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Ryan R Wick

Publications and source records attributed to Ryan R Wick.

4 recordsLinked to original sources

Identification of Klebsiella capsule synthesis loci from whole genome data

BackgroundKlebsiella pneumoniae and close relatives are a growing cause of healthcare-associated infections for which increasing rates of multi-drug resistance are a major concern. The Klebsiella polysaccharide capsule is a major virulence determinant and epidemiological marker. However, little is known about capsule epidemiology since serological typing is not widely accessible, and many isolates are serologically non-typeable. Molecular methods for capsular typing are needed, but existing methods lack sensitivity and specificity and fail to take advantage of the information available in whole-genome sequence data, which is increasingly being generated for surveillance and investigation of Klebsiella.\n\nMethodsWe investigated the diversity of capsule synthesis loci (K loci) among a large, diverse collection of 2503 genome sequences of K. pneumoniae and closely related species. We incorporated analyses of both full-length K locus DNA sequences and clustered protein coding sequences to identify, annotate and compare K locus structures, and we propose a novel method for identifying K loci based on full locus information extracted from whole genome sequences.\n\nResultsA total of 134 distinct K loci were identified, including 31 novel types. Comparative analysis of K locus gene content detected 508 unique protein coding gene clusters that appear to reassort via homologous recombination, generating novel K locus types. Extensive nucleotide diversity was detected among the wzi and wzc genes, both within and between K loci, indicating that current typing schemes based on these genes are inadequate. As a solution, we introduce Kaptive, a novel software tool that automates the process of identifying K loci from large sets of Klebsiella genomes based on full locus information.\n\nConclusionsThis work highlights the extensive diversity of Klebsiella K loci and the proteins that they encode. We propose a standardised K locus nomenclature for Klebsiella, present a curated reference database of all known K loci, and introduce a tool for identifying K loci from genome data (https://github.com/katholt/Kaptive). These developments constitute important new resources for the Klebsiella community for use in genomic surveillance and epidemiology.

Genomics

Inducible colistin resistance via a disrupted plasmid-borne mcr-1 gene in a 2008 Vietnamese Shigella sonnei isolate

The mcr-1 gene, which confers resistance against the last-resort antimicrobial colistin, was recently discovered in Enterobacteriaceae circulating in China. Through genome sequencing we identified a plasmid-associated inactive form of mcr-1 in a 2008 Vietnamese isolate of Shigella sonnei. The plasmid was conjugated into E. coli and mcr-1 was activated upon exposure to colistin, suggesting the gene has been circulating in human-restricted pathogens for some time but carries a selective fitness cost.

Microbiology

Bandage: interactive visualisation of de novo genome assemblies

SummaryWhile de novo assembly graphs contain assembled contigs (nodes), the connections between those contigs (edges) are difficult for users to access. Bandage (a Bioinformatics Application for Navigating De novo Assembly Graphs Easily) is a tool for visualising assembly graphs with connections. Users can zoom in to specific areas of the graph and interact with it by moving nodes, adding labels, changing colours and extracting sequences. BLAST searches can be performed within the Bandage GUI and the hits are displayed as highlights in the graph. By displaying connections between contigs, Bandage presents new possibilities for analysing de novo assemblies that are not possible through investigation of contigs alone.\n\nAvailability and implementationSource code and binaries are freely available at https://github.com/rrwick/Bandage. Bandage is implemented in C++ and supported on Linux, OS X and Windows.\n\nContactrrwick@gmail.com\n\nSupplementary informationA full feature list and screenshots are available at Bioinformatics online and http://rrwick.github.io/Bandage.

Bioinformatics

ISMapper: Identifying insertion sequences in bacterial genomes from short read sequence data

BackgroundInsertion sequences (IS) are small transposable elements, commonly found in bacterial genomes. Identifying the location of IS in bacterial genomes can be useful for a variety of purposes including epidemiological tracking and predicting antibiotic resistance. However IS are commonly present in multiple copies in a single genome, which complicates genome assembly and the identification of IS insertion sites. Here we present ISMapper, a mapping-based tool for identification of the site and orientation of IS insertions in bacterial genomes, direct from paired-end short read data.\n\nResultsISMapper was validated using three types of short read data: (i) simulated reads from a variety of species, (ii) Illumina reads from 5 isolates for which finished genome sequences were available for comparison, and (iii) Illumina reads from 7 Acinetobacter baumannii isolates for which predicted IS locations were tested using PCR. A total of 20 genomes, including 13 species and 32 distinct IS, were used for validation. ISMapper correctly identified 96% of known IS insertions in the analysis of simulated reads, and 98% in real Illumina reads. Subsampling of real Illumina reads to lower depths indicated ISMapper was reliable for average genome-wide read depths >20x. All ISAba1 insertions identified by ISMapper in the A. baumannii genomes were confirmed by PCR. In each A. baumannii genome, ISMapper successfully identified an IS insertion upstream of the ampC beta-lactamase that could explain phenotypic resistance to third-generation cephalosporins. The utility of ISMapper was further demonstrated by profiling genome-wide IS6110 insertions in 138 publicly available Mycobacterium tuberculosis genomes, revealing lineage-speific inserction and multi inserction hotspot.\n\nConclusionsISMapper provides a rapid and robust method for identifying IS insertion sites direct from short read data, with a high degree of accuracy demonstrated across a wide range of bacteria.

Bioinformatics