Annotating the regeneration transcriptome of Cloeon dipterum

Abstract

To recover function, damaged or amputated tissues/organs can regenerate in certain animals. Understanding this process requires investigating the mechanisms controlling regeneration in the widest range of organisms. The mayfly Cloeon dipterum is an emerging model insect to study this phenomenon. Using this model, we analysed the transcriptional response of Cleoon¿s gills during their regeneration, which is completed in just a few days, by focusing on the annotation of differentially expressed genes at different points of the process. A substantial part of these genes showed no apparent homology after running standard comparative genomics approaches. Here we present a computational pipeline devised to annotate genes which cannot be annotated due to lack of homologues elsewhere. With extensive use of technologies based on Deep Learning, we will describe a novel and reliable procedure to improve the functional annotation of regeneration- related genes.