Evaluation of Whole Genome Sequencing for Outbreak Detection of Salmonella enterica
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{"title"=>"Evaluation of whole genome sequencing for outbreak detection of salmonella enterica", "type"=>"journal", "authors"=>[{"first_name"=>"Pimlapas", "last_name"=>"Leekitcharoenphon", "scopus_author_id"=>"36617619400"}, {"first_name"=>"Eva M.", "last_name"=>"Nielsen", "scopus_author_id"=>"35478560500"}, {"first_name"=>"Rolf S.", "last_name"=>"Kaas", "scopus_author_id"=>"51763770700"}, {"first_name"=>"Ole", "last_name"=>"Lund", "scopus_author_id"=>"7103226268"}, {"first_name"=>"Frank M.", "last_name"=>"Aarestrup", "scopus_author_id"=>"7005978857"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84895096959", "sgr"=>"84895096959", "pui"=>"372510249", "isbn"=>"1932-6203", "pmid"=>"24505344", "doi"=>"10.1371/journal.pone.0087991"}, "id"=>"7633e6fd-df0c-3df2-b537-40b51f8915b8", "abstract"=>"Salmonella enterica is a common cause of minor and large food borne outbreaks. To achieve successful and nearly 'real-time' monitoring and identification of outbreaks, reliable sub-typing is essential. Whole genome sequencing (WGS) shows great promises for using as a routine epidemiological typing tool. Here we evaluate WGS for typing of S. Typhimurium including different approaches for analyzing and comparing the data. A collection of 34 S. Typhimurium isolates was sequenced. This consisted of 18 isolates from six outbreaks and 16 epidemiologically unrelated background strains. In addition, 8 S. Enteritidis and 5 S. Derby were also sequenced and used for comparison. A number of different bioinformatics approaches were applied on the data; including pan-genome tree, k-mer tree, nucleotide difference tree and SNP tree. The outcome of each approach was evaluated in relation to the association of the isolates to specific outbreaks. The pan-genome tree clustered 65% of the S. Typhimurium isolates according to the pre-defined epidemiology, the k-mer tree 88%, the nucleotide difference tree 100% and the SNP tree 100% of the strains within S. Typhimurium. The resulting outcome of the four phylogenetic analyses were also compared to PFGE revealing that WGS typing achieved the greater performance than the traditional method. In conclusion, for S. Typhimurium, SNP analysis and nucleotide difference approach of WGS data seem to be the superior methods for epidemiological typing compared to other phylogenetic analytic approaches that may be used on WGS. These approaches were also superior to the more classical typing method, PFGE. Our study also indicates that WGS alone is insufficient to determine whether strains are related or un-related to outbreaks. This still requires the combination of epidemiological data and whole genome sequencing results.", "link"=>"http://www.mendeley.com/research/evaluation-whole-genome-sequencing-outbreak-detection-salmonella-enterica-6", "reader_count"=>186, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>2, "Librarian"=>1, "Researcher"=>34, "Student > Doctoral Student"=>8, "Student > Ph. D. Student"=>54, "Student > Postgraduate"=>3, "Student > Master"=>41, "Other"=>13, "Student > Bachelor"=>25, "Lecturer"=>2, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>2, "Librarian"=>1, "Researcher"=>34, "Student > Doctoral Student"=>8, "Student > Ph. D. Student"=>54, "Student > Postgraduate"=>3, "Student > Master"=>41, "Other"=>13, "Student > Bachelor"=>25, "Lecturer"=>2, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Agricultural and Biological Sciences"=>98, "Veterinary Science and Veterinary Medicine"=>3, "Chemistry"=>1, "Computer Science"=>7, "Economics, Econometrics and Finance"=>1, "Engineering"=>2, "Environmental Science"=>2, "Biochemistry, Genetics and Molecular Biology"=>34, "Mathematics"=>3, "Medicine and Dentistry"=>19, "Social Sciences"=>2, "Immunology and Microbiology"=>10}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>19}, "Social Sciences"=>{"Social Sciences"=>2}, "Mathematics"=>{"Mathematics"=>3}, "Unspecified"=>{"Unspecified"=>4}, "Environmental Science"=>{"Environmental Science"=>2}, "Engineering"=>{"Engineering"=>2}, "Chemistry"=>{"Chemistry"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>10}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>98}, "Computer Science"=>{"Computer Science"=>7}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>34}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>3}}, "reader_count_by_country"=>{"New Zealand"=>2, "Canada"=>2, "Sweden"=>1, "Belgium"=>1, "United States"=>4, "Ireland"=>1, "Denmark"=>3, "United Kingdom"=>1, "Australia"=>1, "Germany"=>2, "India"=>1, "Spain"=>1}, "group_count"=>13}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1374733"], "description"=>"<p>This evaluation was conducted on the set of 34 <i>S</i>. Typhimurium.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Comparative genomics", "Genome sequencing", "Sequence analysis", "Evolutionary biology", "microbiology", "Bacterial pathogens", "Salmonella", "Microbial mutation", "Population biology", "epidemiology", "Infectious disease epidemiology", "Clinical research design", "Infectious diseases", "Bacterial diseases", "concordance", "k-mer"], "article_id"=>924992, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Pimlapas Leekitcharoenphon", "Eva M. Nielsen", "Rolf S. Kaas", "Ole Lund", "Frank M. Aarestrup"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087991.g003", "stats"=>{"downloads"=>2, "page_views"=>38, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Percentage_of_concordance_of_k_mer_tree_on_various_size_of_k_/924992", "title"=>"Percentage of concordance of k-mer tree on various size of k.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-04 02:54:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1374736"], "description"=>"<p>Black bars represent number of SNPs at each core gene. Red and green small circles are core genes in the form of DNA and protein sequences respectively. The seven black dots represent house-keeping genes for MLST analysis of <i>Salmonella</i>.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Comparative genomics", "Genome sequencing", "Sequence analysis", "Evolutionary biology", "microbiology", "Bacterial pathogens", "Salmonella", "Microbial mutation", "Population biology", "epidemiology", "Infectious disease epidemiology", "Clinical research design", "Infectious diseases", "Bacterial diseases", "snps"], "article_id"=>924995, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Pimlapas Leekitcharoenphon", "Eva M. Nielsen", "Rolf S. Kaas", "Ole Lund", "Frank M. Aarestrup"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087991.g004", "stats"=>{"downloads"=>5, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_SNPs_across_Salmonella_core_genes_/924995", "title"=>"Distribution of SNPs across <i>Salmonella</i> core genes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-04 02:54:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1374739"], "description"=>"<p>Green shaded bars show the minimum and maximum number of SNP difference between isolates within outbreaks and red shaded bars represent the number of SNP difference between outbreak-related isolates and background isolates.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Comparative genomics", "Genome sequencing", "Sequence analysis", "Evolutionary biology", "microbiology", "Bacterial pathogens", "Salmonella", "Microbial mutation", "Population biology", "epidemiology", "Infectious disease epidemiology", "Clinical research design", "Infectious diseases", "Bacterial diseases", "snp"], "article_id"=>924998, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Pimlapas Leekitcharoenphon", "Eva M. Nielsen", "Rolf S. Kaas", "Ole Lund", "Frank M. Aarestrup"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087991.g005", "stats"=>{"downloads"=>4, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Minimum_and_maximum_number_of_SNP_difference_/924998", "title"=>"Minimum and maximum number of SNP difference.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-04 02:54:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1374741"], "description"=>"<p>This is the average number of SNP difference between strains within outbreaks and between outbreak-related strains and background strains from the four published dataset.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Comparative genomics", "Genome sequencing", "Sequence analysis", "Evolutionary biology", "microbiology", "Bacterial pathogens", "Salmonella", "Microbial mutation", "Population biology", "epidemiology", "Infectious disease epidemiology", "Clinical research design", "Infectious diseases", "Bacterial diseases", "pairwise", "snps"], "article_id"=>925000, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Pimlapas Leekitcharoenphon", "Eva M. Nielsen", "Rolf S. Kaas", "Ole Lund", "Frank M. Aarestrup"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087991.g006", "stats"=>{"downloads"=>1, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_pairwise_SNPs_distance_/925000", "title"=>"The pairwise SNPs distance.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-04 02:54:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1374743"], "description"=>"<p>Evaluation results.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Comparative genomics", "Genome sequencing", "Sequence analysis", "Evolutionary biology", "microbiology", "Bacterial pathogens", "Salmonella", "Microbial mutation", "Population biology", "epidemiology", "Infectious disease epidemiology", "Clinical research design", "Infectious diseases", "Bacterial diseases"], "article_id"=>925002, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Pimlapas Leekitcharoenphon", "Eva M. Nielsen", "Rolf S. Kaas", "Ole Lund", "Frank M. Aarestrup"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087991.t002", "stats"=>{"downloads"=>5, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Evaluation_results_/925002", "title"=>"Evaluation results.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-02-04 02:54:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1374745"], "description"=>"<p>Epidemiological information for the 47 <i>Salmonella</i> genomes used in this study (source: human).</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Comparative genomics", "Genome sequencing", "Sequence analysis", "Evolutionary biology", "microbiology", "Bacterial pathogens", "Salmonella", "Microbial mutation", "Population biology", "epidemiology", "Infectious disease epidemiology", "Clinical research design", "Infectious diseases", "Bacterial diseases", "47", "genomes"], "article_id"=>925004, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Pimlapas Leekitcharoenphon", "Eva M. Nielsen", "Rolf S. Kaas", "Ole Lund", "Frank M. Aarestrup"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087991.t001", "stats"=>{"downloads"=>6, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Epidemiological_information_for_the_47_Salmonella_genomes_used_in_this_study_source_human_/925004", "title"=>"Epidemiological information for the 47 <i>Salmonella</i> genomes used in this study (source: human).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-02-04 02:54:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1374748", "https://ndownloader.figshare.com/files/1374749", "https://ndownloader.figshare.com/files/1374750", "https://ndownloader.figshare.com/files/1374751", "https://ndownloader.figshare.com/files/1374752"], "description"=>"<div><p><i>Salmonella enterica</i> is a common cause of minor and large food borne outbreaks. To achieve successful and nearly ‘real-time’ monitoring and identification of outbreaks, reliable sub-typing is essential. Whole genome sequencing (WGS) shows great promises for using as a routine epidemiological typing tool. Here we evaluate WGS for typing of <i>S.</i> Typhimurium including different approaches for analyzing and comparing the data. A collection of 34 <i>S.</i> Typhimurium isolates was sequenced. This consisted of 18 isolates from six outbreaks and 16 epidemiologically unrelated background strains. In addition, 8 <i>S</i>. Enteritidis and 5 <i>S</i>. Derby were also sequenced and used for comparison. A number of different bioinformatics approaches were applied on the data; including pan-genome tree, k-mer tree, nucleotide difference tree and SNP tree. The outcome of each approach was evaluated in relation to the association of the isolates to specific outbreaks. The pan-genome tree clustered 65% of the <i>S</i>. Typhimurium isolates according to the pre-defined epidemiology, the k-mer tree 88%, the nucleotide difference tree 100% and the SNP tree 100% of the strains within <i>S</i>. Typhimurium. The resulting outcome of the four phylogenetic analyses were also compared to PFGE reveling that WGS typing achieved the greater performance than the traditional method. In conclusion, for <i>S</i>. Typhimurium, SNP analysis and nucleotide difference approach of WGS data seem to be the superior methods for epidemiological typing compared to other phylogenetic analytic approaches that may be used on WGS. These approaches were also superior to the more classical typing method, PFGE. Our study also indicates that WGS alone is insufficient to determine whether strains are related or un-related to outbreaks. This still requires the combination of epidemiological data and whole genome sequencing results.</p></div>", "links"=>[], "tags"=>["Computational biology", "genomics", "Comparative genomics", "Genome sequencing", "Sequence analysis", "Evolutionary biology", "microbiology", "Bacterial pathogens", "Salmonella", "Microbial mutation", "Population biology", "epidemiology", "Infectious disease epidemiology", "Clinical research design", "Infectious diseases", "Bacterial diseases", "genome", "sequencing", "outbreak", "detection"], "article_id"=>925007, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Pimlapas Leekitcharoenphon", "Eva M. Nielsen", "Rolf S. Kaas", "Ole Lund", "Frank M. Aarestrup"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0087991.s001", "https://dx.doi.org/10.1371/journal.pone.0087991.s002", "https://dx.doi.org/10.1371/journal.pone.0087991.s003", "https://dx.doi.org/10.1371/journal.pone.0087991.s004", "https://dx.doi.org/10.1371/journal.pone.0087991.s005"], "stats"=>{"downloads"=>18, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Evaluation_of_Whole_Genome_Sequencing_for_Outbreak_Detection_of_Salmonella_enterica_/925007", "title"=>"Evaluation of Whole Genome Sequencing for Outbreak Detection of <i>Salmonella enterica</i>", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-02-04 02:54:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1374728"], "description"=>"<p>(A) pan-genome tree, (B) K-mer tree, (C) nucleotide difference tree and (D) SNP tree. The tested set consists of outbreak-related strains displayed with color label and non-related outbreak strains shown without coloring. The outbreak strains were labeled according to the six different outbreak sources.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Comparative genomics", "Genome sequencing", "Sequence analysis", "Evolutionary biology", "microbiology", "Bacterial pathogens", "Salmonella", "Microbial mutation", "Population biology", "epidemiology", "Infectious disease epidemiology", "Clinical research design", "Infectious diseases", "Bacterial diseases", "typing", "34"], "article_id"=>924987, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Pimlapas Leekitcharoenphon", "Eva M. Nielsen", "Rolf S. Kaas", "Ole Lund", "Frank M. Aarestrup"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087991.g001", "stats"=>{"downloads"=>1, "page_views"=>25, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_WGS_typing_results_for_the_set_of_34_genomes_/924987", "title"=>"WGS typing results for the set of 34 genomes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-04 02:54:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1374732"], "description"=>"<p>(A) pan-genome tree, (B) K-mer tree, (C) nucleotide difference tree and (D) SNP tree. The labeled color was displayed the same as <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0087991#pone-0087991-g001\" target=\"_blank\">Figure 1</a>.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Comparative genomics", "Genome sequencing", "Sequence analysis", "Evolutionary biology", "microbiology", "Bacterial pathogens", "Salmonella", "Microbial mutation", "Population biology", "epidemiology", "Infectious disease epidemiology", "Clinical research design", "Infectious diseases", "Bacterial diseases", "typing", "47"], "article_id"=>924991, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Pimlapas Leekitcharoenphon", "Eva M. Nielsen", "Rolf S. Kaas", "Ole Lund", "Frank M. Aarestrup"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087991.g002", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_WGS_typing_results_for_the_set_of_47_genomes_/924991", "title"=>"WGS typing results for the set of 47 genomes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-04 02:54:25"}

PMC Usage Stats | Further Information

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Relative Metric

{"start_date"=>"2014-01-01T00:00:00Z", "end_date"=>"2014-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[291]}, {"subject_area"=>"/Biology and life sciences/Evolutionary biology", "average_usage"=>[333]}, {"subject_area"=>"/Biology and life sciences/Organisms", "average_usage"=>[310]}, {"subject_area"=>"/Medicine and health sciences/Infectious diseases", "average_usage"=>[319]}]}
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