A framework for large scale phylogenetic analysis

by Bruno Miguel Leandro Lourenço

MSc thesis presentation and discussion.

Date: 2021-Jan-22   Time: 16:30
Room: Zoom

Abstract: 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 are no longer feasible. It is now common to run such tasks
in High-performance computing 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 work aims to
develop a modular framework 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 process, like the management of the phylogenetic trees,
which will avoid having to compute them again, and the use of multilayer
networks, that will make the comparison between them more efficient and
scalable. The experimental evaluation results showcase that it can be very
efficient in the mostly used operations and that the supported algorithms
comply with their time complexity.