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By Sérgio Campos

This booklet constitutes the refereed court cases of the ninth Brazilian Symposium on Bioinformatics, BSB 2014, held in Belo Horizonte, Brazil, in October 2014. The 18 revised complete papers offered have been conscientiously reviewed and chosen from 32 submissions. The papers hide all features of bioinformatics and computational biology.

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Extra info for Advances in Bioinformatics and Computational Biology: 9th Brazilian Symposium on Bioinformatics, BSB 2014, Belo Horizonte, Brazil, October 28-30, 2014, Proceedings

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Conversion of a sequence to two structural properties Considering the conversion schema previously mentioned, in order to show the capability of the structural properties to discriminate promoter from nonpromoter sequences, Figure 2 illustrates, for two structural properties, the average structural profile of promoter and non-promoter sequences of the 250-50 dataset. In this figure, TSS is located at the 0 position. 385 -250 -200 -150 -100 Position -50 0 50 Fig. 2. Structural profiles for the 250-50 dataset The complete characterization of a sequence is given by a single numerical vector resulting from the junction of the vectors representing each of the 13 structural properties considered in this work.

Once all the topological network aspects are understood for a particular metagenome, we envisage the possibility of using such community profiles for metagenome comparison as well as classification of unknown microbial genetic network. 32 L. Corrêa et al. Author’s Contributions LC and RA performed the analysis and developped the pipeline. RA and CC supervised the study. LC, RA, CC and LT wrote the manuscript. Acknowledgements. This work is partially supported by the Brazilian National Research Council (CNPq – Universal calls) under the BIOFLOWS project [475620/2012-7].

As an example, the largest dataset used in our experiments, the 250-50 one, results in a set of 3898 predictor attributes. Table 2 shows the number of predictor attributes for each dataset used in this work. Table 2. Predictor attributes for each dataset Dataset 10-1 10-3 10-5 10-10 10-20 10-30 10-40 10-50 Number of atributes Dataset Number of atributes 141 167 193 258 388 518 648 778 20-50 30-50 40-50 50-50 100-50 150-50 200-50 250-50 908 1038 1168 1298 1948 2598 3248 3898 As it can be observed in Table 2, the length of sequences used to compose the dataset defines the amount of their attributes.

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