  Delivering effective data analytics is of crucial importance to the
interpretation of the multitude of biological datasets currently generated by
an ever increasing number of high throughput techniques. Logic programming has
much to offer in this area. Here, we detail advances that highlight two of the
strengths of logical formalisms in developing data analytic solutions in
biological settings: access to large relational databases and building
analytical pipelines collecting graph information from multiple sources. We
present significant advances on the bio_db package which serves biological
databases as Prolog facts that can be served either by in-memory loading or via
database backends. These advances include modularising the underlying
architecture and the incorporation of datasets from a second organism (mouse).
In addition, we introduce a number of data analytics tools that operate on
these datasets and are bundled in the analysis package: bio_analytics. Emphasis
in both packages is on ease of installation and use. We highlight the general
architecture of our components based approach. An experimental graphical user
interface via SWISH for local installation is also available. Finally, we
advocate that biological data analytics is a fertile area which can drive
further innovation in applied logic programming.
