Computational Aspects of In-silico Experiments for Investigating the Impact of the Host Genome on the Influenza Virus A Variability

Nowadays the study of the variability of influenza virus is a problem of very great importance. Influenza type A viruses cause epidemics and pandemics. The problem of restricting the spreading of pandemics and the treatment of the people infected by the influenza virus is widely based on the latest achievements of molecular biology, bioinformatics and biocomputing, as well as many other advanced areas of science. In silico biological sequence processing is a key for molecular biology. This scientific area requires powerful computing resources for exploring large sets of biological data. The paper presents parallel computational simulations for the case study of investigating the role of the host genome in the evolution and fast changeability of the influenza virus A on supercomputer BlueGene/P. The experimental framework is based on all available existing influenza virus A nucleotide sequences, the clustalw algorithm for multiple sequence alignment, the blast algorithm for sequence searching, the Philip software for philogenetic tree reconstruction and the recombination analysis tool for finding hot-spots of mutation/recombination in influenza A virus genomes.