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 e-book constitutes the refereed lawsuits 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 overseas 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 extensive diversity of present themes in computationl biology and bioinformatics that includes unique study in machine technological know-how, arithmetic and data in addition to in molecular biology, biochemistry, genetics, drugs, microbiology and different lifestyles sciences.

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

Sample text

Combinatorial techniques like branch pruning and linear programming are used to solve the problem. In [15] an algorithm based on the planted motif model, looking for the planted motif instead of its occurrences, is proposed. The main idea of this approach, known as Pattern Branching, is that if we have a real occurrence, then we can obtain the original motif by changing, in this occurrence, exactly d bases. So if we analyze all the possible substrings in all DNA strings, we will have, at the end, the best motif that minimizes a given score.

We assume, without lost of generality, that all strings Si are of the same length. The objective of the planted motif problem is to find all the occurrences of the (l, d)-motifs that appear in each of the N input strings without knowing, a priori, the motif. We will now formally define the Planted Motif Finding Problem as the following input/output requirements. , SN } each of length T over the alphabet {a, c, g, t}, where each string contains a planted occurrence of the motif. The 24 G. A. Brizuela length of the motif (l) and the maximum number d of possible mutations in its occurrences.

The second encoding better captures the idea of and individual and its evolution regarding biological motifs. This algorithm is based on ideas provided by a 32 G. A. Brizuela successful heuristic for the planted (l, d)-motif model known as Pattern Branching. Experimental preliminary results show a clear solution quality improvement of the motif based representation over the position based representation. Future immediate research will be aimed at extending the set of instances in the comparison set.

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