Advances in Big Data Bio Analytics

TL;DR

This paper presents advances in logic programming tools for biological data analytics, including enhanced database access, multi-organism support, and user-friendly interfaces, to improve biological data interpretation.

Contribution

It introduces significant improvements to the bio_db package and bio_analytics tools, enabling easier, more flexible biological data analysis using logic programming.

Findings

Enhanced bio_db package with modular architecture

Support for mouse organism datasets

Availability of a graphical user interface via SWISH

Abstract

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.