Biologically relevant MicroRNA Target Prediction by Integrating MicroRNA and mRNA expression data

From Master Projects
Jump to: navigation, search


has title::Biologically relevant MicroRNA Target Prediction by Integrating MicroRNA and mRNA expression data
status: finished
Master: project within::Bioinformatics
Student name: student name::Sander Bervoets
Dates
Start start date:=2010/11/01
End end date:=2011/02/28
Supervision
Supervisor: Maarten van Iterson
Second reader: has second reader::Anton Feenstra
Company: has company::LUMC Human Genomics
Thesis: has thesis::Media:Sander Bervoets - Biologically Relevant MicroRNA Target Prediction by Integrating MicroRNA and mRNA Expression Data using Globaltest.pdf
Poster: has poster::Media:Media:Posternaam.pdf

Signature supervisor



..................................

Abstract

MicroRNA (miRNA)-related research has grown exponentially since their discovery in 1990. Multiple roles for miRNAs have been revealed such as negative regulation (transcript degradation and sequestering, translational suppression) and possible involvement in positive regulation (transcriptional and translational activation). Regulation by miRNA is vital for processes like cell differentiation. Also, misregulation or disruption of miRNA expression has been shown to cause mental retardation and cancer. Therefore, the clinical applications of understanding miRNA regulation are evident.

An important aspect of miRNA research is to find which transcripts of which genes are regulated by which miRNA. However, target prediction tools are far from perfect and experimental identification is laborious and therefore usually done on a one-to-one basis.

Here we present a novel approach were the mRNA expression profiles of a set of putative predicted targets is associated with the miRNA expression profile under the biological condition of interest. Integration of the miRNA and mRNA expression for a set of putative targets gives biological relevance to the miRNA-mRNA target interactions. Our method goes beyond gene set and gene set enrichment method that are currently adapted in order to be able to predict miRNA-mRNA target interactions.