Inverse RNA folding solution based on multi-objective genetic algorithm and Gibbs sampling method

In living systems, RNAs play important biological functions. The functional form of an RNA frequently requires a specific tertiary structure. The scaffold for this structure is provided by secondary structural elements that are hydrogen bonds within the molecule. Here, we concentrate on the inverse RNA folding problem. In this problem, an RNA secondary structure is given as a target structure and the goal is to design an RNA sequence that its structure is the same (or very similar) to the given target structure. Different heuristic search methods have been proposed for this problem. One common feature among these methods is to use a folding algorithm to evaluate the accuracy of the designed RNA sequence during the generation process. The well known folding algorithms take O(n3) times where n is the length of the RNA sequence. In this paper, we introduce a new algorithm called GGI-Fold based on multiobjective genetic algorithm and Gibbs sampling method for the inverse RNA folding problem. Our algorithm generates a sequence where its structure is the same or very similar to the given target structure. The key feature of our method is that it never uses any folding algorithm to improve the quality of the generated sequences. We compare our algorithm with RNA-SSD for some biological test samples. In all test samples, our algorithm outperforms the RNA-SSD method for generating a sequence where its structure is more stable

An intuitionistic approach to scoring DNA sequences against transcription factor binding site motifs

Background: Transcription factors (TFs) control transcription by binding to specific regions of DNA called transcription factor binding sites (TFBSs). The identification of TFBSs is a crucial problem in computational biology and includes the subtask of predicting the location of known TFBS motifs in a given DNA sequence. It has previously been shown that, when scoring matches to known TFBS motifs, interdependencies between positions within a motif should be taken into account. However, this remains a challenging task owing to the fact that sequences similar to those of known TFBSs can occur by chance with a relatively high frequency. Here we present a new method for matching sequences to TFBS motifs based on intuitionistic fuzzy sets (IFS) theory, an approach that has been shown to be particularly appropriate for tackling problems that embody a high degree of uncertainty. Results: We propose SCintuit, a new scoring method for measuring sequence-motif affinity based on IFS theory. Unlike existing methods that consider dependencies between positions, SCintuit is designed to prevent overestimation of less conserved positions of TFBSs. For a given pair of bases, SCintuit is computed not only as a function of their combined probability of occurrence, but also taking into account the individual importance of each single base at its corresponding position. We used SCintuit to identify known TFBSs in DNA sequences. Our method provides excellent results when dealing with both synthetic and real data, outperforming the sensitivity and the specificity of two existing methods in all the experiments we performed. Conclusions: The results show that SCintuit improves the prediction quality for TFs of the existing approaches without compromising sensitivity. In addition, we show how SCintuit can be successfully applied to real research problems. In this study the reliability of the IFS theory for motif discovery tasks is proven

a Center of Excellence in Biomathematics,

Several biological features are presented by different types of trees. Two types of such trees are considered in this paper. the first type is trees with n external nodes that each internal node have at least two children, and are used in neuro-science and called neuronal dendritic trees. The second type is trees with n internal nodes and m external nodes. This type of trees represent the secondary structure of RNA sequences, and called RNA trees. In this paper, we present two new parallel algorithms for generation of these two biological trees. Both algorithms are adoptive and cost-optimal and generate the trees in B-order. Computations run in an SM SIMD model. Keywords: Neuronal Dendritic Trees, RNA Trees, Parallel Algorithm, B-order.