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Harnessing naturally randomized transcription to infer regulatory relationships among genes

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Phil Charles will be presenting a paper, which presents a theoretical result defining three testable conditions which together imply the existence of a directed causal relationship among a pair of genes. This can be used to infer a gene ‘wiring diagram’ at a given probability threshold from an expression dataset. While the model currently only incorporates transcript expression data, there is scope for extension to include other quantitative sources of relationship data including proteomic and metabolomic.

Paper details: Chen LS, Emmert-Streib F, Storey JD (2007) Harnessing naturally randomized transcription to infer regulatory relationships among genes. Genome Biology 8:R219

Abstract: We develop an approach using randomized genotypes to rigorously infer causal regulatory relationships among genes at the transcriptional level, based on experiments in which genotyping and expression profiling are performed. This approach can be used to build transcriptional regulatory networks and to identify putative regulators of genes. We apply the method to an experiment in yeast, in which genes known to be in the same process and functions are recovered in the resulting transcriptional network.

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This talk is part of the Bioinformatics jounal club for the -omics series.

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