Dynamic linear model for the identification of miRNAs in next-generation sequencing data.

ABSTRACT: Next-generation sequencing technologies are poised to revolutionize the field of biomedical research. The increased resolution of these data promise to provide a greater understanding of the molecular processes that control the morphology and behavior of a cell. However, the increased amounts of data require innovative statistical procedures that are powerful while still being computationally feasible. In this article, we present a method for identifying small RNA molecules, called miRNAs, which regulate genes by targeting their mRNAs for degradation or translational repression. In the first step of our modeling procedure, we apply an innovative dynamic linear model that identifies candidate miRNA genes in high-throughput sequencing data. The model is flexible and can accurately identify i

SUBMITTER: Evan Johnson W 

PROVIDER: S-EPMC3116054 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature