K-Pax2: Bayesian identification of cluster-defining amino acid positions in large sequence datasets

Abstract

The recent growth in publicly available sequence data has introduced new opportunities for studying microbial evolution and spread. Because the pace of sequence accumulation tends to exceed the pace of experimental studies of protein function and the roles of individual amino acids, statistical tools to identify meaningful patterns in protein diversity are essential. Large sequence alignments from fast-evolving micro-organisms are particularly challenging to dissect using standard tools from phylogenetics and multivariate statistics because biologically relevant functional signals are easily masked by neutral variation and noise. To meet this need, a novel computational method is introduced that is easily executed in parallel using a cluster environment and can handle thousands of sequences with minimal subjective input from the user. The usefulness of this kind of machine learning is demonstrated by applying it to nearly 5000 haemagglutinin sequences of influenza A/H3N2.Antigenic and 3D structural mapping of the results show that the method can recover the major jumps in antigenic phenotype that occurred between 1968 and 2013 and identify specific amino acids associated with these changes. The method is expected to provide a useful tool to uncover patterns of protein evolution.

Combined Analysis of Variation in Core, Accessory and Regulatory Genome Regions Provides a Super-Resolution View into the Evolution of Bacterial Populations

“…The remaining 8,679 genes were extracted from the pan-genome to create an accessory genome matrix for all 228 genomes. The genomes were then clustered based on their accessory gene content using the Bayesian clustering analysis tool K-Pax2 [ 22 ], resulting in 17 distinct clusters of isolates ( Fig 2 ). Each of these clusters show an association with the type of CTX-M gene carried in agreement with recent data analyzing solely plasmid sequences [ 23 ].…”

“…A pan-genome of the ST131 data set was constructed using LS-BSR [ 21 ], and a matrix of accessory gene presence/absence for each genome constructed using the filter_BSR_variome.py tool. The resulting accessory genome matrix was used to identify clusters of isolates based on their accessory gene content via Bayesian clustering using Kpax2 [ 22 ]. Five independent runs from different starting configurations under the default prior settings and upper bound values for the number of clusters in the interval 30–50 were performed.…”

Convergent Amino Acid Signatures in Polyphyletic Campylobacter jejuni Subpopulations Suggest Human Niche Tropism

“…KPAX2 is a new Bayesian method for identifying evolutionary signals in amino acid sequences that relate to differential evolution of lineages that may be either monophyletic or polyphyletic, for example, resulting from the horizontal distribution of relevant genomic elements through recombination ( Pessia et al. 2015 ).…”

Phylogeographic separation and formation of sexually discrete lineages in a global population of Yersinia pseudotuberculosis

“…KPAX2 software was used to cluster the strains on the basis of their CRISPR spacer profiles [ 22 ]. Input to the software was a binary matrix with columns representing an absence/presence variable for each of the 2969 spacers in each detected CRISPR cassette.…”