Estimating networks from RNA interference and microarray data
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Fellmann Mark
PROVIDER: S-ECPF-GEOD-7033 | biostudies-other |
REPOSITORIES: biostudies-other
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Project description:We here used the technology of RNA interference and cDNA microarrays to reconstruct a network between genes known to be disregulated in ER-positive breast cancer diseases. Therefor single genes of high importance were silenced and the effect of this pertubation was measured with cDNA microarrays Keywords: network_reconstruction_RNAi human ER-positive breast cancer cell line was cultivated as described and transfection with chemically synthesized siRNAs was performed according to standard HiPerFect transfection protocol by Qiagen (Hilden, Germany) At least 4 cDNA-microarrays were hybridized in dye-swap method for the silencing of every gene of interest. To minimize off-target effects we used at least two different siRNAs per gene.
2009-01-28 | E-GEOD-7033 | biostudies-arrayexpress
Project description:We here used the technology of RNA interference and cDNA microarrays to reconstruct a network between genes known to be disregulated in ER-positive breast cancer diseases. Therefor single genes of high importance were silenced and the effect of this pertubation was measured with cDNA microarrays Keywords: network_reconstruction_RNAi
2009-01-29 | GSE7033 | GEO
Project description:BACKGROUND: DNA microarrays and other genomics-inspired technologies provide large datasets that often include hidden patterns of correlation between genes reflecting the complex processes that underlie cellular metabolism and physiology. The challenge in analyzing large-scale expression data has been to extract biologically meaningful inferences regarding these processes - often represented as networks - in an environment where the datasets are often imperfect and biological noise can obscure the actual signal. Although many techniques have been developed in an attempt to address these issues, to date their ability to extract meaningful and predictive network relationships has been limited. Here we describe a method that draws on prior information about gene-gene interactions to infer biologically relevant pathways from microarray data. Our approach consists of using preliminary networks derived from the literature and/or protein-protein interaction data as seeds for a Bayesian network analysis of microarray results. RESULTS: Through a bootstrap analysis of gene expression data derived from a number of leukemia studies, we demonstrate that seeded Bayesian Networks have the ability to identify high-confidence gene-gene interactions which can then be validated by comparison to other sources of pathway data. CONCLUSION: The use of network seeds greatly improves the ability of Bayesian Network analysis to learn gene interaction networks from gene expression data. We demonstrate that the use of seeds derived from the biomedical literature or high-throughput protein-protein interaction data, or the combination, provides improvement over a standard Bayesian Network analysis, allowing networks involving dynamic processes to be deduced from the static snapshots of biological systems that represent the most common source of microarray data. Software implementing these methods has been included in the widely used TM4 microarray analysis package.
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Project description:BackgroundThe accurate detection of differentially expressed (DE) genes has become a central task in microarray analysis. Unfortunately, the noise level and experimental variability of microarrays can be limiting. While a number of existing methods partially overcome these limitations by incorporating biological knowledge in the form of gene groups, these methods sacrifice gene-level resolution. This loss of precision can be inappropriate, especially if the desired output is a ranked list of individual genes. To address this shortcoming, we developed M-BISON (Microarray-Based Integration of data SOurces using Networks), a formal probabilistic model that integrates background biological knowledge with microarray data to predict individual DE genes.ResultsM-BISON improves signal detection on a range of simulated data, particularly when using very noisy microarray data. We also applied the method to the task of predicting heat shock-related differentially expressed genes in S. cerevisiae, using an hsf1 mutant microarray dataset and conserved yeast DNA sequence motifs. Our results demonstrate that M-BISON improves the analysis quality and makes predictions that are easy to interpret in concert with incorporated knowledge. Specifically, M-BISON increases the AUC of DE gene prediction from .541 to .623 when compared to a method using only microarray data, and M-BISON outperforms a related method, GeneRank. Furthermore, by analyzing M-BISON predictions in the context of the background knowledge, we identified YHR124W as a potentially novel player in the yeast heat shock response.ConclusionThis work provides a solid foundation for the principled integration of imperfect biological knowledge with gene expression data and other high-throughput data sources.
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Project description:Network analysis transcends conventional pairwise approaches to data analysis as the context of components in a network graph can be taken into account. Such approaches are increasingly being applied to genomics data, where functional linkages are used to connect genes or proteins. However, while microarray gene expression datasets are now abundant and of high quality, few approaches have been developed for analysis of such data in a network context. We present a novel approach for 3-D visualisation and analysis of transcriptional networks generated from microarray data. These networks consist of nodes representing transcripts connected by virtue of their expression profile similarity across multiple conditions. Analysing genome-wide gene transcription across 61 mouse tissues, we describe the unusual topography of the large and highly structured networks produced, and demonstrate how they can be used to visualise, cluster, and mine large datasets. This approach is fast, intuitive, and versatile, and allows the identification of biological relationships that may be missed by conventional analysis techniques. This work has been implemented in a freely available open-source application named BioLayout Express(3D).
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