Recent advances in RNA deep sequencing have revealed numbers of noncoding RNAs (ncRNAs). These ncRNAs are usually classified into microRNAs and long noncoding RNAs (lncRNAs). Both expression and regulation perturbations of lncRNAs have been frequently found across various cancer types. However, functional characterization of lncRNAs in human complex diseases is still a challenging task. Taking advantage of the omics datasets, we have developed a number of computational methods to systematically predict the function of lncRNAs.
First, we propose a resource LncSpA to explore tissue-elevated (TE) lncRNA across human normal and adult and pediatric cancer tissues. Notably, TE lncRNAs were found to be regulated by m6A modification across tissues, particular brain tissues. At regulatory level, we revealed that lncRNAs play critical roles in cancer by perturbing the transcription regulatory network. Recently, we systematically identified xperimentally supported and predicted lncRNA peptides, and predicted tumour neoantigens from peptides encoded by lncRNAs, which would provide novel insights into cancer immunotherapy. Recent studies also highlighted the function of ncRNAs in immune cell differentiation and immune system function in cancer. Thus, we proposed ImmLnc to systematically identify the immune-related lncRNAs. We found that ImmLnc helps prioritize cancer-related lncRNAs and identifies cancer subtypes with different immunotype. 
Taken together, integrating the multi-omic data of expression and regulation, we generated biologically meaningful functional annotations for lncRNAs genome-wide. Our proposed computational models illustrate the power in functional prediction of lncRNAs, and opens up new avenues to study and functionally characterize lncRNAs. We anticipate that in the future, the integration of computational function prediction and more knockout or over-expression experiments will offer even deeper insight into the lncRNA functions.