Implementation of computational approaches to identify long noncoding RNAs involved in neuronal differentiation

Abstract

Recent studies have revealed that long noncoding RNAs (> 200 nt, lncRNAs) may act by distinct mechanisms to exert regulatory functions to control gene expression in eukaryotes. It is known that lncRNAs may affect gene expression patterns during cellular differentiation in a way similar to the observed for well-studied regulatory proteins. In fact, some lncRNAs have been shown to be essential to the differentiation process. During this project we will implement computational methodologies and resources to the analysis of global gene expression data collected from murine undifferentiated embryonary carcinoma cells (P19 cells) or P19 differentiated into neuron or glia cells. LncRNAs differentially expressed during cell differentiation will go through detailed structural and genomic annotation. This will include deployment of different methodologies to evaluate their coding potential and degree of evolutionary conservation, the presence of thermodynamically stable and evolutionary conserved secondary structures, and the annotation of neighboring genes and regulatory chromatin marks. To identify gene networks regulated by lncRNAs, gene expression data will searched for co-regulation of lncRNAs and mRNAs during cell differentiation, as well as for expression changes is specific gene categories and molecular pathways. All the results will be incorporated in a relational database. The end result of the project will be a bioinformatics platform for processing and data mining of gene expression data to help the selection of novel lncRNAs relevant to the neuronal differentiation for detailed experimental characterization. This platform will be generic and able to incorporate additional sets of gene expression data that will be generated from other models of cellular differentiation along ongoing research projects in our laboratory.