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Full-Text Articles in Engineering
A Latent Variable Model For Chemogenomic Profiling, Patrick Flaherty
A Latent Variable Model For Chemogenomic Profiling, Patrick Flaherty
Mathematics and Statistics Department Faculty Publication Series
Motivation: In haploinsufficiency profiling data, pleiotropic genes are often misclassified by clustering algorithms that impose the constraint that a gene or experiment belong to only one cluster. We have developed a general probabilistic model that clusters genes and experiments without requiring that a given gene or drug only appear in one cluster. The model also incorporates the functional annotation of known genes to guide the clustering procedure. Results: We applied our model to the clustering of 79 chemogenomic experiments in yeast. Known pleiotropic genes PDR5 and MAL11 are more accurately represented by the model than by a clustering procedure that …
Chemogenomic Profiling: Identifying The Functional Interactions Of Small Molecules In Yeast, Patrick Flaherty
Chemogenomic Profiling: Identifying The Functional Interactions Of Small Molecules In Yeast, Patrick Flaherty
Mathematics and Statistics Department Faculty Publication Series
We demonstrate the efficacy of a genome-wide protocol in yeast that allows the identification of those gene products that functionally interact with small molecules and result in the inhibition of cellular proliferation. Here we present results from screening 10 diverse compounds in 80 genome-wide experiments against the complete collection of heterozygous yeast deletion strains. These compounds include anticancer and antifungal agents, statins, alverine citrate, and dyclonine. In several cases, we identified previously known interactions; furthermore, in each case, our analysis revealed novel cellular interactions, even when the relationship between a compound and its cellular target had been well established. In …
Functional Profiling Of The Saccharomyces Cerevisiae Genome, Patrick Flaherty
Functional Profiling Of The Saccharomyces Cerevisiae Genome, Patrick Flaherty
Mathematics and Statistics Department Faculty Publication Series
Determining the effect of gene deletion is a fundamental approach to understanding gene function. Conventional genetic screens exhibit biases, and genes contributing to a phenotype are often missed. We systematically constructed a nearly complete collection of gene-deletion mutants (96% of annotated open reading frames, or ORFs) of the yeast Saccharomyces cerevisiae. DNA sequences dubbed 'molecular bar codes' uniquely identify each strain, enabling their growth to be analysed in parallel and the fitness contribution of each gene to be quantitatively assessed by hybridization to high-density oligonucleotide arrays. We show that previously known and new genes are necessary for optimal growth …