  With growing exchanges of people and merchandise between countries, epidemics
have become an issue of increasing importance and huge amounts of data are
being collected every day. Hence, analyses that were usually run in personal
computers and desktops are no longer feasible. It is now common to run such
tasks in High-performance computing (HPC) environments and/or dedicated
systems. On the other hand we are often dealing in these analyses with graphs
and trees, and running algorithms to find patterns in such structures. Hence,
although graph oriented databases and processing systems can be of much help in
this setting, as far as we know there is no solution relying on these
technologies to address large scale phylogenetic analysis challenges. This
project aims to develop a modular framework for large scale phylogenetic
analysis that exploits such technologies, namely Neo4j. We address this
challenge by proposing and developing a framework which allows representing
large phylogenetic networks and trees, as well as ancillary data, that supports
queries on such data, and allows the deployment of algorithms for
inferring/detecting patterns and pre-computing visualizations, as a Neo4j
plugin. This framework is innovative and brings several advantages to the
phylogenetic analysis, such as by storing the phylogenetic trees will avoid
having to compute them again, and by using multilayer networks will make the
comparison between them more efficient and scalable. Experimental results
showcase that it can be very efficient in the mostly used operations and that
the supported algorithms comply with their time complexity.
