scholarly journals Application of Whole-Genome Sequencing Data for O-Specific Antigen Analysis andIn SilicoSerotyping of Pseudomonas aeruginosa Isolates

2016 ◽  
Vol 54 (7) ◽  
pp. 1782-1788 ◽  
Author(s):  
Sandra Wingaard Thrane ◽  
Véronique L. Taylor ◽  
Ole Lund ◽  
Joseph S. Lam ◽  
Lars Jelsbak
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Specific Antigen ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Chronic Infections ◽  
Content Type

Accurate typing methods are required for efficient infection control. The emergence of whole-genome sequencing (WGS) technologies has enabled the development of genome-based methods applicable for routine typing and surveillance of bacterial pathogens. In this study, we developed thePseudomonas aeruginosaserotyper (PAst) program, which enabledin silicoserotyping ofP. aeruginosaisolates using WGS data. PAst has been made publically available as a web service and aptly facilitates high-throughput serotyping analysis. The program overcomes critical issues such as the loss ofin vitrotypeability often associated withP. aeruginosaisolates from chronic infections and quickly determines the serogroup of an isolate based on the sequence of the O-specific antigen (OSA) gene cluster. Here, PAst analysis of 1,649 genomes resulted in successful serogroup assignments in 99.27% of the cases. This frequency is rarely achievable by conventional serotyping methods. The limited number of nontypeable isolates found using PAst was the result of either a complete absence of OSA genes in the genomes or the artifact of genomic misassembly. With PAst,P. aeruginosaserotype data can be obtained from WGS information alone. PAst is a highly efficient alternative to conventional serotyping methods in relation to outbreak surveillance of serotype O12 and other high-risk clones, while maintaining backward compatibility to historical serotype data.

scholarly journals Recommendations To Address the Difficulties Encountered When Determining Linezolid Resistance from Whole-Genome Sequencing Data

2018 ◽  
Vol 62 (8) ◽  
Author(s):  
Alicia G. Beukers ◽  
Henrik Hasman ◽  
Kristin Hegstad ◽  
Sebastiaan J. van Hal
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
23S Rrna ◽  
Whole Genome Sequencing Data ◽  
Rrna Gene ◽  
Whole Genome ◽  
Sequencing Data ◽  
Content Type ◽  
Numbering System ◽  
Linezolid Resistance

ABSTRACT Mutations associated with linezolid resistance within the V domain of 23S rRNA are annotated using an Escherichia coli numbering system. The 23S rRNA gene varies in length, nucleotide sequence, and copy number among bacterial species. Consequently, this numbering system is not intuitive and can lead to confusion when mutation sites are being located using whole-genome sequencing data. Using the mutation G2576T as an example, we demonstrate the difficulties associated with using the E. coli numbering system.

scholarly journals Short-Read Whole-Genome Sequencing for Laboratory-Based Surveillance of Bordetella pertussis

2017 ◽  
Vol 55 (5) ◽  
pp. 1446-1453 ◽  
Author(s):  
Alex Marchand-Austin ◽  
Raymond S. W. Tsang ◽  
Jennifer L. Guthrie ◽  
Jennifer H. Ma ◽  
Gillian H. Lim ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Bordetella Pertussis ◽  
Vaccine Effectiveness ◽  
Vaccine Antigen ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Short Read ◽  
Content Type

ABSTRACTBordetella pertussisis a Gram-negative bacterium that causes respiratory infections in humans. Ongoing molecular surveillance ofB. pertussisacellular vaccine (aP) antigens is critical for understanding the interaction between evolutionary pressures, disease pathogenesis, and vaccine effectiveness. Methods currently used to characterize aP components are relatively labor-intensive and low throughput. To address this challenge, we sought to derive aP antigen genotypes from minimally processed short-read whole-genome sequencing data generated from 40 clinicalB. pertussisisolates and analyzed using the SRST2 bioinformatic package. SRST2 was able to identify aP antigen genotypes for all antigens with the exception of pertactin, possibly due to low read coverage in GC-rich low-complexity regions of variation. Two main genotypes were observed in addition to a singular third genotype that contained an 84-bp deletion that was identified by SRST2 despite the issues in allele calling. This method has the potential to generate large pools ofB. pertussismolecular data that can be linked to clinical and epidemiological information to facilitate research of vaccine effectiveness and disease severity in the context of emerging vaccine antigen-deficient strains.

scholarly journals Whole-Genome Sequencing Data for Serotyping Escherichia coli—It's Time for a Change!

2015 ◽  
Vol 53 (8) ◽  
pp. 2402-2403 ◽  
Author(s):  
Claire Jenkins
Keyword(s):  
Escherichia Coli ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Content Type ◽  
Link Type ◽  
Article Type ◽  
User Friendly

The accessibility of whole-genome sequencing (WGS) presents the opportunity for national reference laboratories to provide a state-of-the-art public health surveillance service. The replacement of traditional serology-based typing ofEscherichia coliby WGS is supported by user-friendly, freely available data analysis Web tools. Anarticle in this issueof theJournal of Clinical Microbiology(K. G. Joensen, A. M. M. Tetzschner, A. Iguchi, F. M. Aarestrup, and F. Scheutz, J Clin Microbiol, 53:2410–2426, 2015,http://dx.doi.org/10.1128/JCM.00008-15) describes SerotypeFinder, an essential guide to serotypingE. coliin the 21st century.

scholarly journals Predicting Phenotypic Polymyxin Resistance in Klebsiella pneumoniae through Machine Learning Analysis of Genomic Data

mSystems ◽  
2020 ◽  
Vol 5 (3) ◽  
Author(s):  
Nenad Macesic ◽  
Oliver J. Bear Don’t Walk ◽  
Itsik Pe’er ◽  
Nicholas P. Tatonetti ◽  
Anton Y. Peleg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Klebsiella Pneumoniae ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Genomic Data ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Pr Genes ◽  
Content Type

ABSTRACT Polymyxins are used as treatments of last resort for Gram-negative bacterial infections. Their increased use has led to concerns about emerging polymyxin resistance (PR). Phenotypic polymyxin susceptibility testing is resource intensive and difficult to perform accurately. The complex polygenic nature of PR and our incomplete understanding of its genetic basis make it difficult to predict PR using detection of resistance determinants. We therefore applied machine learning (ML) to whole-genome sequencing data from >600 Klebsiella pneumoniae clonal group 258 (CG258) genomes to predict phenotypic PR. Using a reference-based representation of genomic data with ML outperformed a rule-based approach that detected variants in known PR genes (area under receiver-operator curve [AUROC], 0.894 versus 0.791, P = 0.006). We noted modest increases in performance by using a bacterial genome-wide association study to filter relevant genomic features and by integrating clinical data in the form of prior polymyxin exposure. Conversely, reference-free representation of genomic data as k-mers was associated with decreased performance (AUROC, 0.692 versus 0.894, P = 0.015). When ML models were interpreted to extract genomic features, six of seven known PR genes were correctly identified by models without prior programming and several genes involved in stress responses and maintenance of the cell membrane were identified as potential novel determinants of PR. These findings are a proof of concept that whole-genome sequencing data can accurately predict PR in K. pneumoniae CG258 and may be applicable to other forms of complex antimicrobial resistance. IMPORTANCE Polymyxins are last-resort antibiotics used to treat highly resistant Gram-negative bacteria. There are increasing reports of polymyxin resistance emerging, raising concerns of a postantibiotic era. Polymyxin resistance is therefore a significant public health threat, but current phenotypic methods for detection are difficult and time-consuming to perform. There have been increasing efforts to use whole-genome sequencing for detection of antibiotic resistance, but this has been difficult to apply to polymyxin resistance because of its complex polygenic nature. The significance of our research is that we successfully applied machine learning methods to predict polymyxin resistance in Klebsiella pneumoniae clonal group 258, a common health care-associated and multidrug-resistant pathogen. Our findings highlight that machine learning can be successfully applied even in complex forms of antibiotic resistance and represent a significant contribution to the literature that could be used to predict resistance in other bacteria and to other antibiotics.

scholarly journals Development and evaluation of an outbreak surveillance system integrating whole genome sequencing data for non-typhoidal Salmonella in London and South East of England, 2016-17

2021 ◽  
pp. 1-26
Author(s):  
Karthik Paranthaman ◽  
Piers Mook ◽  
Daniele Curtis ◽  
Edward-Wynne Evans ◽  
Emma Crawley-Boevey ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Surveillance System ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data

scholarly journals Whole genome sequencing reveals high differentiation, low levels of genetic diversity and short runs of homozygosity among Swedish wels catfish

Heredity ◽  
2021 ◽  
Author(s):  
Axel Jensen ◽  
Mette Lillie ◽  
Kristofer Bergström ◽  
Per Larsson ◽  
Jacob Höglund
Keyword(s):  
Genetic Diversity ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Whole Genome Sequencing Data ◽  
Peripheral Populations ◽  
Whole Genome ◽  
Runs Of Homozygosity ◽  
Sequencing Data ◽  
Isolated Populations ◽  
Native Populations

AbstractThe use of genetic markers in the context of conservation is largely being outcompeted by whole-genome data. Comparative studies between the two are sparse, and the knowledge about potential effects of this methodology shift is limited. Here, we used whole-genome sequencing data to assess the genetic status of peripheral populations of the wels catfish (Silurus glanis), and discuss the results in light of a recent microsatellite study of the same populations. The Swedish populations of the wels catfish have suffered from severe declines during the last centuries and persists in only a few isolated water systems. Fragmented populations generally are at greater risk of extinction, for example due to loss of genetic diversity, and may thus require conservation actions. We sequenced individuals from the three remaining native populations (Båven, Emån, and Möckeln) and one reintroduced population of admixed origin (Helge å), and found that genetic diversity was highest in Emån but low overall, with strong differentiation among the populations. No signature of recent inbreeding was found, but a considerable number of short runs of homozygosity were present in all populations, likely linked to historically small population sizes and bottleneck events. Genetic substructure within any of the native populations was at best weak. Individuals from the admixed population Helge å shared most genetic ancestry with the Båven population (72%). Our results are largely in agreement with the microsatellite study, and stresses the need to protect these isolated populations at the northern edge of the distribution of the species.

scholarly journals Norgal: extraction and de novo assembly of mitochondrial DNA from whole-genome sequencing data

2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Kosai Al-Nakeeb ◽  
Thomas Nordahl Petersen ◽  
Thomas Sicheritz-Pontén
Keyword(s):  
Mitochondrial Dna ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
De Novo Assembly ◽  
De Novo ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data
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