Another strategy for understanding the functions of 1000s of new lncRNA genes is to study their properties as a group. We use bioinformatic analysis of large genomic datasets to try to characterise lncRNAs, divide them into classes, and predict their functions. Right now, we are pursuing several main approaches.
First, we are attempting to predict lncRNAs that are involved in the progression of cancer. This work is carried out within the PanCancer Analysis of Whole Genomes (PCAWG) effort by the International Cancer Genome Consortium. The software tool we developed, ExInAtor, uses maps of mutations from thousands of tumours to identify cancer driver lncRNAs. The candidates predicted in this way can be tested experimentally.
Another approach we have is driven by the idea that an lncRNA’s function is strongly reflected in its localisation within the cell. For example, chromatin regulatory lncRNAs must presumably be located in the nucleus,while those regulating mRNA translation should be located in the cytosol. We are creating genome-wide maps of lncRNA sub-cellular localisation to predict their functions and attempt to identify the factors that control this process.