> In order to be able to exchange the semantics of information in a database on viruses one first needs to agree on how to explicitly model a virus ontology architecture.
Just as weather forecasters chart storms off Africa to predict which ones could become hurricanes, he hopes to create a database of animal viruses that have the potential to cross into the human population over the next few decades.
Moreover, by performing a bioinformatics analysis, using the 266 human H5N1 virus strains available in the database of Influenza Virus Resource (http://www.ncbi.nlm.nih.gov/genomes/FLU/FLU.html), we found that residues Lys119 and Trp122 were absolutely conserved while other residues in the proposed linear epitope were highly conserved among 266 human H5N1 viruses (96.2% 99.9%).
To evaluate variations in gene function, metagenomic reads were compared to an expansive database of marine virus protein sequences (>456K protein clusters derived from over 6M reads from 32 diverse pelagic ocean virus communities [ 18]).
Therefore, we developed the database of bat-associated viruses (DBatVir), a comprehensive, up-to-date and well-curated repository of bat-associated animal viruses.
One Codex, which is currently in open beta, can search its growing database of 30,000 bacteria, viruses and fungi in real time and identify data sets in minutes (millions of DNA base pairs per second).
Of the 756 unique spacers, only 50 have significant (E<0.001) BLAST matches to a database of Sulfolobus genomes, viruses, and plasmids.
To facilitate further research, we constructed the database of bat-associated viruses (DBatVir).
To estimate the dependence of the number of ORFans to our database of fully sequenced viruses, we have searched for homologs of our identified viral ORFans within the recent (as of May 22, 2007) nr [ 35] and env_nr [ 36] (which includes the 6 million recently published predicted marine metagenomic proteins [ 37]) databases.
We analyzed a database of known zoonotic viruses in mammal hosts to answer the driving question of whether we should stratify surveillance strategies (i.e., conduct surveillance of visibly diseased vs. apparently healthy animals) by wildlife host groups to best detect novel pathogens with zoonotic potential.
