GAGE: generally applicable gene set enrichment for pathway analysis

Background  

Gene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge. GSA focuses on sets of related genes and has established major advantages over individual gene analyses, including greater robustness, sensitivity and biological relevance. However, previous GSA methods have limited usage as they cannot handle datasets of different sample sizes or experimental designs.     

Prediction of breast cancer prognosis using gene set statistics provides signature stability and biological context

Background  

Different microarray studies have compiled gene lists for predicting outcomes of a range of treatments and diseases. These have produced gene lists that have little overlap, indicating that the results from any one study are unstable. It has been suggested that the underlying pathways are essentially identical, and that the expression of gene sets, rather than that of individual genes, may be more informative with respect to prognosis and understanding of the underlying biological process.     

Identification of responsive gene modules by network-based gene clustering and extending: application to inflammation and angiogenesis

Background  

Cell responses to environmental stimuli are usually organized as relatively separate responsive gene modules at the molecular level. Identification of responsive gene modules rather than individual differentially expressed (DE) genes will provide important information about the underlying molecular mechanisms. Most of current methods formulate module identification as an optimization problem: find the active sub-networks in the genome-wide gene network by maximizing the objective function considering the gene differential expression and/or the gene-gene co-expression information. Here we presented a new formulation of this task: a group of closely-connected and co-expressed DE genes in the gene network are regarded as the signatures of the underlying responsive gene modules; the modules can be identified by finding the signatures and then recovering the "missing parts" by adding the intermediate genes that connect the DE genes in the gene network.     

Laser capture microdissection of gonads from juvenile zebrafish

Background  

Investigating gonadal gene expression is important in attempting to elucidate the molecular mechanism of sex determination and differentiation in the model species zebrafish. However, the small size of juvenile zebrafish and correspondingly their gonads complicates this type of investigation. Furthermore, the lack of a genetic sex marker in juvenile zebrafish prevents pooling gonads from several individuals. The aim of this study was to establish a method to isolate the gonads from individual juvenile zebrafish allowing future investigations of gonadal gene expression during sex determination and differentiation.     

Network analysis of skin tumor progression identifies a rewired genetic architecture affecting inflammation and tumor susceptibility

Background  

Germline polymorphisms can influence gene expression networks in normal mammalian tissues and can affect disease susceptibility. We and others have shown that analysis of this genetic architecture can identify single genes and whole pathways that influence complex traits, including inflammation and cancer susceptibility. Whether germline variants affect gene expression in tumors that have undergone somatic alterations, and the extent to which these variants influence tumor progression, is unknown.     

Extensive molecular differences between anterior- and posterior-half-sclerotomes underlie somite polarity and spinal nerve segmentation

Background  

The polarization of somite-derived sclerotomes into anterior and posterior halves underlies vertebral morphogenesis and spinal nerve segmentation. To characterize the full extent of molecular differences that underlie this polarity, we have undertaken a systematic comparison of gene expression between the two sclerotome halves in the mouse embryo.     

Co-expression module analysis reveals biological processes,genomic gain,and regulatory mechanisms associated with breast cancer progression

Background  

Gene expression signatures are typically identified by correlating gene expression patterns to a disease phenotype of interest. However, individual gene-based signatures usually suffer from low reproducibility and interpretability.     

Study of gene function based on spatial co-expression in a high-resolution mouse brain atlas

Background  

The Allen Brain Atlas (ABA) project systematically profiles three-dimensional high-resolution gene expression in postnatal mouse brains for thousands of genes. By unveiling gene behaviors at both the cellular and molecular levels, ABA is becoming a unique and comprehensive neuroscience data source for decoding enigmatic biological processes in the brain. Given the unprecedented volume and complexity of the in situ hybridization image data, data mining in this area is extremely challenging. Currently, the ABA database mainly serves as an online reference for visual inspection of individual genes; the underlying rich information of this large data set is yet to be explored by novel computational tools. In this proof-of-concept study, we studied the hypothesis that genes sharing similar three-dimensional expression profiles in the mouse brain are likely to share similar biological functions.     

PathMAPA: a tool for displaying gene expression and performing statistical tests on metabolic pathways at multiple levels for <Emphasis Type="Italic">Arabidopsis</Emphasis>

Background  

To date, many genomic and pathway-related tools and databases have been developed to analyze microarray data. In published web-based applications to date, however, complex pathways have been displayed with static image files that may not be up-to-date or are time-consuming to rebuild. In addition, gene expression analyses focus on individual probes and genes with little or no consideration of pathways. These approaches reveal little information about pathways that are key to a full understanding of the building blocks of biological systems. Therefore, there is a need to provide useful tools that can generate pathways without manually building images and allow gene expression data to be integrated and analyzed at pathway levels for such experimental organisms as Arabidopsis.     

Testing the additional predictive value of high-dimensional molecular data

Background  

While high-dimensional molecular data such as microarray gene expression data have been used for disease outcome prediction or diagnosis purposes for about ten years in biomedical research, the question of the additional predictive value of such data given that classical predictors are already available has long been under-considered in the bioinformatics literature.     

SynBlast: Assisting the analysis of conserved synteny information

Motivation  

In the last years more than 20 vertebrate genomes have been sequenced, and the rate at which genomic DNA information becomes available is rapidly accelerating. Gene duplication and gene loss events inherently limit the accuracy of orthology detection based on sequence similarity alone. Fully automated methods for orthology annotation do exist but often fail to identify individual members in cases of large gene families, or to distinguish missing data from traceable gene losses. This situation can be improved in many cases by including conserved synteny information.     

Inferring biological functions and associated transcriptional regulators using gene set expression coherence analysis

Background  

Gene clustering has been widely used to group genes with similar expression pattern in microarray data analysis. Subsequent enrichment analysis using predefined gene sets can provide clues on which functional themes or regulatory sequence motifs are associated with individual gene clusters. In spite of the potential utility, gene clustering and enrichment analysis have been used in separate platforms, thus, the development of integrative algorithm linking both methods is highly challenging.     

mRNA detection of individual cells with the single cell nanoprobe method compared with in situ hybridization

Background  

The localization of specific mRNA generates cell polarity by controlling the translation sites of specific proteins. Although most of these events depend on differences in gene expression, no method is available to examine time dependent gene expression of individual living cells. In situ hybridization (ISH) is a powerful and useful method for detecting the localization of mRNAs, but it does not allow a time dependent analysis of mRNA expression in single living cells because the cells have to be fixed for mRNA detection. To overcome these issues, the extraction of biomolecules such as mRNAs, proteins, and lipids from living cells should be performed without severe damage to the cells. In previous studies, we have reported a single cell nanoprobe (SCN) method to examine gene expression of individual living cells using atomic force microscopy (AFM) without killing the cells.     

Alternative promoter usage of the membrane glycoprotein CD36

Background  

CD36 is a membrane glycoprotein involved in a variety of cellular processes such as lipid transport, immune regulation, hemostasis, adhesion, angiogenesis and atherosclerosis. It is expressed in many tissues and cell types, with a tissue specific expression pattern that is a result of a complex regulation for which the molecular mechanisms are not yet fully understood. There are several alternative mRNA isoforms described for the gene. We have investigated the expression patterns of five alternative first exons of the CD36 gene in several human tissues and cell types, to better understand the molecular details behind its regulation.     

Socio-environmental and endocrine influences on developmental and caste-regulatory gene expression in the eusocial termite <Emphasis Type="Italic">Reticulitermes flavipes</Emphasis>

Background  

Strict regulation of caste differentiation, at the molecular level, is thought to be important to maintain social structure in insect societies. Previously, a number of extrinsic and intrinsic factors have been shown to influence caste composition in termite colonies. One important factor is the influence of nestmates; in particular, soldier termites are known to inhibit hormone-dependent worker-to-soldier differentiation. However, soldier influences on nestmates at the molecular level are virtually unknown. Here, to test the hypothesis that soldiers can influence nestmate gene expression, we investigated the impact of four treatments on whole-body gene expression in totipotent Reticulitermes flavipes workers: (i) juvenile hormone III (JHIII; a morphogenetic hormone), (ii) soldier head extracts (SHE), (iii) JHIII+SHE, and (iv) live soldiers.     

Duplicate gene evolution and expression in the wake of vertebrate allopolyploidization

Background  

The mechanism by which duplicate genes originate – whether by duplication of a whole genome or of a genomic segment – influences their genetic fates. To study events that trigger duplicate gene persistence after whole genome duplication in vertebrates, we have analyzed molecular evolution and expression of hundreds of persistent duplicate gene pairs in allopolyploid clawed frogs (Xenopus and Silurana). We collected comparative data that allowed us to tease apart the molecular events that occurred soon after duplication from those that occurred later on. We also quantified expression profile divergence of hundreds of paralogs during development and in different tissues.     

A Δ11 desaturase gene genealogy reveals two divergent allelic classes within the European corn borer (<Emphasis Type="Italic">Ostrinia nubilalis</Emphasis>)

Background  

Moth pheromone mating systems have been characterized at the molecular level, allowing evolutionary biologists to study how changes in protein sequence or gene expression affect pheromone phenotype, patterns of mating, and ultimately, the formation of barriers to gene exchange. Recent studies of Ostrinia pheromones have focused on the diversity of sex pheromone desaturases and their role in the specificity of pheromone production. Here we produce a Δ11 desaturase genealogy within Ostrinia nubilalis. We ask what has been the history of this gene, and whether this history suggests that changes in Δ11 desaturase have been involved in the divergence of the E and Z O. nubilalis pheromone strains.