The Gap Procedure: for the identification of phylogenetic clusters in HIV-1 sequence data

Abstract

BackgroundIn the context of infectious disease, sequence clustering can be used to provide important insights into the dynamics of transmission. Cluster analysis is usually performed using a phylogenetic approach whereby clusters are assigned on the basis of sufficiently small genetic distances and high bootstrap support (or posterior probabilities). The computational burden involved in this phylogenetic threshold approach is a major drawback, especially when a large number of sequences are being considered. In addition, this method requires a skilled user to specify the appropriate threshold values which may vary widely depending on the application.ResultsThis paper presents the Gap Procedure, a distance-based clustering algorithm for the classification of DNA sequences sampled from individuals infected with the human immunodeficiency virus type 1 (HIV-1). Our heuristic algorithm bypasses the need for phylogenetic reconstruction, thereby supporting the quick analysis of large genetic data sets. Moreover, this fully automated procedure relies on data-driven gaps in sorted pairwise distances to infer clusters, thus no user-specified threshold values are required. The clustering results obtained by the Gap Procedure on both real and simulated data, closely agree with those found using the threshold approach, while only requiring a fraction of the time to complete the analysis.ConclusionsApart from the dramatic gains in computational time, the Gap Procedure is highly effective in finding distinct groups of genetically similar sequences and obviates the need for subjective user-specified values. The clusters of genetically similar sequences returned by this procedure can be used to detect patterns in HIV-1 transmission and thereby aid in the prevention, treatment and containment of the disease.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0791-x) contains supplementary material, which is available to authorized users.

Impacts and shortcomings of genetic clustering methods for infectious disease outbreaks

“…Gap Procedure : This program partitions sequences based on the largest gaps between adjacent pairwise genetic distances in a sorted vector for the i th sequence: $\max \left\{{\delta }_{i,j}\right\}$ where the range of j is truncated to omit the n largest gaps as outliers (Vrbik et al 2015). Each simulated alignment was used as the input matrix for the GapProcedure function in R, which was computed using its default implementation of the Kimura 2-parameter model (“aK80”) and an outlier adjustment value of 0.9.…”

“…Under this criterion, one assumes that individuals within a cluster are related through one or more recent transmission events, such that there has been limited time for their respective virus populations to diverge in sequence from their common ancestors. More sophisticated clustering algorithms that operate on pairwise genetic distances have since been proposed by Prosperi et al (2010) and Vrbik et al (2015, Gap Procedure; see below).…”

“…Balfe et al 1990; Yerly et al 2001; Hué et al 2004). Furthermore, software implementations for several of these methods have since been released into the public domain (Prosperi et al 2011; Ragonnet-Cronin et al 2013; Vrbik et al 2015). However, these methods have seldom been validated on simulated data where actual clusters are known (but see Villandre et al 2016), and few studies have evaluated different clustering methods on the same empirical datasets.…”

Comparative Evaluation of Subtyping Tools for Surveillance of Newly Emerging HIV-1 Strains

“…Practically, HIV-1 subtyping can be performed through several approaches, among which automated tools are commonly used for clinical purposes (23)(24)(25), while molecular phylogeny (Mphy) is commonly used for epidemiological surveillance. To date, Mphy is the gold standard for both epidemiological surveillance and clinical practice (23).…”

Biased phylodynamic inferences from analysing clusters of viral sequences

“…Grouping sequences into phylogenetic clusters has previously proven useful for sifting through these large datasets, and this approach has been used to correlate transmission with contact rates, social network structures, risk behaviours and presence of co-infections with other viruses in a number of HIV studies, including those from the United Kingdom, Switzerland, Canada, the Netherlands and South America [1][2][3][4][5]. However, the definition of a phylogenetic cluster has not so far been standardised, despite there being numerous approaches and software implementations to identify them [6][7][8][9][10].…”