Distance-based phylogenetic inference from typing data: a unifying view

TL;DR

This paper reviews and unifies distance-based phylogenetic inference algorithms from typing data, highlighting their common principles, computational challenges, and potential improvements for large-scale infectious disease analysis.

Contribution

It provides a comprehensive unifying framework for distance-based phylogenetic algorithms, clarifying their differences and identifying computational bottlenecks.

Findings

Unified view of distance-based phylogenetic algorithms

Identification of key computational bottlenecks

Insights for improving scalability with large data sets

Abstract

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, addressing both correctness and scalability issues. Most of the distance-based algorithms for inferring phylogenetic trees follow the closest-pair joining scheme. This is one of the approaches used in hierarchical clustering. And although phylogenetic inference algorithms may seem rather different, the main difference among them resides on how one defines cluster proximity and on which optimization criterion is used. Both cluster proximity and optimization criteria rely often on a model of evolution. In this work we review, and we provide an unified view of these algorithms. This is an important step not only to better understand such algorithms, but also to identify possible computational bottlenecks and improvements, important to deal with large data sets.