Beyond Annotation: Assigning Function to Chicken ncRNAs

New genomic technologies are accumulating information about functional elements within genomic sequence at a faster rate than ever before, and the FAANG Project is expected to increase this rate of acquisition. Understanding how these elements contribute to traits and phenotypes requires a concomitant effort to understand the function of these genes and regulatory regions. However, functional annotation relies on curation of published data and computational prediction pipelines; genomic technologies routinely identify novel functional elements (with no existing body of literature) and there are no existing computational pipelines for functional annotation of ncRNAs. We describe here our initial efforts to develop functional annotation workflows for miRNAs and lncRNAs, two classes of ncRNAs that are being annotated in species that are studied as part of the FAANG Project. We pair miRNA target prediction with proteomics data and GO enrichment analysis to (1) better identify true miRNA targets and (2) predict the function of novel miRNAs. For predicting function of novel lncRNAs we use a similar approach, identifying lncRNA-mRNA pairs expressed in the same tissues, identifying interaction networks for these pairs and investigating GO term enrichment to predict function. By linking experimental expression data with computational approach we expect to generate preliminary, high-throughput functional information about commonly identified classes on ncRNAs. This in turn will allow us to provide information that can then be incorporated into functional analyses of typical gene expression data sets (e.g., transcriptome data).