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Streptococcus agalactiae is an invasive multi-host pathogen that causes invasive diseases mainly in newborns, elderly, and individuals with underlying health complications. In fish, S. agalactiae causes streptococcosis, which is characterized by septicemia and neurological signs, and leads to great economic losses to the fish farming industry worldwide. These bacteria can be classified into different serotypes based on capsular antigens, and into different sequence types (ST) based on multilocus sequence typing (MLST). In 2015, serotype III ST283 was identified to be associated with a foodborne invasive disease in non-pregnant immunocompetent humans in Singapore, and the infection was related to raw fish consumption. In addition, a serotype III strain isolated from tilapia in Brazil has been reported to be resistant to five antibiotic classes. This specific serotype can serve as a reservoir of resistance genes and pose a serious threat to public health. Thus, new approaches for the control and treatment of S. agalactiae infections are needed. In the present study, 24 S. agalactiae serotype III complete genomes, isolated from human and fish hosts, were compared. The core genome was identified, and, using bioinformatics tools and subtractive criteria, five proteins were identified as potential drug targets. Furthermore, 5,008 drug-like natural compounds were virtually screened against the identified targets. The ligands with the best binding properties are suggested for further in vitro and in vivo analysis.

Improvements in genome sequencing techniques have resulted in generation of huge volumes of data. As a consequence of this progress, the genome assembly stage demands even more computational power, since the incoming sequence files contain large amounts of data. To speed up the process, it is often necessary to distribute the workload among a group of machines. However, this requires hardware and software solutions specially configured for this purpose. Grid computing try to simplify this process of aggregate resources, but do not always offer the best performance possible due to heterogeneity and decentralized management of its resources. Thus, it is necessary to develop software that takes into account these peculiarities. In order to achieve this purpose, we developed an algorithm aimed to optimize the functionality of de novo assembly software ABySS in order to optimize its operation in grids. We run ABySS with and without the algorithm we developed in the grid simulator SimGrid. Tests showed that our algorithm is viable, flexible and scalable even on a heterogeneous environment, which improved the genome assembly time in computational grids without changing its quality.