Motif Finding with Application to the Transcription Factor Binding Sites Problem (original) (raw)
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are useful to determine nucleotides/amino-acids that are likely involved in structures, functions, regulations and evolutions, or to infer homology between genes/proteins. The main objective of this paper is the fusion of motifs. Our task is to analyze a set of possible motifs and to detect if similarity exists between them, to construct a general motif. The motifs fusion method is based on the algorithm of combinatorial optimization called Artificial Ants System. This method uses the nucleotides of the first motif to construct the graph where the ants will walk. Then, the graph is crossed by the ants according to the path of the second motif, using a transition function that promoves to flow the path between similar nucleotides. The ants when walking leave pheromone in the nodes, in a way that at the end several have a lot of or little pheromone. Finally the graph is crossed again to construct the resultant motif composed by the nodes with much pheromone.
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Many approaches are currently devoted to find DNA motifs in nucleotide sequences. However, this task remains challenging for specialists nowadays due to the difficulties they find to deeply understand gene regulatory mechanisms, especially when analyzing binding sites in DNA. These sites or specific nucleotide sequences are known to be responsible for transcription processes. Thus, this work aims at providing an updated overview on strategies developed to discover meaningful motifs in DNA-related sequences, and, in particular, their attempts to find out relevant binding sites. From all existing approaches, this work is focused on dictionary, ensemble, and artificial intelligence-based algorithms since they represent the classical and the leading ones, respectively.
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The Ant Colony Optimization Algorithm is a novel optimization algorithm based on the intelligence of ant behavior, whereas the Traveling Salesman Problem is the problem of determining the shortest route between a group of cities that start in one city and visit each other city only once before returning to the starting (home) city. This study proposes an Ant Colony Optimization approach with the Traveling Salesman Problem (ACO-TSP) for DNA Sequence Optimizations. The proposed technique is a unique ant colony optimization approach for reconstructing DNA sequences from fragments of DNA. Existing meta-heuristics, on the other hand, are consistently outperformed in terms of performance by newly invented constructive heuristics. This model was developed based on these novel heuristics, with four nodes (cities) representing the four DNA bases. According to the findings of the experiments, the new approach is more reliable and generates higher-quality results with a minimum average of 17.6 and a maximum DNA accuracy similarity of 59.1.
An Efficient Combinatorial Approach for Solving the DNA Motif Finding Problem
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The detection of an over-represented sub-sequence in a set of (carefully chosen) DNA sequences is often the main clue leading to the investigation of a possible functional role for such a subsequence. Over-represented substrings (with possibly local mutations) in a biological string are termed motifs. A typical functional unit that can be modeled by a motif is a Transcription Factor Binding Site (TFBS), a portion of the DNA sequence apt to the binding of a protein that participates in complex transcriptomic biochemical reactions.