FlyDIVaS: A Comparative Genomics Resource for Drosophila Divergence and Selection. Issue 8 (1st August 2016) - Stanley, Craig E; Kulathinal, Rob J
Abstract: With arguably the best finished and expertly annotated genome assembly, Drosophila melanogaster is a formidable genetics model to study all aspects of biology. Nearly a decade ago, the 12 Drosophila genomes project expanded D. melanogaster 's breadth as a comparative model through the community-development of an unprecedented genus- and genome-wide comparative resource. However, since its inception, these datasets for evolutionary inference and biological discovery have become increasingly outdated, outmoded, and inaccessible. Here, we provide an updated and upgradable comparative genomics resource of Drosophila divergence and selection, flyDIVaS, based on the latest genomic assemblies, curated FlyBase annotations, and recent OrthoDB orthology calls. flyDIVaS is an online database containing D. melanogaster -centric orthologous gene sets, CDS and protein alignments, divergence statistics (% gaps, dN, dS, dN / dS ), and codon-based tests of positive Darwinian selection. Out of 13, 920 protein-coding D. melanogaster genes, ∼80% have one aligned ortholog in the closely related species, D. simulans, and ∼50% have 1–1 12-way alignments in the original 12 sequenced species that span over 80 million yr of divergence. Genes and their orthologs can be chosen from four different taxonomic datasets differing in phylogenetic depth and coverage density, and visualized via interactive alignments and phylogenetic trees. Users can also batch download entire comparative datasets. A functional survey finds conserved mitotic and neural genes, highly diverged immune and reproduction-related genes, more conspicuous signals of divergence across tissue-specific genes, and an enrichment of positive selection among highly diverged genes. flyDIVaS will be regularly updated and can be freely accessed at www.flydivas.info . We encourage researchers to regularly use this resource as a tool for biological inference and discovery, and in their classrooms to help train the next generation of biologists to creatively use such genomic big data resources in an integrative manner.