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Download Advances in Bioinformatics and Computational Biology: Second by K. S. Machado, E. K. Schroeder, D. D. Ruiz, O. Norberto de PDF

By K. S. Machado, E. K. Schroeder, D. D. Ruiz, O. Norberto de Souza (auth.), Marie-France Sagot, Maria Emilia M. T. Walter (eds.)

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Read or Download Advances in Bioinformatics and Computational Biology: Second Brazilian Symposium on Bioinformatics, BSB 2007, Angra dos Reis, Brazil, August 29-31, 2007. Proceedings PDF

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Extra resources for Advances in Bioinformatics and Computational Biology: Second Brazilian Symposium on Bioinformatics, BSB 2007, Angra dos Reis, Brazil, August 29-31, 2007. Proceedings

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Each pi ∈ {1, 2, · · · , T − l + 1} ∪ {0}, with pi = 0 means that sequence Si contains no occurrences of the motif. Notice that this problem was already implicitly defined in [17]. Although the planted motif problem was not explicitly defined, the author introduced two variants of the motif finding problem and, one of them includes the motif finding problem as it is defined above. The complexity of this problem and its variants (all NP-Hard [23]) has motivated the development of efficient heuristics to deal with them.

P2N ], we choose a crossover point at random, in such a way that the new individual (child1) will inherit all positions to the left of the crossover point from P1 , and the positions to the right from P2 . The following example illustrates this crossover operator: P1 = 23 309 276 12 513 , P2 = 506 281 105 33 447 child1 = 23 309 105 33 447 In this example, the crossover point is chosen between the second and third position, so the first two positions are copied from the first parent and the other three from the second.

The algorithm tries to find all alignments, which maximize the sum of the maximum frequencies of nucleotides at each position of the alignment over the motif length. In the second algorithm, GA2, the chromosome represents a candidate consensus string of length l and the algorithm tries to maximize the score of this consensus string with respect to all sequences Comparison of Simple Encoding Schemes 25 in the data set. Both algorithms were tested with synthetic data and some real samples (in the case of GA2).

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