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Advisor(s)
Abstract(s)
Background: Microbial typing methods are commonly used to study the relatedness of bacterial strains. Sequence based typing methods are a gold standard for epidemiological surveillance due to the inherent portability of sequence and allelic profle data, fast analysis times and their capacity to create common nomenclatures for strains or clones. This led to development of several novel methods and several databases being made available for many microbial species. With the mainstream use of High Throughput Sequencing, the amount of data being accumulated in these databases is huge, storing thousands of diferent profles. On the other hand, computing genetic evolution ary distances among a set of typing profles or taxa dominates the running time of many phylogenetic inference methods. It is important also to note that most of genetic evolution distance defnitions rely, even if indirectly, on computing the pairwise Hamming distance among sequences or profles.
Results: We propose here an average-case linear-time algorithm to compute pairwise Hamming distances among a set of taxa under a given Hamming distance threshold. This article includes both a theoretical analysis and extensive experimental results concerning the proposed algorithm. We further show how this algorithm can be successfully integrated into a well known phylogenetic inference method, and how it can be used to speedup querying local phylogenetic patterns over large typing databases.
Description
Keywords
Computational biology Phylogenetic inference Hamming distance
Citation
CARRIÇO, João A.; [et al] – Fast phylogenetic inference from typing data. Algorithms for Molecular Biology. ISSN 1748-7188. Vol. 13 (2018), pp. 1-14
Publisher
Springer Nature