title: In silico prediction of lncRNA function using tissue specific and evolutionary conserved expression.
abstract: BACKGROUND
  In recent years long non coding RNAs (lncRNAs) have been the subject of increasing interest. Thanks to many recent functional studies, the existence of a large class of lncRNAs with potential regulatory functions is now widely accepted. Although an increasing number of lncRNAs is being characterized and shown to be involved in many biological processes, the functions of the vast majority lncRNA genes is still unknown. Therefore computational methods able to take advantage of the increasing amount of publicly available data to predict lncRNA functions could be very useful.
  
  
  RESULTS
  Since coding genes are much better annotated than lncRNAs, we attempted to project known functional information regarding proteins onto non coding genes using the guilt by association principle: if a gene shows an expression profile that correlates with those of a set of coding genes involved in a given function, that gene is probably involved in the same function. We computed gene coexpression for 30 human tissues and 9 vertebrates and mined the resulting networks with a methodology inspired by the rank product algorithm used to identify differentially expressed genes. Using different types of reference data we can predict putative new annotations for thousands of lncRNAs and proteins, ranging from cellular localization to relevance for disease and cancer.
  
  
  CONCLUSIONS
  New function of coding genes and lncRNA can be profitably predicted using tissue specific coexpression, as well as expression of orthologous genes in different species. The data are available for download and through a user-friendly web interface at www.funcpred.com .
authors.affiliation: Department of Molecular Biotechnology and Health Sciences, University of Turin, via Nizza 52, Torino, 10126, Italy.
authors.forename: Umberto
authors.initials: U
authors.lastname: Perron
authors.affiliation: Department of Molecular Biotechnology and Health Sciences, University of Turin, via Nizza 52, Torino, 10126, Italy.|Center for Translational Genomics and Bioinformatics, San Raffaele Scientific Institute, via Olgettina 60, Milan, 20132, Italy.
authors.forename: Paolo
authors.initials: P
authors.lastname: Provero
authors.affiliation: Department of Molecular Biotechnology and Health Sciences, University of Turin, via Nizza 52, Torino, 10126, Italy. ivan.molineris@unito.it.
authors.forename: Ivan
authors.initials: I
authors.lastname: Molineris
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D019143:Evolution, Molecular; D006801:Humans; D008957:Models, Genetic; D009928:Organ Specificity; D062085:RNA, Long Noncoding / Q000235:genetics* / Q000502:physiology; D059467:Transcriptome
chemical_list: D062085:RNA, Long Noncoding
keywords: Coexpression; Disease gene prediction; Functional prediction; lncRNA