Authors: Spetale Flavio Ezequiel; Murillo Javier; Bulacio Pilar; Tapia Elizabeth.
Resumen: A large part of the RNA in eukaryotic organisms is non-coding, that is, its final product is not a protein. Unlike proteins, the functionalinformation of lncRNAs (noncoding RNAs with +200 nucleotides) is only weakly reflected at their primary sequence level. The study of lncRNA is relevant because they participate in gene regulation and activation. Gene Ontology (GO) provides a controlled vocabulary of terms for describing gene properties and their relationships in a species-independent manner. GO includes three sub-ontologies, biological process (BP), molecular function (MF), and cellular component (CC).Taking into account that the experimental characterization of lncRNA is hard due to the increasing instability of RNA molecules with their length, the development of in-silico methods is crucial to accelerate their study.In this work, we present a supervised computational method,graph-based, for the automatic GO prediction of lncRNAs. The method, called FGGA-GO+ , aims consistent lncRNA GO annotations across the three GO sub-ontologies domains. Preliminary results for lncRNAs from the model organism Zebrafish (Danio rerio) on the BP and MF GO domains confirm the feasibility of our proposal. For the sake of simplicity, lncRNAs were characterized by their RFAM domains.
Meeting type: Conferencia.
Type of job: Artículo Breve.
Production: Cross-Ontology Prediction of lncRNAs on Zebrafish.
Scientific meeting: A2B2C 10th Meeting.
Meeting place: Mendoza.
Organizing Institution: Universidad Nacional de Cuyo.
It's published?: Yes
Publication place: Mendoza
Meeting month: 11