RIPGA: RNA-RNA interaction prediction using genetic algorithm

Non-coding RNAs are RNA molecules that do not translate into proteins. These RNAs are functional important in many biological processes. Their biological functions are highly related to their interaction partners. RNA-RNA interactions are one of the possibilities. It is desired to use computational methods to study and predict the interaction partners of non-coding RNAs. Most recent programs for RNA-RNA interaction prediction programs are based on Turner Energy Model. Some papers show that the free energy of RNA-structures is lower than random sequences but it is not statistically significant. It shows that we may need to modify the energy model for different RNA structures applications. In this paper, we first study the RNA-RNA interaction pattern using experimental validated RNA-RNA interaction data, which are extracted from sRNATarBase. We study the sRNA-mRNA interaction data and extract some features of the RNA-RNA interaction patterns. Then we combine these features about interaction sites into the Turner Energy Model. We develop a genetic algorithm based program RIPGA to solve the RNA-RNA interaction prediction problem. We use genetic algorithm because the RNA-RNA interaction prediction is NP-hard and we are interested to find out good suboptimal solutions. We use an sRNA-mRNA interaction dataset to evaluate the performance of the modified energy model and compare the results with two state-of-the-art programs. The comparison of the original model and the modified model shows that the modified energy model has better performance in both sensitivity and positive predictive value (PPV). Comparing RIPGA with state-of-the-art programs, RIPGA have better sensitivity and comparable positive predictive value.

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