Functional characterization of disease/comorbidity-associated IncRNA

Background:Functional characterization of the long noncoding RNAs(IncRNAs)in disease attracts great attention,which results in a limited number of experimentally characterized IncRNAs.The major problems underlying the lack of experimental verifications are considered to come from the significant false-positive assignments and extensive genetic-heterogeneity of disease.These problems are even worse when it comes to the functional characterization in comorbidity(simultaneous/sequential presence of multiple diseases in a patient,and showing much wider prevalence,poorer treatment-response and longer illness-course than a single disease).Methods:Herein,FCCLnc was developed to characterize IncRNA function by(1)integrating diverse SNPs that were associated with 193 diseases standardized by International Classification of Diseases(ICD-11),(2)condition-specific expression of IncRNAs,(3)weighted correlation network of IncRNAs and protein-coding neighboring genes.Results:FCCLnc can characterize IncRNA function in both disease and comorbidity by not only controlling false discovery but also tolerating their disease heterogeneity.Moreover,FCCLnc can provide interactive visualization and full download of IncRNA-centered co-expression network.Conclusion:In summary,FCCLnc is unique in characterizing IncRNA function in diverse diseases and comorbidities and is highly expected to emerge to be an indispensable complement to other available tools.FCCLnc is accessible at https://idrblab.org/fcclnc/.

Comparative study of network-based prioritization of protein domains associated with human complex diseases

Domains are basic structural and functional unit of proteins,and,thus,exploring associations between protein domains and human inherited diseases will greatly improve our understanding of the pathogenesis of human complex diseases and further benefit the medical prevention,diagnosis and treatment of these diseases.Based on the assumption that deleterious nonsynonymous single nucleotide polymorphisms(nsSNPs)underlying human complex diseases may actually change structures of protein domains,affect functions of corresponding proteins,and finally result in these diseases,we compile a dataset that contains 1174 associations between 433 protein domains and 848 human disease phenotypes.With this dataset,we compare two approaches(guilt-by-association and correlation coefficient)that use a domain-domain interaction network and a phenotype similarity network to prioritize associations between candidate domains and human disease phenotypes.We implement these methods with three distance measures(direct neighbor,shortest path with Gaussian kernel,and diffusion kernel),demonstrate the effectiveness of these methods using three large-scale leave-one-out cross-validation experiments(random control,simulated linkage interval,and whole-genome scan),and evaluate the performance of these methods in terms of three criteria(mean rank ratio,precision,and AUC score).Results show that both methods can effectively prioritize domains that are associated with human diseases at the top of the candidate list,while the correlation coefficient approach can achieve slightly higher performance in most cases.Finally,taking the advantage that the correlation coefficient method does not require known disease-domain associations,we calculate a genome-wide landscape of associations between 4036 protein domains and 5080 human disease phenotypes using this method and offer a freely accessible web interface for this landscape.