ABSTRACT
Three-way junctions in folded RNAs have been investigated both experimentally and computationally. The interest in their analysis stems from the fact that they have significantly been found to possess a functional role. In recent work, three-way junctions have been categorized into families depending on the relative lengths of the segments linking the three helices. Here, based on ideas originating from computational geometry, an algorithm is proposed for detecting three-way junctions in data sets of genes that are related to a metabolic pathway of interest. In its current implementation, the algorithm relies on a moving window that performs energy minimization folding predictions, and is demonstrated on a set of genes that are involved in purine metabolism in plants. The pattern matching algorithm can be extended to other organisms and other metabolic cycles of interest in which three-way junctions have been or will be discovered to play an important role. In the test case presented here with, the computational prediction of a three-way junction in Arabidopsis that was speculated to have an interesting functional role is verified experimentally.
