Prediction of lncRNA’s Function Based on Gene Ontology

Long non-coding RNAs (lncRNAs), defined as non-protein-coding transcripts longer than 200 nucleotides, are one of the most common RNA species, but they are in most cases poorly understood with respect to function. Almost 16,000 human long non-coding RNA genes have been identified in the GENCODE project. The function of lncRNAs and other novel genes can be predicted by identifying significantly enriched annotation terms in already annotated genes that are co-expressed with the lncRNAs. However, such approaches are sensitive to the methods that are used to estimate the level of co-expression. We have tested and compared two well-known statistical metrics (Pearson and Spearman) and two geometrical metrics (Sobolev and Fisher) for identification of the co-expressed genes, using experimental expression data across 19 normal human tissues. We have also used a benchmarking approach based on semantic similarity to evaluate how well these methods are able to predict annotation terms, using a well-annotated set of protein-coding genes.This work shows that geometrical metrics, in particular in combination with the statistical metrics, will predict annotation terms more efficiently than traditional approaches. Tests on selected lncRNAs confirm that it is possible to predict the function of these genes given a reliable set of expression data.

Keywords:

Function prediction , Gene annotation , Co , expression , Fisher information metric , Sobolev metric.