Trycycler: consensus long-read assemblies for bacterial genomes

While long-read sequencing allows for the complete assembly of bacterial genomes, long-read assemblies contain a variety of errors. Here, we present Trycycler, a tool which produces a consensus assembly from multiple input assemblies of the same genome. Benchmarking showed that Trycycler assemblies contained fewer errors than assemblies constructed with a single tool. Post-assembly polishing further reduced errors and Trycycler+polishing assemblies were the most accurate genomes in our study. As Trycycler requires manual intervention, its output is not deterministic. However, we demonstrated that multiple users converge on similar assemblies that are consistently more accurate than those produced by automated assembly tools

A genomic surveillance framework and genotyping tool for Klebsiella pneumoniae and its related species complex.

Klebsiella pneumoniae is a leading cause of antimicrobial-resistant (AMR) healthcare-associated infections, neonatal sepsis and community-acquired liver abscess, and is associated with chronic intestinal diseases. Its diversity and complex population structure pose challenges for analysis and interpretation of K. pneumoniae genome data. Here we introduce Kleborate, a tool for analysing genomes of K. pneumoniae and its associated species complex, which consolidates interrogation of key features of proven clinical importance. Kleborate provides a framework to support genomic surveillance and epidemiology in research, clinical and public health settings. To demonstrate its utility we apply Kleborate to analyse publicly available Klebsiella genomes, including clinical isolates from a pan-European study of carbapenemase-producing Klebsiella, highlighting global trends in AMR and virulence as examples of what could be achieved by applying this genomic framework within more systematic genomic surveillance efforts. We also demonstrate the application of Kleborate to detect and type K. pneumoniae from gut metagenomes

Small IncQ1 and Col-Like Plasmids Harboring bla KPC-2 and Non-Tn4401 Elements (NTEKPC-IId) in High-Risk Lineages of Klebsiella pneumoniae CG258.

A retrospective genomic study led to the identification of two carbapenem-resistant K. pneumoniae isolates (KPN535 and KPC45) carrying bla KPC-2 genes on non-conjugative plasmids.…

PIPS: Pathogenicity Island Prediction Software

The adaptability of pathogenic bacteria to hosts is influenced by the genomic plasticity of the bacteria, which can be increased by such mechanisms as horizontal gene transfer. Pathogenicity islands play a major role in this type of gene transfer because they are large, horizontally acquired regions that harbor clusters of virulence genes that mediate the adhesion, colonization, invasion, immune system evasion, and toxigenic properties of the acceptor organism. Currently, pathogenicity islands are mainly identified in silico based on various characteristic features: (1) deviations in codon usage, G+C content or dinucleotide frequency and (2) insertion sequences and/or tRNA genetic flanking regions together with transposase coding genes. Several computational techniques for identifying pathogenicity islands exist. However, most of these techniques are only directed at the detection of horizontally transferred genes and/or the absence of certain genomic regions of the pathogenic bacterium in closely related non-pathogenic species. Here, we present a novel software suite designed for the prediction of pathogenicity islands (pathogenicity island prediction software, or PIPS). In contrast to other existing tools, our approach is capable of utilizing multiple features for pathogenicity island detection in an integrative manner. We show that PIPS provides better accuracy than other available software packages. As an example, we used PIPS to study the veterinary pathogen Corynebacterium pseudotuberculosis, in which we identified seven putative pathogenicity islands

Evidence for Reductive Genome Evolution and Lateral Acquisition of Virulence Functions in Two Corynebacterium pseudotuberculosis Strains

Ruiz JC, D'Afonseca V, Silva A, et al. Evidence for Reductive Genome Evolution and Lateral Acquisition of Virulence Functions in Two Corynebacterium pseudotuberculosis Strains. PLoS ONE. 2011;6(4): e18551.Background: Corynebacterium pseudotuberculosis, a Gram-positive, facultative intracellular pathogen, is the etiologic agent of the disease known as caseous lymphadenitis (CL). CL mainly affects small ruminants, such as goats and sheep; it also causes infections in humans, though rarely. This species is distributed worldwide, but it has the most serious economic impact in Oceania, Africa and South America. Although C. pseudotuberculosis causes major health and productivity problems for livestock, little is known about the molecular basis of its pathogenicity. Methodology and Findings: We characterized two C. pseudotuberculosis genomes (Cp1002, isolated from goats; and CpC231, isolated from sheep). Analysis of the predicted genomes showed high similarity in genomic architecture, gene content and genetic order. When C. pseudotuberculosis was compared with other Corynebacterium species, it became evident that this pathogenic species has lost numerous genes, resulting in one of the smallest genomes in the genus. Other differences that could be part of the adaptation to pathogenicity include a lower GC content, of about 52%, and a reduced gene repertoire. The C. pseudotuberculosis genome also includes seven putative pathogenicity islands, which contain several classical virulence factors, including genes for fimbrial subunits, adhesion factors, iron uptake and secreted toxins. Additionally, all of the virulence factors in the islands have characteristics that indicate horizontal transfer. Conclusions: These particular genome characteristics of C. pseudotuberculosis, as well as its acquired virulence factors in pathogenicity islands, provide evidence of its lifestyle and of the pathogenicity pathways used by this pathogen in the infection process. All genomes cited in this study are available in the NCBI Genbank database (http://www.ncbi.nlm.nih.gov/genbank/) under accession numbers CP001809 and CP001829

Complete Genome Sequence of Linezolid-Susceptible Staphylococcus haemolyticus Sh29/312/L2, a Clonal Derivative of a Linezolid-Resistant Clinical Strain

We report the whole-genome sequence (WGS) of an in vitro susceptible derivative revertant mutant from a bloodstream isolate involved in a nosocomial outbreak in Brazil. The WGS comprises 2.5 Mb with 2,500 protein-coding sequences, 16rRNA genes, and 60 tRNA genes

Frequencies of PAI features within the PICPs and in the full genomes of <i>C. pseudotuberculosis</i> strains 1002 and C231.

<p>Y-axis: frequency in percentage; X-axis: PICPs and genomes of <i>C. pseudotuberculosis</i> strains 1002 and C231. The frequencies of the features in each PICP and in the whole genomes of the two strains are represented in the following colors: blue for codon usage deviation; red for GC content deviation; green for virulence factors; and purple for hypothetical proteins.</p

Percentage of PAI features along the genome and the pathogenicity islands of <i>C. pseudotuberculosis</i> and <i>C. diphtheriae</i>.

<p>Percentage of PAI features along the genome and the pathogenicity islands of <i>C. pseudotuberculosis</i> and <i>C. diphtheriae</i>.</p

PICP3 and PICD3 (top and bottom, respectively) in the <i>C. pseudotuberculosis</i> and <i>C. diphtheriae</i> genomes.

<p>Cp1002 and <i>C. diphtheriae</i> NCTC 13129 are shown at the top and bottom, respectively. Regions of similarity between the two genomes are marked in pink. Regions of similarity between two PAIs are marked in yellow, showing the presence of PICD3 in <i>C. pseudotuberculosis</i> with an insertion. Image generated by ACT (the Artemis Comparison Tool).</p

Flowchart presenting each PAI analysis step performed by PIPS.

<p>The procedure is divided into the following steps: (A) data treatment; (B) automatic analyses; and (C) manual analyses.</p