Improving microRNA target prediction with gene expression profiles.

ABSTRACT:

Background

Mammalian genomes encode for thousands of microRNAs, which can potentially regulate the majority of protein-coding genes. They have been implicated in development and disease, leading to great interest in understanding their function, with computational methods being widely used to predict their targets. Most computational methods rely on sequence features, thermodynamics, and conservation filters; essentially scanning the whole transcriptome to predict one set of targets for each microRNA. This has the limitation of not considering that the same microRNA could have different sets of targets, and thus different functions, when expressed in different types of cells.

Results

To address this problem, we combine popular target prediction methods with expression profil

SUBMITTER: Ovando-Vazquez C 

PROVIDER: S-EPMC4869178 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature