By Broňa Brejová, Michal Burger, Tomáš Vinař (auth.), Teresa M. Przytycka, Marie-France Sagot (eds.)

This ebook constitutes the refereed lawsuits of the eleventh overseas Workshop on Algorithms in Bioinformatics, WABI 2011, held in Saarbrücken, Germany, in September 2011.

The 30 papers awarded have been rigorously reviewed and chosen from seventy seven submissions. They hide facets of algorithms in bioinformatics, computational biology and platforms biology.

**Read Online or Download Algorithms in Bioinformatics: 11th International Workshop, WABI 2011, Saarbrücken, Germany, September 5-7, 2011. Proceedings PDF**

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**Additional info for Algorithms in Bioinformatics: 11th International Workshop, WABI 2011, Saarbrücken, Germany, September 5-7, 2011. Proceedings**

**Sample text**

Binary ˜i,p = variable a ˜i,p = 1 if and only if locus p of genotype i is heterozygous: a |a2i−1,p − a2i,p | for 1 ≤ i ≤ L, 1 ≤ p ≤ m. Now a genotype i is heterozygous, indicated by hi = 1, if at least one of its loci is heterozygous: hi ≥ a ˜i,p for 1 ≤ i ≤ L, 1 ≤ p ≤ m. It is ensured that hi = 0 whenever a ˜i,p = 0, ∀1 ≤ p ≤ m, as hi = 1 would increase the required population size. e. ν(i) and ν(i + 1). We capture the sequence s of heterozygous loci used in (2) by binary variables bi,p,q , which indicate a maximal block of homozygous loci between heterozygous loci p and q in genotype i: q−1 bi,p,q = a ˜i,p · a ˜i,q · (1 − a ˜i,r ) 1 ≤ i ≤ L, 1 ≤ p < q ≤ m (9) r=p+1 To formulate the probability given in (2), let ξkj denote the event of obtaining a chromosome k from a genotype j.

It is ensured that hi = 0 whenever a ˜i,p = 0, ∀1 ≤ p ≤ m, as hi = 1 would increase the required population size. e. ν(i) and ν(i + 1). We capture the sequence s of heterozygous loci used in (2) by binary variables bi,p,q , which indicate a maximal block of homozygous loci between heterozygous loci p and q in genotype i: q−1 bi,p,q = a ˜i,p · a ˜i,q · (1 − a ˜i,r ) 1 ≤ i ≤ L, 1 ≤ p < q ≤ m (9) r=p+1 To formulate the probability given in (2), let ξkj denote the event of obtaining a chromosome k from a genotype j.

01 5000 5 0 tree 0 obj. val. ratio 30 4 8 5 6 7 8 1 Fig. 2. Results for generated instances. Left: optimality of solutions. Right: running times; instances exceeding the time limit were not considered, objective value ratio (right y-axis). Marker-Assisted Gene Pyramiding 37 In total 140 instances are generated, among which 20 concern instances of 4 loci; the classes of 5-8 loci are comprised by 30 instances each. We run both the DAG and the tree version of the MIP on all instances. For the DAG case, we were able to obtain solutions to 128 instances compared to 119 instances (see Figure 2) for the tree version.