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Advisor(s)
Abstract(s)
Typing methods are widely used in the surveillance of infectious diseases, outbreaks investigation and studies of the natural history of an infection. And their use is becoming standard, in particular with the introduction of High Throughput Sequencing (HTS). On the other hand, the data being generated is massive and many algorithms have been proposed for phylogenetic analysis of typing data, such as the goeBURST algorithm. These algorithms must however be run whenever new data becomes available starting from scratch. We address this issue proposing a dynamic version of goeBURST algorithm. Experimental results show that this new version is efficient on integrating new data and updating inferred evolutionary patterns, improving the update running time by at least one order of magnitude.
Description
Keywords
Phylogenetic inference Phylogenetic trees Dynamic algorithms Sequence-based typing data
Citation
Dynamic phylogenetic inference for sequence-based typing data. In ACM-BCB '17 Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics. Boston, Massachusetts, USA: Assoc. Computing Machinery, 2017. ISBN 978-1-4503-4722-8. Pp. 604-604
Publisher
Association for Computing Machinery