As one of the original goals of this competition, iGEM has been dedicated to collecting biobricks. After more than a decade of efforts by iGEMers, iGEM has accumulated a large amount of parts, data and systems, which can be regarded as one of the most influential databases in the field of synthetic biology. However, according to our survey of nearly 100 synthetic biologists, iGEM still lacks joint analysis and retrieval with traditional databases (Genbank, Uniprot, GO, etc.), which is a limitation for iGEMers and researchers to explore and use iGEM big data. In addition, some iGEMers also reflect that the functions such as retrieval and interface in iGEM registry are not intelligent enough and need to be further improved.
BioMaster is a new integrated biobricks database based on iGEM Registry, dedicated to providing synthetic biologists with the best biobricks retrieval experience. We integrated eight traditional biological research databases including Uniprot, EPD and GO, supplemented the information of functions, sites, interactions and references in the biobricks in iGEM, and visualized some information, saving the extra time needed for synthetic biologists to retrieve. At the same time, BioMaster provides four more intelligent retrieval methods to help synthetic biologists more accurately retrieve biological bricks. In addition, we also provide biological bricks predicted by the algorithm for a reference.
As a biobrick database made for synthetic biologists, BioMaster will provide you with the most comprehensive "biobricks service" when building genetic circuits, making biobricks, and advancing synthetic biology projects.
Compared to other similar projects, BioMaster is a database instead of a community or platform. So，BioMaster is more focused on improving iGEM Registry. First of all, BioMaster is far superior to others in terms of information. It integrated eight traditional biological research databases and provides users with sequence, literature, interaction and other information, saving users time to find relevant information. Second, more retrieval methods and more visual expressions enable users to work more efficiently with BioMaster. In the future, we hope that iGEM can be combined with more traditional databases for analysis, and that all the information needed by synthetic biologists can be retrieved in one database, allowing iGEM to be combined with big data and artificial intelligence.