Publications (9) View all
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Article: Meta-analysis of pulsed-field gel electrophoresis fingerprints based on a constructed salmonella database.
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ABSTRACT: A database was constructed consisting of 45,923 Salmonella pulsed-field gel electrophoresis (PFGE) patterns. The patterns, randomly selected from all submissions to CDC PulseNet during 2005 to 2010, included the 20 most frequent serotypes and 12 less frequent serotypes. Meta-analysis was applied to all of the PFGE patterns in the database. In the range of 20 to 1100 kb, serotype Enteritidis averaged the fewest bands at 12 bands and Paratyphi A the most with 19, with most serotypes in the 13-15 range among the 32 serptypes. The 10 most frequent bands for each of the 32 serotypes were sorted and distinguished, and the results were in concordance with those from distance matrix and two-way hierarchical cluster analyses of the patterns in the database. The hierarchical cluster analysis divided the 32 serotypes into three major groups according to dissimilarity measures, and revealed for the first time the similarities among the PFGE patterns of serotype Saintpaul to serotypes Typhimurium, Typhimurium var. 5-, and I 4,[5],12:i:-; of serotype Hadar to serotype Infantis; and of serotype Muenchen to serotype Newport. The results of the meta-analysis indicated that the pattern similarities/dissimilarities determined the serotype discrimination of PFGE method, and that the possible PFGE markers may have utility for serotype identification. The presence of distinct, serotype specific patterns may provide useful information to aid in the distribution of serotypes in the population and potentially reduce the need for laborious analyses, such as traditional serotyping.PLoS ONE 01/2013; 8(3):e59224. · 4.09 Impact Factor -
Article: Prediction system for rapid identification of Salmonella serotypes based on pulsed-field gel electrophoresis fingerprints.
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ABSTRACT: A classification model is presented for rapid identification of Salmonella serotypes based on pulsed-field gel electrophoresis (PFGE) fingerprints. The classification model was developed using random forest and support vector machine algorithms and was then applied to a database of 45,923 PFGE patterns, randomly selected from all submissions to CDC PulseNet from 2005 to 2010. The patterns selected included the top 20 most frequent serotypes and 12 less frequent serotypes from various sources. The prediction accuracies for the 32 serotypes ranged from 68.8% to 99.9%, with an overall accuracy of 96.0% for the random forest classification, and ranged from 67.8% to 100.0%, with an overall accuracy of 96.1% for the support vector machine classification. The prediction system improves reliability and accuracy and provides a new tool for early and fast screening and source tracking of outbreak isolates. It is especially useful to get serotype information before the conventional methods are done. Additionally, this system also works well for isolates that are serotyped as "unknown" by conventional methods, and it is useful for a laboratory where standard serotyping is not available.Journal of clinical microbiology 02/2012; 50(5):1524-32. · 4.16 Impact Factor -
Article: Microarray analysis of virulence gene profiles in Salmonella serovars from food/food animal environment.
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ABSTRACT: Rapid, accurate and inexpensive analysis of the disease-causing potential of foodborne pathogens is an important consideration in food safety and biodefense, particularly in developing countries. The objective of this study is to demonstrate the use of a robust and inexpensive microarray platform to assay the virulence gene profiles in Salmonella from food and/or the food animal environment, and then use ArrayTrack™ for data analysis. The spotted array consisted of 69 selected Salmonella-specific virulence gene probes (65bp each). These probes were printed on poly-L-lysine-coated slides. Genomic DNA was digested with Sau3AI, labeled with Cy3 dye, hybridized to the gene probes, and the images were captured and analyzed by GenePix 4000B and ArrayTrack™, a free software developed by Food and Drug Administration (FDA) researchers. Nearly 58% of the virulence-associated genes tested were present in all Salmonella strains tested. In general, genes belonging to inv, pip, prg, sic, sip, spa or ttr families were detected in more than 90% of the isolates, while the iacP, avrA, invH, rhuM, sirA, sopB, sopE or sugR genes were detected in 40 to 80% of the isolates. The gene variability was independent of the Salmonella serotype. This hybridization array presents an accurate and cost-effective method for evaluating the disease-causing potential of Salmonella in outbreak investigations by targeting a selective set of Salmonella-associated virulence genes.The Journal of Infection in Developing Countries 01/2011; 5(2):94-105. · 1.19 Impact Factor -
Article: Evaluation of pulsed-field gel electrophoresis profiles for identification of Salmonella serotypes.
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ABSTRACT: Pulsed-field gel electrophoresis (PFGE) is a standard typing method for isolates from Salmonella outbreaks and epidemiological investigations. Eight hundred sixty-six Salmonella enterica isolates from eight serotypes, including Heidelberg (n = 323), Javiana (n = 200), Typhimurium (n = 163), Newport (n = 93), Enteritidis (n = 45), Dublin (n = 25), Pullorum (n = 9), and Choleraesuis (n = 8), were subjected to PFGE, and their profiles were analyzed by random forest classification and compared to conventional hierarchical cluster analysis to determine potential predictive relationships between PFGE banding patterns and particular serotypes. Cluster analysis displayed only the underlying similarities and relationships of the isolates from the eight serotypes. However, for serotype prediction of a nonserotyped Salmonella isolate from its PFGE pattern, random forest classification provided better accuracy than conventional cluster analysis. Discriminatory DNA band class markers were identified for distinguishing Salmonella serotype Heidelberg, Javiana, Typhimurium, and Newport isolates.Journal of clinical microbiology 09/2010; 48(9):3122-6. · 4.16 Impact Factor -
Article: An FDA bioinformatics tool for microbial genomics research on molecular characterization of bacterial foodborne pathogens using microarrays.
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ABSTRACT: Advances in microbial genomics and bioinformatics are offering greater insights into the emergence and spread of foodborne pathogens in outbreak scenarios. The Food and Drug Administration (FDA) has developed a genomics tool, ArrayTrack™, which provides extensive functionalities to manage, analyze, and interpret genomic data for mammalian species. ArrayTrack™ has been widely adopted by the research community and used for pharmacogenomics data review in the FDA's Voluntary Genomics Data Submission program. ArrayTrack™ has been extended to manage and analyze genomics data from bacterial pathogens of human, animal, and food origin. It was populated with bioinformatics data from public databases such as NCBI, Swiss-Prot, KEGG Pathway, and Gene Ontology to facilitate pathogen detection and characterization. ArrayTrack™'s data processing and visualization tools were enhanced with analysis capabilities designed specifically for microbial genomics including flag-based hierarchical clustering analysis (HCA), flag concordance heat maps, and mixed scatter plots. These specific functionalities were evaluated on data generated from a custom Affymetrix array (FDA-ECSG) previously developed within the FDA. The FDA-ECSG array represents 32 complete genomes of Escherichia coli and Shigella. The new functions were also used to analyze microarray data focusing on antimicrobial resistance genes from Salmonella isolates in a poultry production environment using a universal antimicrobial resistance microarray developed by the United States Department of Agriculture (USDA). The application of ArrayTrack™ to different microarray platforms demonstrates its utility in microbial genomics research, and thus will improve the capabilities of the FDA to rapidly identify foodborne bacteria and their genetic traits (e.g., antimicrobial resistance, virulence, etc.) during outbreak investigations. ArrayTrack™ is free to use and available to public, private, and academic researchers at http://www.fda.gov/ArrayTrack.BMC Bioinformatics 01/2010; 11 Suppl 6:S4. · 2.75 Impact Factor
