  Up-to-date meta-databases are vital for the analysis of biological data.
However,the current exponential increase in biological data leads to
exponentially increasing meta-database sizes. Large-scale meta-database
management is therefore an important challenge for production platforms
providing services for biological data analysis. In particular, there is often
a need either to run an analysis with a particular version of a meta-database,
or to rerun an analysis with an updated meta-database. We present our GeStore
approach for biological meta-database management. It provides efficient storage
and runtime generation of specific meta-database versions, and efficient
incremental updates for biological data analysis tools. The approach is
transparent to the tools, and we provide a framework that makes it easy to
integrate GeStore with biological data analysis frameworks. We present the
GeStore system, an evaluation of the performance characteristics of the system,
and an evaluation of the benefits for a biological data analysis workflow.
