Flanker: a tool for comparative genomics of gene flanking regions

Abstract

Analysing the flanking sequences surrounding genes of interest is often highly relevant to understanding the role of mobile genetic elements (MGEs) in horizontal gene transfer, particular for antimicrobial resistance genes. Here, we present Flanker, a Python package which performs alignment-free clustering of gene flanking sequences in a consistent format, allowing investigation of MGEs without prior knowledge of their structure. Flanker clusters flanking sequences based on Mash distances, allowing for easy comparison of similarity and the extent of this similarity across sequences. Additionally, Flanker can be flexibly parameterised to finetune outputs by characterising upstream and downstream regions separately and investigating variable lengths of flanking sequence. We apply Flanker to two recent datasets describing plasmid-associated carriage of important carbapenemase genes (blaOXA-48 and blaKPC-2/3) and show that it successfully identifies distinct clusters of flanking sequences (flank patterns), including both known and previously uncharacterised structural variants. We demonstrate that flank patterns are linked to geographical regions and carbapenem phenotypes, suggesting they may be useful as epidemiological markers. Flanker is freely available under an MIT license at https://github.com/wtmatlock/flanker.

The mobilome associated with Gram-negative bloodstream infections: A large-scale observational hybrid sequencing based study

“…AMR content/year/species was examined using a permanova perfomed in the R package vegan(27). Gene flanking regions were analysed using Flanker (v1.0, –w 0 –wstop 5000 –wstep 100)(28). The Reder package(29) was used to cluster a weighted graph created from a matrix in which distances were determined to be the greatest distance from the gene (in both upstream and downstream directions) in pairs of isolates which were in the same Flanker cluster; this analysis was repeated in an all vs all fashion for all isolates.…”

Enterobacteralesplasmid sharing amongst human bloodstream infections, livestock, wastewater, and waterway niches in Oxfordshire, UK

“…Plasmid networks have previously been constructed by full sequence alignments 87 , annotated genes 88 , and alignment-free Mash distances 43, 89, 90 . We chose to use the Jaccard index of entire plasmid 21 -mer distributions to capture coding sequences, their immediate contexts 91, 92 , and intergenic regions 93, 94 , all of which have known importance to bacterial evolution. Network thresholding to some extent depends subjectively on the dataset, with trade-offs between successfully revealing the underlying structure of plasmid relationships without excessively separating relatives.…”

Genomic investigation of multi-species and multi-variant blaNDM outbreak reveals key role of horizontal IncN and IncX3 plasmid transfer

“…MOB-typer v1.4.9 was used to determine plasmid replicons present and identify plasmid groups according to its clustering algorithm [13]. We compared individual plasmids (progressiveMauve v2.4.0.r4736 [14]) and assessed NDM flanking regions (Flanker v0.1.5 [15]).…”