Abstract
Background: Phylogenies enrich our understanding of how genes, genomes, and species evolve. Traditionally, alignment-based methods are used to construct phylogenies from genetic sequence data; however, this process can be time-consuming when analyzing the large amounts of genomic data available today. Additionally, these analyses face challenges due to differences in genome structure, synteny, and the need to identify similarities in the face of repeated substitutions resulting in loss of phylogenetic information contained in the sequence. Alignment Free (AF) approaches using k-mers (short subsequences) can be an efficient alternative due to their indifference to positional rearrangements in a sequence. However, these approaches may be sensitive to k-mer length and the distance between samples.Results: In this paper, we analyzed the sensitivity of an AF approach based on k-mer frequencies to these challenges using cosine and Euclidean distance metrics for both assembled genomes and unassembled sequencing reads. Quantification of the sensitivity of this AF approach for phylogeny reconstruction to branch length and k-mer length provides a better understanding of the necessary parameter ranges for accurate phylogeny reconstruction. Our results show that a frequency-based AF approach can result in accurate phylogeny reconstruction when using whole genomes, but not stochastically sequenced reads, so long as longer k-mers are used. Conclusions: In this study, we have shown an AF approach for phylogeny reconstruction is robust in analyzing assembled genome data for a range of numbers of substitutions using longer k-mers. Using simulated reads randomly selected from the genome by the Illumina sequencer had a detrimental effect on phylogeny estimation. Additionally, filtering out infrequent k-mers improved the computational efficiency of the method while preserving the accuracy of the results thus suggesting the feasibility of using only a subset of data to improve computational efficiency in cases where large sets of genome-scale data are analyzed.