Computational methods of inferring regions of noncoding DNA that regulate gene activity are important to efficient biological validation of gene regulatory control. In many cases the available resources may allow for relatively few biological assays to be performed, and computational results allow these assays to be tightly focused on the highest confidence candidate regulatory regions. Here, we present a workflow for the computational identification of candidate regulatory regions that incorporates multiple lines of evidence and illustrate its use to select high-confidence targets for experimental verification using the ciliary gene Tektin3 as an example. The Tektin3 protein is vital for ciliogenesis, a process in which cilia are formed. Cilia are important organelles of cells that are involved in numerous activities, and are related to many human diseases. The study of ciliogenesis genes may lead to advances in treatment for related diseases in humans, including diseases caused by malformation of the cilia.