Antimicrobial resistance and genomic diversity of Campylobacter jejuni isolates from broiler caeca and neck skin samples collected at key stages during processing.

Objectives Identification of antimicrobial resistance genes is important for understanding the underlying mechanisms and the epidemiology of antimicrobial resistance. As the costs of whole-genome sequencing (WGS) continue to decline, it becomes increasingly available in routine diagnostic laboratories and is anticipated to substitute traditional methods for resistance gene identification. Thus, the current challenge is to extract the relevant information from the large amount of generated data. Methods We developed a web-based method, ResFinder that uses BLAST for identification of acquired antimicrobial resistance genes in whole-genome data. As input, the method can use both pre-assembled, complete or partial genomes, and short sequence reads from four different sequencing platforms. The method was evaluated on 1862 GenBank files containing 1411 different resistance genes, as well as on 23 de- novo-sequenced isolates. Results When testing the 1862 GenBank files, the method identified the resistance genes with an ID = 100% (100% identity) to the genes in ResFinder. Agreement between in silico predictions and phenotypic testing was found when the method was further tested on 23 isolates of five different bacterial species, with available phenotypes. Furthermore, ResFinder was evaluated on WGS chromosomes and plasmids of 30 isolates. Seven of these isolates were annotated to have antimicrobial resistance, and in all cases, annotations were compatible with the ResFinder results. Conclusions A web server providing a convenient way of identifying acquired antimicrobial resistance genes in completely sequenced isolates was created. ResFinder can be accessed at www.genomicepidemiology.org. ResFinder will continuously be updated as new resistance genes are identified.

Abstract The virulence factor database (VFDB, http://www.mgc.ac.cn/VFs/) is devoted to providing the scientific community with a comprehensive warehouse and online platform for deciphering bacterial pathogenesis. The various combinations, organizations and expressions of virulence factors (VFs) are responsible for the diverse clinical symptoms of pathogen infections. Currently, whole-genome sequencing is widely used to decode potential novel or variant pathogens both in emergent outbreaks and in routine clinical practice. However, the efficient characterization of pathogenomic compositions remains a challenge for microbiologists or physicians with limited bioinformatics skills. Therefore, we introduced to VFDB an integrated and automatic pipeline, VFanalyzer, to systematically identify known/potential VFs in complete/draft bacterial genomes. VFanalyzer first constructs orthologous groups within the query genome and preanalyzed reference genomes from VFDB to avoid potential false positives due to paralogs. Then, it conducts iterative and exhaustive sequence similarity searches among the hierarchical prebuilt datasets of VFDB to accurately identify potential untypical/strain-specific VFs. Finally, via a context-based data refinement process for VFs encoded by gene clusters, VFanalyzer can achieve relatively high specificity and sensitivity without manual curation. In addition, a thoroughly optimized interactive web interface is introduced to present VFanalyzer reports in comparative pathogenomic style for easy online analysis.

Accurate strain identification is essential for anyone working with bacteria. For many species, multilocus sequence typing (MLST) is considered the "gold standard" of typing, but it is traditionally performed in an expensive and time-consuming manner. As the costs of whole-genome sequencing (WGS) continue to decline, it becomes increasingly available to scientists and routine diagnostic laboratories. Currently, the cost is below that of traditional MLST. The new challenges will be how to extract the relevant information from the large amount of data so as to allow for comparison over time and between laboratories. Ideally, this information should also allow for comparison to historical data. We developed a Web-based method for MLST of 66 bacterial species based on WGS data. As input, the method uses short sequence reads from four sequencing platforms or preassembled genomes. Updates from the MLST databases are downloaded monthly, and the best-matching MLST alleles of the specified MLST scheme are found using a BLAST-based ranking method. The sequence type is then determined by the combination of alleles identified. The method was tested on preassembled genomes from 336 isolates covering 56 MLST schemes, on short sequence reads from 387 isolates covering 10 schemes, and on a small test set of short sequence reads from 29 isolates for which the sequence type had been determined by traditional methods. The method presented here enables investigators to determine the sequence types of their isolates on the basis of WGS data. This method is publicly available at www.cbs.dtu.dk/services/MLST.