By Fabian Amman, Stephan H. Bernhart (auth.), João C. Setubal, Nalvo F. Almeida (eds.)

ISBN-10: 3319026232

ISBN-13: 9783319026237

ISBN-10: 3319026240

ISBN-13: 9783319026244

This ebook constitutes the refereed lawsuits of the eighth Brazilian Symposium on Bioinformatics, BSB 2013, held in Recife, Brazil, in November 2013. The 18 typical papers offered have been conscientiously reviewed and chosen for inclusion during this publication. The papers disguise all facets of bioinformatics and computational biology.

**Read Online or Download Advances in Bioinformatics and Computational Biology: 8th Brazilian Symposium on Bioinformatics, BSB 2013, Recife, Brazil, November 3-7, 2013, Proceedings PDF**

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**Additional info for Advances in Bioinformatics and Computational Biology: 8th Brazilian Symposium on Bioinformatics, BSB 2013, Recife, Brazil, November 3-7, 2013, Proceedings**

**Example text**

S. S. Guimar˜ aes An important point is the fact that PTG is based solely on parsimony, disregarding any other type of information or precondition about the genotype sequences, while methods based on Markov chains, such as fastPHASE, use the parsimony criterion as a help, applying additional techniques, models, and insights to ﬁnd a biologically more plausible solution. Nonetheless, that combination leads to an extremely high computational time requirement. Hence, as the length of the genotype sequences grow, those methods become non-viable.

Comparing HybHap to the classical approach fastPHASE, we observed that the accuracy performances considering Error Rate were very close, and for the larger datasets in the benchmark, the diﬀerences between the Error Rates for the two methods were smaller than 2%. 93% of error in about 72 hours. 74%, however, the time necessary for fastPHASE to resolve it was approximately 1080 times longer than the time required by HybHap. Figure 3 shows graphical comparisons of HybHap with fastPHASE and PTG, in regard to Error Rate (Figure 3-A) and computational time (Figure 3-B).

The HybHap method (Algorithm 1) has three main steps: Initialization, Resolution of genotype preﬁx with known solution (genotype fragments that have only homozygous sites or one heterozygous site), as described in Algorithm 2, and explanation of genotype fragments that have no previous resolution (more than one heterozygous site), as described in Algorithm 3. In initialization the Markov chain is computed as described before, the root is created and labelled with all genotype Ids of G. The 2 explains the genotype fragments (preﬁx) that have at most one heterozygous site.

### Advances in Bioinformatics and Computational Biology: 8th Brazilian Symposium on Bioinformatics, BSB 2013, Recife, Brazil, November 3-7, 2013, Proceedings by Fabian Amman, Stephan H. Bernhart (auth.), João C. Setubal, Nalvo F. Almeida (eds.)

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