Created on 7th June 2017
Genome sequences for hundreds of mammalian species are available, but an understanding of genomic regulatory regions for non-model species is only beginning. A comprehensive prediction of potential active regulatory regions is necessary to functionally study the roles of the majority of genomic variants in evolution, domestication, and animal production. We developed a computational method to predict regulatory DNA sequences (promoters, enhancers and transcription factor binding sites) in production animals (cows and pigs) and extended its broad applicability to other mammals. The pipeline utilizes human regulatory features identified from thousands of tissues, cell lines, and experimental assays to predict homologous regions in another mammalian species. Importantly, we developed a filtering strategy, including a machine learning classification method, to utilize a very small number of species-specific experimental datasets available to select for the likely active regulatory regions. The method finds the optimal combination of sensitivity and accuracy to unbiasedly predict regulatory regions in non-model species. Importantly, we demonstrated the utility of the predicted regulatory datasets in cattle for prioritizing variants associated with multiple production and climate change adaptation traits, and identifying potential genome editing targets.Show more
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|Kyle Schachtschnei...||Completed||11 Jul 2017||View review|
|Diego Villar||Completed||21 Jul 2017||View review|