Advances in Bioinformatics and Computational Biology: Second by Marie-France Sagot, Maria Emilia M.T. Walter

By Marie-France Sagot, Maria Emilia M.T. Walter

This ebook constitutes the refereed court cases of the second one Brazilian Symposium on Bioinformatics, BSB 2007, held in Angra dos Reis, Brazil, in August 2007; co-located with IWGD 2007, the foreign Workshop on Genomic Databases.

The thirteen revised complete papers and six revised prolonged abstracts have been rigorously reviewed and chosen from 60 submissions. The papers tackle a vast variety of present subject matters in computationl biology and bioinformatics that includes unique learn in computing device technology, arithmetic and records in addition to in molecular biology, biochemistry, genetics, medication, microbiology and different lifestyles sciences.

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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. 1 Previous Work This problem has been dealt with different approaches. Some of the existing algorithms like CONSENSUS [8], Gibbs [11], and MEME [1] are local search based algorithms.

First, we look for a set of “consensus” partitions instead of only one. In fact, our set of solutions may contain partitions that are combinations of other partitions, or partitions of high quality that already appeared in the set of individual partitions. Second, we combine pairs of partitions, iteratively, in an optimization process, instead of the usual combination of all partitions at the same time. Such an iterative combination/selection of the partitions avoids the negative influence of low quality base partitions that can decrease the quality of the results of the traditional ensembles.

In order to compute this distance we first recall some basic definitions. The Hamming distance d(s1 , s2 ) between two substrings of length l is the number of characters in which they differ. For each string Sj , let d(i, Sj ) = min{d(i, p)|p ∈ Sj }, where p denotes a substring of length l and i the candidate motif. Then the N total distance from i to the N strings is given by d(i, S) = j=1 d(i, Sj ). In this case we need to minimize this distance, so at the end of the algorithm, those who have survived will represent a candidate motif for the one that was originally implanted.

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