Codes for RNA structure prediction based on energy minimization are usually very time and resource intensive. For this reason several codes have been significantly simplified: in some cases they are unable to predict complex secondary structures such as pseudoknots, while at other times they are able to predict structures with reduced lengths, or they are only able to predict some elementary and simple pseudoknots. Each of the existing codes has its strengths and weaknesses. Providing scientists with tools that are able to combine the strengths of the several codes is a worthwhile objective.
To address this need, we present compPknots, a parallel framework that uses a combination of existing codes such as Pknots-RE and Pknots-RG, to predict RNA secondary structures concurrently and automatically compare them with reference structures from databases or literature. In this paper compPknots is used to compare and contrast the prediction accuracies of 217 RNA secondary structures from the PseudoBase database using Pknots-RE and Pknots-RG separately, or both together. Its parallel master-slave architecture allowed us to prove that combinations of prediction codes can provide scientists with higher prediction accuracies in a very short time.