Use of Bayesian networks to probabilistically model and improve the likelihood of validation of microarray findings by RT-PCR.

Though genome-wide technologies, such as microarrays, are widely used, data from these methods are considered noisy; there is still varied success in downstream biological validation. We report a method that increases the likelihood of successfully validating microarray findings using real time RT-PCR, including genes at low expression levels and with small differences. We use a Bayesian network to identify the most relevant sources of noise based on the successes and failures in validation for an initial set of selected genes, and then improve our subsequent selection of genes for validation based on eliminating these sources of noise.

Evaluation and integration of 49 genome-wide experiments and the prediction of previously unknown obesity-related genes.

Motivation: Genome-wide experiments only rarely show resounding success in yielding genes associated with complex polygenic disorders. We evaluate 49 obesity-related genome-wide experiments with publicly available findings including microarray, genetics, proteomics and gene knock-down from human, mouse, rat and worm, in terms of their ability to rediscover a comprehensive set of genes previously found to be causally associated or having variants associated with obesity.

Results: Individual experiments show poor predictive ability for rediscovering known obesity-associated genes.

Genome-wide analysis of host responses to the Pseudomonas aeruginosa type III secretion system yields synergistic effects.

The type III secretion system (TTSS) is a dedicated bacterial pathogen protein targeting system that directly affects host cell signalling and response pathways. Our goal was to identify host responses to the Pseudomonas aeruginosa effectors, introduced into target cells utilizing the TTSS. We carried out expression profiling of a human lung pneumocyte cell line A549 exposed to isogenic mutants of P.

Genome-scale expression profiling of Hutchinson-Gilford progeria syndrome reveals widespread transcriptional misregulation leading to mesodermal/mesenchymal defects and accelerated atherosclerosis.

Hutchinson-Gilford progeria syndrome (HGPS) is a rare genetic disease with widespread phenotypic features resembling premature aging. HGPS was recently shown to be caused by dominant mutations in the LMNA gene, resulting in the in-frame deletion of 50 amino acids near the carboxyl terminus of the encoded lamin A protein. Children with this disease typically succumb to myocardial infarction or stroke caused by severe atherosclerosis at an average age of 13 years.