Project description:Time course analysis series in Development of the transcriptome from Drosophila melanogaster using the Heidelberg FlyArray. All stages were hybridized against embryonic stage 0-4 h as reference control. Keywords: time-course
Project description:RNA-seq on Drosophila melanogaster ovary (stages 1-7) in: diapause at 12ºC (after 28 days), reproductive at 12ºC (after 28 days), and age-matched control at 25ºC in two inbred genotypes. Each biological replicate is a pool of 25 ovary pairs, and each treatment has three biological replicates.
Project description:Time course analysis series in Development of the transcriptome from Drosophila melanogaster using the Heidelberg FlyArray. All stages were hybridized against embryonic stage 0-4 h as reference control.
Project description:Understanding the genotype-phenotype map and how variation at different levels of biological organization is associated are central topics in modern biology. Fast developments in sequencing technologies and other molecular omic tools enable researchers to obtain detailed information on variation at DNA level and on intermediate endophenotypes, such as RNA, proteins and metabolites. This can facilitate our understanding of the link between genotypes and molecular and functional organismal phenotypes. Here, we use the Drosophila melanogaster Genetic Reference Panel and nuclear magnetic resonance (NMR) metabolomics to investigate the ability of the metabolome to predict organismal phenotypes. We performed NMR metabolomics on four replicate pools of male flies from each of 170 different isogenic lines. Our results show that metabolite profiles are variable among the investigated lines and that this variation is highly heritable. Second, we identify genes associated with metabolome variation. Third, using the metabolome gave better prediction accuracies than genomic information for four of five quantitative traits analyzed. Our comprehensive characterization of population-scale diversity of metabolomes and its genetic basis illustrates that metabolites have large potential as predictors of organismal phenotypes. This finding is of great importance, e.g., in human medicine, evolutionary biology and animal and plant breeding.
