Fast phylogenetic inference from typing data

Abstract

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 profile 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 different profiles. On the other hand, computing genetic evolutionary distances among a set of typing profiles or taxa dominates the running time of many phylogenetic inference methods. It is important also to note that most of genetic evolution distance definitions rely, even if indirectly, on computing the pairwise Hamming distance among sequences or profiles.

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.

Keywords: Computational biology; Hamming distance; Phylogenetic inference.

Fig. 1

Synthetic datasets, with $\sigma =2$ and $k=\lfloor m/(2logm)\rfloor$ according to Theorem 1. Running time for computing pairwise distances by finding lower and higher bounds in the SA, and by processing LCP based clusters, as function of the input size $n=dm$

Fig. 2

Synthetic datasets, with $\sigma =2$ and $m=4096$. Running time for computing pairwise distances by finding lower and higher bounds in the SA, and by processing LCP based clusters, as function of the number d of profiles and for different values of k

Fig. 3

Synthetic datasets, with $\sigma =2$ and $k=\lfloor m/(2logm)\rfloor$ according to Theorem 1. Running time for computing pairwise distances naïvely, by finding lower and higher bounds in the SA, and by processing LCP based clusters, as a function of the number d of profiles

Fig. 4

The tree inferred for the largest connected component found with $k=52$ for the C. jejuni dataset. Image produced by PHYLOViZ [35]