clusters based on gene ontology

Background  

The biological interpretation of large-scale gene expression data is one of the paramount challenges in current bioinformatics. In particular, placing the results in the context of other available functional genomics data, such as existing bio-ontologies, has already provided substantial improvement for detecting and categorizing genes of interest. One common approach is to look for functional annotations that are significantly enriched within a group or cluster of genes, as compared to a reference group.     

Term-tissue specific models for prediction of gene ontology biological processes using transcriptional profiles of aging in drosophila melanogaster

Background  

Predictive classification on the base of gene expression profiles appeared recently as an attractive strategy for identifying the biological functions of genes. Gene Ontology (GO) provides a valuable source of knowledge for model training and validation. The increasing collection of microarray data represents a valuable source for generating functional hypotheses of uncharacterized genes.