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The information originally stored at NCBI GenBank in ASN.1 flat-files has been extracted, reorganize and stored in a MySQL relational database. Such information extraction and re- organization enables researchers to construct meaningful biological queries that can be carried out in a precise and flexible environment. For example, at NCBI the specific cultivar cannot be specified for searches. If a researcher were to execute a query for sequences using the following query: Zea mays[ORGN] AND GSS[PROP] AND B73 with intention of retrieving all maize GSS sequences generated only from inbred cultivar B73, she would end up with a mixture of the desired sequences along with hundreds of thousands of RescueMu transposon-tagged GSS sequences generated from a W23/A188/B73 mixed background. Similar problems exist for searching tissue type, developmental stage, etc. However, at PlantGDB, the cultivar information is stored in a column of a relational table, enabling researchers to accurately specify that only B73 GSS be retrieved using the PlantGDB TableMaker tool.

Another limitation at NCBI and many other biological databases is that web-based query capabilities are restricted. For example, a researcher might search for kinesin genes at GenBank by carrying out a keyword search. GenBank allows users to retrieve a list of records matching the kinesin keyword, but often biologists want more than just a list. Depending upon the desired research application, researchers may need a flexible tabular report such as the one with four-columns Organism, Sequence ID, Developmental Stage, and Tissue Type. By using a relational database, PlantGDB is able to provide dynamic query tools over the web permitting researchers to easily construct their own queries based on the table