Project description:Drosophila melanogaster RNA sequencing with Illumina Genome Analyzer. High-throughput sequencing of Drosophila melanogaster RNAs. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf
2008-11-04 | GSE13441 | GEO
Project description:Shotgun RNA metagenomic sequencing of colonized Drosophila melanogaster and Aedes aegypti
Project description:Dnmt2 and NSun2 are (cytosine-5) RNA methyltransferases. Using CRISPR/Cas9 we created null mutations in Dnmt2 and NSun2 genes in Drosophila melanogaster. We also ectopically expressed a wild type and catalytically inactive Dnmt2 form in the Dnmt2 mutant background. RNA bisulfite sequencing was used to follow RNA methylation at partical tRNA substrates.
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.
