Dataset Information

AGEM: an integrative system for analyzing spatial-temporal gene-expression information.

ABSTRACT:

Motivation

The work presented here describes the 'anatomical Gene-Expression Mapping (aGEM)' Platform, a development conceived to integrate phenotypic information with the spatial and temporal distributions of genes expressed in the mouse. The aGEM Platform has been built by extending the Distributed Annotation System (DAS) protocol, which was originally designed to share genome annotations over the WWW. DAS is a client-server system in which a single client integrates information from multiple distributed servers.

Results

The aGEM Platform provides information to answer three main questions. (i) Which genes are expressed in a given mouse anatomical component? (ii) In which mouse anatomical structures are a given gene or set of genes expressed? And (iii) is there any correlation among these findings? Currently, this Platform includes several well-known mouse resources (EMAGE, GXD and GENSAT), hosting gene-expression data mostly obtained from in situ techniques together with a broad set of image-derived annotations.

Availability

The Platform is optimized for Firefox 3.0 and it is accessed through a friendly and intuitive display: http://agem.cnb.csic.es

SUBMITTER: Jimenez-Lozano N

PROVIDER: S-EPMC2752607 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

aGEM: an integrative system for analyzing spatial-temporal gene-expression information.

Jiménez-Lozano Natalia N Segura Joan J Macías José Ramón JR Vega Juanjo J Carazo José María JM

Bioinformatics (Oxford, England) 20090709 19

<h4>Motivation</h4>The work presented here describes the 'anatomical Gene-Expression Mapping (aGEM)' Platform, a development conceived to integrate phenotypic information with the spatial and temporal distributions of genes expressed in the mouse. The aGEM Platform has been built by extending the Distributed Annotation System (DAS) protocol, which was originally designed to share genome annotations over the WWW. DAS is a client-server system in which a single client integrates information from m ...[more]

PMID: 19592395

Similar Datasets

Project description:Precise patterns of spatial and temporal gene expression are central to metazoan complexity and act as a driving force for embryonic development. While there has been substantial progress in dissecting and predicting cis-regulatory activity, our understanding of how information from multiple enhancer elements converge to regulate a gene's expression remains elusive. This is in large part due to the number of different biological processes involved in mediating regulation as well as limited availability of experimental measurements for many of them. Here, we used a Bayesian approach to model diverse experimental regulatory data, leading to accurate predictions of both spatial and temporal aspects of gene expression. We integrated whole-embryo information on transcription factor recruitment to multiple cis-regulatory modules, insulator binding and histone modification status in the vicinity of individual gene loci, at a genome-wide scale during Drosophila development. The model uses Bayesian networks to represent the relation between transcription factor occupancy and enhancer activity in specific tissues and stages. All parameters are optimized in an Expectation Maximization procedure providing a model capable of predicting tissue- and stage-specific activity of new, previously unassayed genes. Performing the optimization with subsets of input data demonstrated that neither enhancer occupancy nor chromatin state alone can explain all gene expression patterns, but taken together allow for accurate predictions of spatio-temporal activity. Model predictions were validated using the expression patterns of more than 600 genes recently made available by the BDGP consortium, demonstrating an average 15-fold enrichment of genes expressed in the predicted tissue over a naïve model. We further validated the model by experimentally testing the expression of 20 predicted target genes of unknown expression, resulting in an accuracy of 95% for temporal predictions and 50% for spatial. While this is, to our knowledge, the first genome-wide approach to predict tissue-specific gene expression in metazoan development, our results suggest that integrative models of this type will become more prevalent in the future.

Project description:BackgroundTo determine which changes in the host cell genome are crucial for cervical carcinogenesis, a longitudinal in vitro model system of HPV-transformed keratinocytes was profiled in a genome-wide manner. Four cell lines affected with either HPV16 or HPV18 were assayed at 8 sequential time points for gene expression (mRNA) and gene copy number (DNA) using high-resolution microarrays. Available methods for temporal differential expression analysis are not designed for integrative genomic studies.ResultsHere, we present a method that allows for the identification of differential gene expression associated with DNA copy number changes over time. The temporal variation in gene expression is described by a generalized linear mixed model employing low-rank thin-plate splines. Model parameters are estimated with an empirical Bayes procedure, which exploits integrated nested Laplace approximation for fast computation. Iteratively, posteriors of hyperparameters and model parameters are estimated. The empirical Bayes procedure shrinks multiple dispersion-related parameters. Shrinkage leads to more stable estimates of the model parameters, better control of false positives and improvement of reproducibility. In addition, to make estimates of the DNA copy number more stable, model parameters are also estimated in a multivariate way using triplets of features, imposing a spatial prior for the copy number effect.ConclusionWith the proposed method for analysis of time-course multilevel molecular data, more profound insight may be gained through the identification of temporal differential expression induced by DNA copy number abnormalities. In particular, in the analysis of an integrative oncogenomics study with a time-course set-up our method finds genes previously reported to be involved in cervical carcinogenesis. Furthermore, the proposed method yields improvements in sensitivity, specificity and reproducibility compared to existing methods. Finally, the proposed method is able to handle count (RNAseq) data from time course experiments as is shown on a real data set.

Project description:BACKGROUND: Rice is highly sensitive to drought, and the effect of drought may vary with the different genotypes and development stages. Genome-wide gene expression profiling was used as the initial point to dissect molecular genetic mechanism of this complex trait and provide valuable information for the improvement of drought tolerance in rice. Affymetrix rice genome array containing 48,564 japonica and 1,260 indica sequences was used to analyze the gene expression pattern of rice exposed to drought stress. The transcriptome from leaf, root, and young panicle at three developmental stages was comparatively analyzed combined with bioinformatics exploring drought stress related cis-elements. RESULTS: There were 5,284 genes detected to be differentially expressed under drought stress. Most of these genes were tissue- or stage-specific regulated by drought. The tissue-specific down-regulated genes showed distinct function categories as photosynthesis-related genes prevalent in leaf, and the genes involved in cell membrane biogenesis and cell wall modification over-presented in root and young panicle. In a drought environment, several genes, such as GA2ox, SAP15, and Chitinase III, were regulated in a reciprocal way in two tissues at the same development stage. A total of 261 transcription factor genes were detected to be differentially regulated by drought stress. Most of them were also regulated in a tissue- or stage-specific manner. A cis-element containing special CGCG box was identified to over-present in the upstream of 55 common induced genes, and it may be very important for rice plants responding to drought environment. CONCLUSIONS: Genome-wide gene expression profiling revealed that most of the drought differentially expressed genes (DEGs) were under temporal and spatial regulation, suggesting a crosstalk between various development cues and environmental stimuli. The identification of the differentially regulated DEGs, including TF genes and unique candidate cis-element for drought responsiveness, is a very useful resource for the functional dissection of the molecular mechanism in rice responding to environment stress.