Phylogenetic Analysis of HIV-1 Genomes Based on the Position-Weighted K-mers Method

Abstract

HIV-1 viruses, which are predominant in the family of HIV viruses, have strong pathogenicity and infectivity. They can evolve into many different variants in a very short time. In this study, we propose a new and effective alignment-free method for the phylogenetic analysis of HIV-1 viruses using complete genome sequences. Our method combines the position distribution information and the counts of the k-mers together. We also propose a metric to determine the optimal k value. We name our method the Position-Weighted k-mers (PWkmer) method. Validation and comparison with the Robinson–Foulds distance method and the modified bootstrap method on a benchmark dataset show that our method is reliable for the phylogenetic analysis of HIV-1 viruses. PWkmer can resolve within-group variations for different known subtypes of Group M of HIV-1 viruses. This method is simple and computationally fast for whole genome phylogenetic analysis.

Genetic source completeness of HIV-1 circulating recombinant forms (CRFs) predicted by multi-label learning

“…The feature space merges the frequency and position information of k-mers to capture sequence characteristics. The features underlining the frequency characteristics of k-mers are derived by DLTree (Wu et al, 2017), and the feature information on the positions of k-mers are obtained by PWKMER (Ma et al, 2020). In the process of acquiring relevant features, so-called standard k-mers are determined from 44 HIV-1 reference sequences to narrow down the feature space.…”

“…Hence we can convert a sequence into a vector 1 × 4 k with the components q k (s), as derived by the DLTree method. The position information of nucleotide bases is significant in genetics as shown by (Ding et al, 2013;Ma et al, 2020), where phylogenetic analysis were conducted using the information from the positions of kmers. These features are also used here together with the k-mer frequency features to describe HIV-1 sequences for subtype prediction.…”

“…We extract two kinds of sequence features to represent every HIV-1 genome. Specifically, the genetic information of k-mers in the sequences are extracted using DLTree (Wu et al, 2017) and the position information of k-mers in the sequences are extracted using PWKMER (Ma et al, 2020). We select the most relevant features by assessing scores of the strings from a set of standard HIV-1 sequences (Thomas et al, 2005) calculated by DLTree under various k settings.…”

“…We select the most relevant features by assessing scores of the strings from a set of standard HIV-1 sequences (Thomas et al, 2005) calculated by DLTree under various k settings. Important genetic features and the sequence position-related features (Ma et al, 2020) are merged into a feature vector to describe the sequences for multi-label learning. In addition, we compute the pairwise distances of these sequences to form a distance matrix, and apply the classical Multi-Dimensional Scaling method to plot Molecular Distance Maps (Kari et al, 2014) for better understanding the sequence clustering behavior of HIV-1 genomes.…”

An Information-Entropy Position-Weighted K-Mer Relative Measure for Whole Genome Phylogeny Reconstruction

“…Tang et al (2014) presented the average relative distance of normalized k-mers to improve the method of Ding et al (2013). Ma et al (2020) proposed the PWKmer method, which combines the k-mer counts and k-mer position distributions for phylogenetic analysis.…”