Presentation from the ECDC expert consultation on Whole Genome Sequencing organised by the European Centre of Disease Prevention and Control - Stockholm, 19 November 2015
Next Generation Sequencing for Identification and Subtyping of Foodborne Pat...Nathan Olson
"Next Generation Sequencing for Identification and Subtyping of Foodborne Pathogens" presentation at the Standards for Pathogen Identification via NGS (SPIN) workshop hosted by the National Institute for Standards and Technology October 2014 by Rebecca Lindsey, PhD from Enteric Diseases Laboratory Branch of the CDC.
Presentation from the ECDC expert consultation on Whole Genome Sequencing organised by the European Centre of Disease Prevention and Control - Stockholm, 19 November 2015
Next Generation Sequencing for Identification and Subtyping of Foodborne Pat...nist-spin
"Next Generation Sequencing for Identification and Subtyping of Foodborne Pathogens" presentation at the Standards for Pathogen Identification via NGS (SPIN) workshop hosted by National Institute for Standards and Technology October 2014 by Rebecca Lindsey, PhD from Enteric Diseases Laboratory Branch of the CDC.
This presents a number of case studies on the application on high-throughput sequencing (HTS), next generation sequencing (NGS), to biological problems ranging from human genome sequencing, identification of disease mutations, metagenomics, virus discovery, epidemic, transmission chains and viral populations. Presented at the University of Glasgow on Friday 26th June 2015.
1. Whole genome sequencing is becoming more affordable and widespread, allowing for large datasets and personalized medicine applications.
2. However, genomic data is extremely sensitive and can be used to identify individuals and their relatives, even when anonymized. Once a genome is leaked, it cannot be revoked.
3. Computer scientists are exploring techniques to protect genomic privacy, such as differential privacy and secure computation, but enabling privacy-preserving genomic research remains a challenge.
Course: Bioinformatics for Biomedical Research (2014).
Session: 2.1.3- Next Generation Sequencing. Technologies and Applications. Part III: NGS Applications II.
Statistics and Bioinformatisc Unit (UEB) & High Technology Unit (UAT) from Vall d'Hebron Research Institute (www.vhir.org), Barcelona.
Molecular biomarkers can be used for several purposes in infectious disease research and clinical practice. These include detecting pathogens, measuring antibody responses, identifying markers of virulence, resistance, and disease severity, and understanding human immune responses and genetic susceptibility. Challenges include lack of sensitivity, mobile genetic elements, and changes in RNA sequences. Whole genome sequencing allows investigation of microbial phylogeny, evolution, and virulence factors.
Next Generation Sequencing for Identification and Subtyping of Foodborne Pat...Nathan Olson
"Next Generation Sequencing for Identification and Subtyping of Foodborne Pathogens" presentation at the Standards for Pathogen Identification via NGS (SPIN) workshop hosted by the National Institute for Standards and Technology October 2014 by Rebecca Lindsey, PhD from Enteric Diseases Laboratory Branch of the CDC.
Presentation from the ECDC expert consultation on Whole Genome Sequencing organised by the European Centre of Disease Prevention and Control - Stockholm, 19 November 2015
Next Generation Sequencing for Identification and Subtyping of Foodborne Pat...nist-spin
"Next Generation Sequencing for Identification and Subtyping of Foodborne Pathogens" presentation at the Standards for Pathogen Identification via NGS (SPIN) workshop hosted by National Institute for Standards and Technology October 2014 by Rebecca Lindsey, PhD from Enteric Diseases Laboratory Branch of the CDC.
This presents a number of case studies on the application on high-throughput sequencing (HTS), next generation sequencing (NGS), to biological problems ranging from human genome sequencing, identification of disease mutations, metagenomics, virus discovery, epidemic, transmission chains and viral populations. Presented at the University of Glasgow on Friday 26th June 2015.
1. Whole genome sequencing is becoming more affordable and widespread, allowing for large datasets and personalized medicine applications.
2. However, genomic data is extremely sensitive and can be used to identify individuals and their relatives, even when anonymized. Once a genome is leaked, it cannot be revoked.
3. Computer scientists are exploring techniques to protect genomic privacy, such as differential privacy and secure computation, but enabling privacy-preserving genomic research remains a challenge.
Course: Bioinformatics for Biomedical Research (2014).
Session: 2.1.3- Next Generation Sequencing. Technologies and Applications. Part III: NGS Applications II.
Statistics and Bioinformatisc Unit (UEB) & High Technology Unit (UAT) from Vall d'Hebron Research Institute (www.vhir.org), Barcelona.
Molecular biomarkers can be used for several purposes in infectious disease research and clinical practice. These include detecting pathogens, measuring antibody responses, identifying markers of virulence, resistance, and disease severity, and understanding human immune responses and genetic susceptibility. Challenges include lack of sensitivity, mobile genetic elements, and changes in RNA sequences. Whole genome sequencing allows investigation of microbial phylogeny, evolution, and virulence factors.
"Bacterial Pathogen Genomics at NCBI" presentation at the Standards for Pathogen Identification via NGS (SPIN) workshop hosted by National Institute for Standards and Technology October 2014 by Dr. Bill Klimke.
This document describes a study that uses next-generation re-sequencing and bioinformatics to analyze presence/absence variation of accessory chromosomes across isolates of the wheat pathogen Zymoseptoria tritici. The genome of the reference isolate IPO323 contains 21 chromosomes including 8 accessory chromosomes. Low-cost next-generation sequencing of 13 novel Z. tritici isolates is performed and the reads are aligned to the IPO323 reference genome to determine if accessory chromosomes present in IPO323 are also present in the novel isolates based on read coverage. De novo assembly of reads from the novel isolates is also conducted and compared to IPO323 to identify any additional accessory chromosomes or sequences not present in IPO323. This
Supporting Genomics in the Practice of Medicine by Heidi RehmKnome_Inc
View the webinar at http://www.knome.com/webinar-supporting-genomics-practice-medicine. In this presentation, Dr. Heidi Rehm, Chief Laboratory Director of the Laboratory for Molecular Medicine at Partners Healthcare and one of the Principal Investigators on ClinGen, elucidates the challenges of genomics in medicine and outlined the path to integrating large scale sequencing into clinical practice.
The document discusses using structured phenotype data to improve the interpretation and prioritization of candidate genes from exome sequencing data, particularly for undiagnosed diseases. It outlines current challenges in candidate gene prioritization based on phenotypes alone. It then describes how ontologies can be used to semantically represent and compare phenotypes across species to leverage knowledge from model organisms. The document presents results showing that combining phenotype data with variant data using a tool called PhenIX improves the ability to correctly prioritize candidate genes from exome data compared to using variant data alone. This demonstrates the utility of structured phenotype data for computational analysis of exomes to diagnose rare diseases.
Dr. Douglas Marthaler - Use of Next Generation Sequencing for Whole Genome An...John Blue
Use of Next Generation Sequencing for Whole Genome Analysis of Pathogens - Dr. Douglas Marthaler, Veterinary Diagnostic Laboratory, College of Veterinary Medicine, University of Minnesota, from the 2016 Allen D. Leman Swine Conference, September 17-20, 2016, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2016-leman-swine-conference-material
Viral metagenomics is the study of viral genetic material sourced directly from the environment rather than from a host or natural reservoir. The goal is to ascertain the viral diversity in the environment that is often missed in studies targeting specific potential reservoirs.
Application of Whole Genome Sequencing in the infectious disease’ in vitro di...ExternalEvents
This document discusses the application of whole genome sequencing in infectious disease diagnostics. It provides examples of how genome sequencing has been used to identify bacterial species, detect antibiotic resistance genes, and study outbreaks. The document also discusses challenges around regulatory approval of genomic tests, data sharing policies, and database management. Overall, it argues that whole genome sequencing is a valuable tool but that standards must be developed to ensure high quality data.
This study demonstrates the utility of using Next Generation Sequencing (NGS) technology and DNA analysis to identify and analyze closely related insect species and populations. The researchers sequenced DNA from two mitochondrial genes and a nuclear gene from individuals of two closely related fly species, Bactrocera philippinensis and B. occipitalis. They obtained overlapping sequences from these genes that could be assembled into full gene sequences. Their goal is to ultimately sequence the entire genome of multiple individuals to better characterize populations and species through comparative genomic analysis. DNA-based methods provide advantages over traditional taxonomy by requiring less material and being consistent across life stages.
Dr. Ben Hause - Pathogen Discovery Using Metagenomic SequencingJohn Blue
Pathogen Discovery Using Metagenomic Sequencing - Dr. Ben Hause, College of Veterinary Medicine, Kansas State University, from the 2016 Allen D. Leman Swine Conference, September 17-20, 2016, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2016-leman-swine-conference-material
Dag Harmsen presented on the evolvement and challenges of cgMLST for the harmonization of bacterial genome sequencing and analysis. Key points include:
- cgMLST (core genome multilocus sequence typing) involves identifying and comparing alleles across a fixed set of core genome genes and has been applied to outbreak investigation and global pathogen nomenclature.
- Tools for cgMLST analysis have been developed and improved to work on read, draft, and complete genome levels and allow scalable, additive analysis of single genes to whole genomes.
- Standardizing a hierarchical cgMLST-based approach and developing common nomenclature poses challenges but is important for microbial genotypic surveillance across laboratories and countries.
Next generation sequencing (NGS) provides a high-throughput and cheaper alternative to DNA sequencing through massively parallel sequencing of millions of DNA fragments simultaneously. NGS can be used for target sequencing to identify disease-causing mutations, RNA sequencing to study entire transcriptomes, and has various applications in cancer research and treatment including identifying mutations that predict responses to immunotherapy. However, NGS also faces challenges like accurately sequencing regions with repeats and detecting fusion genes.
WGS in public health microbiology - MDU/VIDRL Seminar - wed 17 jun 2015Torsten Seemann
How genomics is changing the practice of public health microbiology. The role of whole genome sequencing as the "one true assay". Another powerful tool for the epidemiologist.
The Global Virome Project is a 10-year global effort to identify and characterize naturally occurring viruses with pandemic potential. It aims to build a comprehensive database of the estimated 1.6 million viral species circulating in mammals and waterfowl. This will allow researchers to develop broad-spectrum countermeasures against future zoonotic viruses and identify high-risk viruses to prevent spillover. The project will sample viruses in 108 sites across 63 countries over 10 years, prioritizing countries and species based on viral discovery rates and zoonotic risk prediction models. The goal is to capture over 85% of the global mammalian virome to transform virology and pandemic preparedness.
2016 Dal Human Genetics - Genomics in Medicine LectureDan Gaston
Genomic medicine aims to identify genetic variations that cause disease and inform treatment. While whole genome sequencing is technically possible for $1000, analysis costs remain high. Current clinical applications include diagnosing rare childhood disorders and guiding cancer treatment. Continued cost reductions and expanding biological knowledge databases will drive further innovation, though challenges around data interpretation and reporting remain. Large reference populations and functional studies are still needed to realize genomics' full potential in healthcare.
K-mers in metagenomics
K-mers play a critical role in the exploration of metagenomic data. They have been widely used to assign taxonomic attributions to the short genomic fragments characteristic of shotgun (metagenomic) sequencing. These approaches provide an assembly-free method for profiling microbial communities, and have helped elucidate the factors driving microbial community composition across biogeochemical gradients. Advances in sequencing technology are now making it cost-effective to sequence microbial communities at sufficient depths to allow for the assembly of high-quality contigs. This has made it possible to adopt k-mer based approaches to enable reliable binning of contigs originating from a single microbial population within a community. In this session, I will present both an overview of how k-mers can be used to assign taxonomic attributions to short metagenomic reads, and discuss how these approaches have advanced to a point where population genomes can be recovered en masse from even complex microbial communities.
How to transform genomic big data into valuable clinical informationJoaquin Dopazo
This document discusses how to transform genomic big data into valuable clinical information. It begins by defining genomic big data and explaining how individual genome data contains more information than the original experiment. Next, it discusses lessons learned from genome-wide association studies, including that many loci contribute to traits and there is evidence of pleiotropy. However, individual genes cannot fully explain trait heritability. The document then discusses challenges in detecting disease-related variants from exome/genome sequencing data due to the large number of variants and presence of apparently deleterious variants in healthy individuals. It suggests taking a systems approach considering interactions and multigenicity to better understand variation and disease mechanisms.
This document discusses NIST's work in developing genomic reference materials and methods to evaluate microbial genomics measurements. It describes three projects: 1) assessing genomic purity by detecting low levels of contaminants using sequencing and classification, 2) evaluating SNP calling methods using reference materials and replicates to establish confidence, and 3) developing characterized genomic reference materials for public health pathogens. The overall aim is to build an infrastructure to support genome-based characterization of microbial samples.
This document discusses the transition to personalized genomic medicine and some of the challenges involved. It describes how genomic data constitutes "big data" due to the large amount and complexity. While sequencing costs are decreasing, there are still difficulties in analyzing and managing the genomic data. Successive filtering approaches and knowledge databases are proposed to help identify disease-causing variants and link them to therapies.
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
National Program on Prevention and Control of Infection and Antimicrobial Resistance – PPCIRA / PORTUGAL
Before 2013, Portugal had a high prevalence of hospital-acquired infections and high antimicrobial consumption compared to other EU countries. Carbapenem and quinolone use was among the highest. Through the PPCIRA program established in 2013, Portugal implemented initiatives like establishing epidemiological surveillance, antimicrobial stewardship programs, and a standard precautions campaign. These efforts led to reductions in antimicrobial consumption in both hospital and community settings, lower rates of methicillin-resistant Staphylococcus aureus, and decreases in some hospital-acquired infections like surgical site infections and infections in neonatal and adult ICUs.
"Bacterial Pathogen Genomics at NCBI" presentation at the Standards for Pathogen Identification via NGS (SPIN) workshop hosted by National Institute for Standards and Technology October 2014 by Dr. Bill Klimke.
This document describes a study that uses next-generation re-sequencing and bioinformatics to analyze presence/absence variation of accessory chromosomes across isolates of the wheat pathogen Zymoseptoria tritici. The genome of the reference isolate IPO323 contains 21 chromosomes including 8 accessory chromosomes. Low-cost next-generation sequencing of 13 novel Z. tritici isolates is performed and the reads are aligned to the IPO323 reference genome to determine if accessory chromosomes present in IPO323 are also present in the novel isolates based on read coverage. De novo assembly of reads from the novel isolates is also conducted and compared to IPO323 to identify any additional accessory chromosomes or sequences not present in IPO323. This
Supporting Genomics in the Practice of Medicine by Heidi RehmKnome_Inc
View the webinar at http://www.knome.com/webinar-supporting-genomics-practice-medicine. In this presentation, Dr. Heidi Rehm, Chief Laboratory Director of the Laboratory for Molecular Medicine at Partners Healthcare and one of the Principal Investigators on ClinGen, elucidates the challenges of genomics in medicine and outlined the path to integrating large scale sequencing into clinical practice.
The document discusses using structured phenotype data to improve the interpretation and prioritization of candidate genes from exome sequencing data, particularly for undiagnosed diseases. It outlines current challenges in candidate gene prioritization based on phenotypes alone. It then describes how ontologies can be used to semantically represent and compare phenotypes across species to leverage knowledge from model organisms. The document presents results showing that combining phenotype data with variant data using a tool called PhenIX improves the ability to correctly prioritize candidate genes from exome data compared to using variant data alone. This demonstrates the utility of structured phenotype data for computational analysis of exomes to diagnose rare diseases.
Dr. Douglas Marthaler - Use of Next Generation Sequencing for Whole Genome An...John Blue
Use of Next Generation Sequencing for Whole Genome Analysis of Pathogens - Dr. Douglas Marthaler, Veterinary Diagnostic Laboratory, College of Veterinary Medicine, University of Minnesota, from the 2016 Allen D. Leman Swine Conference, September 17-20, 2016, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2016-leman-swine-conference-material
Viral metagenomics is the study of viral genetic material sourced directly from the environment rather than from a host or natural reservoir. The goal is to ascertain the viral diversity in the environment that is often missed in studies targeting specific potential reservoirs.
Application of Whole Genome Sequencing in the infectious disease’ in vitro di...ExternalEvents
This document discusses the application of whole genome sequencing in infectious disease diagnostics. It provides examples of how genome sequencing has been used to identify bacterial species, detect antibiotic resistance genes, and study outbreaks. The document also discusses challenges around regulatory approval of genomic tests, data sharing policies, and database management. Overall, it argues that whole genome sequencing is a valuable tool but that standards must be developed to ensure high quality data.
This study demonstrates the utility of using Next Generation Sequencing (NGS) technology and DNA analysis to identify and analyze closely related insect species and populations. The researchers sequenced DNA from two mitochondrial genes and a nuclear gene from individuals of two closely related fly species, Bactrocera philippinensis and B. occipitalis. They obtained overlapping sequences from these genes that could be assembled into full gene sequences. Their goal is to ultimately sequence the entire genome of multiple individuals to better characterize populations and species through comparative genomic analysis. DNA-based methods provide advantages over traditional taxonomy by requiring less material and being consistent across life stages.
Dr. Ben Hause - Pathogen Discovery Using Metagenomic SequencingJohn Blue
Pathogen Discovery Using Metagenomic Sequencing - Dr. Ben Hause, College of Veterinary Medicine, Kansas State University, from the 2016 Allen D. Leman Swine Conference, September 17-20, 2016, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2016-leman-swine-conference-material
Dag Harmsen presented on the evolvement and challenges of cgMLST for the harmonization of bacterial genome sequencing and analysis. Key points include:
- cgMLST (core genome multilocus sequence typing) involves identifying and comparing alleles across a fixed set of core genome genes and has been applied to outbreak investigation and global pathogen nomenclature.
- Tools for cgMLST analysis have been developed and improved to work on read, draft, and complete genome levels and allow scalable, additive analysis of single genes to whole genomes.
- Standardizing a hierarchical cgMLST-based approach and developing common nomenclature poses challenges but is important for microbial genotypic surveillance across laboratories and countries.
Next generation sequencing (NGS) provides a high-throughput and cheaper alternative to DNA sequencing through massively parallel sequencing of millions of DNA fragments simultaneously. NGS can be used for target sequencing to identify disease-causing mutations, RNA sequencing to study entire transcriptomes, and has various applications in cancer research and treatment including identifying mutations that predict responses to immunotherapy. However, NGS also faces challenges like accurately sequencing regions with repeats and detecting fusion genes.
WGS in public health microbiology - MDU/VIDRL Seminar - wed 17 jun 2015Torsten Seemann
How genomics is changing the practice of public health microbiology. The role of whole genome sequencing as the "one true assay". Another powerful tool for the epidemiologist.
The Global Virome Project is a 10-year global effort to identify and characterize naturally occurring viruses with pandemic potential. It aims to build a comprehensive database of the estimated 1.6 million viral species circulating in mammals and waterfowl. This will allow researchers to develop broad-spectrum countermeasures against future zoonotic viruses and identify high-risk viruses to prevent spillover. The project will sample viruses in 108 sites across 63 countries over 10 years, prioritizing countries and species based on viral discovery rates and zoonotic risk prediction models. The goal is to capture over 85% of the global mammalian virome to transform virology and pandemic preparedness.
2016 Dal Human Genetics - Genomics in Medicine LectureDan Gaston
Genomic medicine aims to identify genetic variations that cause disease and inform treatment. While whole genome sequencing is technically possible for $1000, analysis costs remain high. Current clinical applications include diagnosing rare childhood disorders and guiding cancer treatment. Continued cost reductions and expanding biological knowledge databases will drive further innovation, though challenges around data interpretation and reporting remain. Large reference populations and functional studies are still needed to realize genomics' full potential in healthcare.
K-mers in metagenomics
K-mers play a critical role in the exploration of metagenomic data. They have been widely used to assign taxonomic attributions to the short genomic fragments characteristic of shotgun (metagenomic) sequencing. These approaches provide an assembly-free method for profiling microbial communities, and have helped elucidate the factors driving microbial community composition across biogeochemical gradients. Advances in sequencing technology are now making it cost-effective to sequence microbial communities at sufficient depths to allow for the assembly of high-quality contigs. This has made it possible to adopt k-mer based approaches to enable reliable binning of contigs originating from a single microbial population within a community. In this session, I will present both an overview of how k-mers can be used to assign taxonomic attributions to short metagenomic reads, and discuss how these approaches have advanced to a point where population genomes can be recovered en masse from even complex microbial communities.
How to transform genomic big data into valuable clinical informationJoaquin Dopazo
This document discusses how to transform genomic big data into valuable clinical information. It begins by defining genomic big data and explaining how individual genome data contains more information than the original experiment. Next, it discusses lessons learned from genome-wide association studies, including that many loci contribute to traits and there is evidence of pleiotropy. However, individual genes cannot fully explain trait heritability. The document then discusses challenges in detecting disease-related variants from exome/genome sequencing data due to the large number of variants and presence of apparently deleterious variants in healthy individuals. It suggests taking a systems approach considering interactions and multigenicity to better understand variation and disease mechanisms.
This document discusses NIST's work in developing genomic reference materials and methods to evaluate microbial genomics measurements. It describes three projects: 1) assessing genomic purity by detecting low levels of contaminants using sequencing and classification, 2) evaluating SNP calling methods using reference materials and replicates to establish confidence, and 3) developing characterized genomic reference materials for public health pathogens. The overall aim is to build an infrastructure to support genome-based characterization of microbial samples.
This document discusses the transition to personalized genomic medicine and some of the challenges involved. It describes how genomic data constitutes "big data" due to the large amount and complexity. While sequencing costs are decreasing, there are still difficulties in analyzing and managing the genomic data. Successive filtering approaches and knowledge databases are proposed to help identify disease-causing variants and link them to therapies.
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
National Program on Prevention and Control of Infection and Antimicrobial Resistance – PPCIRA / PORTUGAL
Before 2013, Portugal had a high prevalence of hospital-acquired infections and high antimicrobial consumption compared to other EU countries. Carbapenem and quinolone use was among the highest. Through the PPCIRA program established in 2013, Portugal implemented initiatives like establishing epidemiological surveillance, antimicrobial stewardship programs, and a standard precautions campaign. These efforts led to reductions in antimicrobial consumption in both hospital and community settings, lower rates of methicillin-resistant Staphylococcus aureus, and decreases in some hospital-acquired infections like surgical site infections and infections in neonatal and adult ICUs.
Cryptococcal meningitis poses an ongoing public health burden in Africa, particularly among those with HIV/AIDS. New diagnostic tools like the lateral flow immunoassay for cryptococcal antigen detection in urine could enable earlier diagnosis and treatment. Ongoing clinical trials are evaluating shorter courses of amphotericin B combined with high-dose fluconazole as alternative treatment strategies that are more feasible and sustainable in resource-limited settings.
Carbapenem-resistant Acinetobacter baumannii poses a significant threat in healthcare settings across Europe. It can cause serious infections that are difficult to treat due to limited antibiotic options. The number of countries reporting spread and endemicity of carbapenem-resistant A. baumannii has increased in recent years. Increased detection and control efforts are needed to prevent it from becoming endemic in more European regions and healthcare facilities.
WGS data for bacterial typing
This document discusses using whole genome sequencing (WGS) data for bacterial strain typing and phylogenetic analysis. It covers:
1) Bacterial genomes consist of DNA made up of 4 nucleotides (A, C, T, G) that can be sequenced. Genes encode proteins and make up most of bacterial genomes.
2) Mutations like single nucleotide changes can be used to differentiate bacterial strains. Molecular methods like MLST, MLVA, and core genome MLST analyze categorical or continuous differences in bacterial sequences.
3) As sequencing technology advanced, it became possible to generate and analyze whole bacterial genomes, allowing highly discriminatory strain typing and reconstruction of bacterial phylogenies based on single nucleotide polymorph
Neisseria gonorrhoeae and Neisseria meningitidis are pathogenic Neisseria species. N. gonorrhoeae causes the sexually transmitted infection gonorrhea, characterized by urethritis and cervicitis. N. meningitidis most commonly causes meningitis but can also cause septicemia. Both are gram-negative diplococci that can be identified microscopically in clinical samples and cultured on selective media. Treatment involves antibiotics like penicillin, cephalosporins, or tetracyclines.
This document summarizes genomic epidemiology techniques for analyzing bacterial pathogens. It discusses using whole genome sequencing and core genome multilocus sequence typing (cgMLST) to genotype and classify bacterial strains, identify clonal groups and outbreaks, and determine antimicrobial resistance and virulence profiles. Specifically, it provides examples analyzing the emergence of antibiotic-resistant clones of Klebsiella pneumoniae and using cgMLST to improve characterization of Listeria monocytogenes outbreaks globally.
Genetic variation and its role in health pharmacologyDeepak Kumar
Genetic variation exists at different scales, from single nucleotide polymorphisms within individuals of a species to larger structural differences between species. Genetic variation arises through mutations, recombination, gene flow, genetic drift, and the interaction of these processes over time. The effective population size of a species influences how genetic variation is shaped by these evolutionary forces.
Population genomics and public health examines how population genomics, which synthesizes genomics, population biology, and evolution, can help answer public health questions about bacterial pathogens. Martin Maiden discusses how high-throughput sequencing and multi-locus sequence typing (MLST) of large pathogen populations has provided insights into the population structure, evolution, and epidemiology of bacterial pathogens like Neisseria meningitidis. MLST data has shown clonal dominance of certain lineages and evidence of recombination in pathogen populations. Analysis of population structure has implications for vaccine design and understanding disease potential.
importance of pathogenomics in plant pathologyvinay ju
The document provides an outline for a seminar on pathogenomics for diagnosis and management of plant diseases. It includes sections on pathogenomics in plant pathology, diagnostic tools using next-generation sequencing technologies, host-microbe interaction and genes involved in virulence and resistance. The outline also lists various bioinformatics databases and molecular techniques used for pathogen detection, including PCR-based methods and microarrays. It discusses several examples of pathogenicity genes and host proteins involved in plant-virus interactions.
Draft bacterial genomes are missing certain gene types compared to complete genomes, especially those associated with mobile genetic elements like transposons. A study analyzed 36 draft Listeria monocytogenes genomes and their complete versions, finding that the "replication, recombination, and repair" gene superfamily was significantly underrepresented in the drafts. Genomic islands, which often reside near contig breaks in drafts, were also more likely to be missed. However, analysis of antimicrobial resistance and virulence genes was still valuable for the species examined. More work is needed to generalize these findings to other species.
A New Generation Of Mechanism-Based Biomarkers For The ClinicJoaquin Dopazo
The document discusses moving from single gene biomarkers to more functional, modular biomarkers for disease. It argues that most diseases are caused by combinations of variants affecting functional modules rather than single genes. The document proposes analyzing genomic data like SNPs and gene expression in the context of protein interaction networks and gene ontologies to better capture disease mechanisms and identify more informative biomarkers. Examples show how this approach can prioritize genes interacting with known disease genes and find enriched functional groups associated with diseases.
This study sequenced the genomes of 11 clinical Mycobacterium abscessus isolates from 8 US patients with pulmonary infections. Core genome analysis compared these isolates to 30 globally diverse strains to investigate population structure. Longitudinally sampled isolates showed very few genetic differences, suggesting homogenous infection populations. Genome content variation between isolates was 0.3-8.3% compared to the reference strain, indicating plasticity.
This document describes the development of a multiplex PCR assay targeting the cgcA gene, which encodes a diguanylate cyclase, to differentiate between species within the genus Cronobacter. Analysis of 12 Cronobacter genomes identified 7 conserved diguanylate cyclase-encoding genes, one of which, cgcA, showed species-specific divergence that matched known phylogenetic relationships between Cronobacter species. Primers were designed for this gene and tested in a multiplex PCR assay on 305 Cronobacter isolates representing 6 species. The assay correctly identified the species of all isolates tested and did not identify any of 20 non-Cronobacter species, demonstrating high specificity and sensitivity for rapid identification of Cronobacter.
This study aimed to optimize norovirus GI genotyping primers and apply them to characterize norovirus GI diversity in clinical and environmental samples from South Africa. Five norovirus GI genotypes were found circulating between 2015-2016, with GI.4 being the most prevalent in 63.2% of samples. The primers were optimized to improve genotyping of viruses from sewage samples. National and regional strain clusters were identified, adding to understanding of norovirus genetics and transmission globally.
Towards Precision Medicine: Tute Genomics, a cloud-based application for anal...Reid Robison
Tute Genomics is cloud-based software that can rapidly analyze entire human genomes. The cost of whole genome sequencing is dropping rapidly and we are in the middle of a genomic revolution. Tute is opening a new door for personalized medicine by helping researchers & healthcare organizations analyze human genomes.
This document discusses several primary immunodeficiencies including agammaglobulinemia, common variable immunodeficiency, and selective IgA deficiency. It provides information on the epidemiology, pathophysiology, clinical manifestations, diagnosis, and treatment of each condition. Key points include that agammaglobulinemia is caused by mutations in the BTK gene that halt B cell development, common variable immunodeficiency has unknown causes in most cases, and selective IgA deficiency involves a block in differentiation of B cells into IgA secreting plasma cells. Clinical features often involve recurrent respiratory and gastrointestinal infections. Treatment focuses on antibody replacement therapy and infection prophylaxis.
This document discusses several primary immunodeficiencies including agammaglobulinemia, common variable immunodeficiency, and selective IgA deficiency. It provides information on the epidemiology, pathophysiology, clinical manifestations, diagnosis, and treatment of each condition. Key points include that agammaglobulinemia is caused by mutations in the BTK gene that halt B cell development, common variable immunodeficiency has unknown causes in most cases, and selective IgA deficiency involves a block in differentiation of B cells into IgA secreting plasma cells. Clinical features often involve recurrent respiratory and gastrointestinal infections. Treatment focuses on antibody replacement and infection prevention.
BRN Symposium 03/06/16 The gut microbiome in HIV infectionbrnmomentum
This document summarizes a study examining the relationship between the gut microbiome and HIV. It describes:
1) How HIV infection damages the gut-associated lymphoid tissue and leads to microbial translocation, inflammation, and immune activation.
2) The study's aims to characterize the gut microbiome in HIV patients with different phenotypes and risk groups, and to evaluate associations with diet, genetics, and HIV markers.
3) The study's methodology which involved collecting fecal samples from 156 HIV patients in Barcelona and analyzing them using 16S rRNA sequencing and shotgun metagenomics to characterize the microbiome composition and gene content.
This journal club discusses multiple myeloma and EBV. The document summarizes characteristics of multiple myeloma, its diagnostic workup, criteria for diagnosis, and role of infections like EBV, HCV and HIV in pathogenesis. It then summarizes a case-control study that found higher rates of EBV DNA in bone marrow of multiple myeloma patients compared to controls, as detected by PCR. It concludes there may be an association between EBV and multiple myeloma but larger studies are needed.
This document provides an overview of Hepatitis C. It begins with an introduction stating that over 71 million people worldwide are chronically infected with HCV. It then covers the virology of HCV including its structure, genome, replication cycle, genotypes/quasispecies. The epidemiology section discusses the global prevalence and incidence. Pathogenesis outlines how HCV evades the immune system to cause chronic infection. Clinical features are separated into acute hepatitis C and chronic hepatitis C. Extrahepatic manifestations associated with HCV are also summarized.
The document describes malaria immunology and epidemiology studies conducted in Papua New Guinea between 2004-2017. It involved several cohort and intervention studies with observational cohorts of approximately 500-2000 individuals. The studies aimed to understand immunity targets and mechanisms to malaria in order to rationalize vaccine development. They examined both antibody and cellular immune responses. Key findings included that γδ T cells are a major source of IFNγ response and certain NK cell receptors are associated with risk of high density infections.
Post-transplant lymphoproliferative disorder (PTLD) is a lymphoid proliferation that occurs after solid organ or stem cell transplantation due to immunosuppression. It is caused by Epstein-Barr virus (EBV) infection in B cells that is normally kept in check by cytotoxic T cells. PTLD ranges from benign early lesions to malignant monoclonal B cell proliferations and has an incidence of around 10% in solid organ transplant recipients. Diagnosis involves evaluating EBV viral load, imaging, and biopsy to detect EBV infection and lymphoid proliferation. Treatment depends on disease severity and includes reducing immunosuppression, antiviral drugs, surgery, rituximab, chemotherapy, and
Similar to Proof of concept of WGS based surveillance: meningococcal disease (20)
The document discusses the global spread of the mcr-1 gene, which confers plasmid-mediated colistin resistance in Enterobacteriaceae. This poses a substantial public health risk as it limits treatment options for multidrug-resistant infections. Options for response include improved detection of mcr-1 via laboratory methods like PCR and whole genome sequencing, enhanced surveillance programs, infection control measures in healthcare settings, antimicrobial stewardship, and reducing colistin use in animals to prevent further spread. A One Health approach combining human and veterinary medicine is needed to monitor mcr-1 in food and the environment.
Presentation from the ECDC expert consultation on Whole Genome Sequencing organised by the European Centre of Disease Prevention and Control - Stockholm, 19 November 2015
Presentation from the ECDC expert consultation on Whole Genome Sequencing organised by the European Centre of Disease Prevention and Control - Stockholm, 19 November 2015
Presentation from the ECDC expert consultation on Whole Genome Sequencing organised by the European Centre of Disease Prevention and Control - Stockholm, 19 November 2015
This document summarizes discussions from several sessions of a meeting on antimicrobial resistance and healthcare-associated infections. Key points include:
- Most countries submit antimicrobial consumption data close to the deadline, and there are specific rules for who can access and publish the data.
- It is important but challenging to compare hospital antimicrobial consumption data between countries due to differences in how data is collected. Both defined daily doses and packages are needed for comparison.
- A pilot hospital-based antimicrobial consumption survey was proposed to collect additional data starting in late 2015, but the protocol requires further review and clarification before implementation.
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
Validation studies are essential to accurately assess the sensitivity, specificity, and predictive values of point prevalence surveys (PPS) of healthcare-associated infections (HAI). Previous validation studies of PPS have shown varied results, underscoring the need for formal evaluations. Without validation, true HAI prevalence is unknown and differences between locations cannot be properly investigated. International organizations can help support national validation efforts to improve HAI surveillance.
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
This document contains forms and instructions for conducting a point prevalence survey of healthcare-associated infections and antimicrobial use in European acute care hospitals. The forms collect data at the hospital, ward, patient, and national/regional level. Hospital data includes bed numbers, staffing levels, infection control activities and organizational culture. Ward data includes bed numbers, hand hygiene infrastructure. Patient data collects infection details, antimicrobial use, and patient characteristics for those with infections or receiving antibiotics. National data provides healthcare system context. The forms standardize data collection to allow prevalence comparisons across settings.
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015
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4. Population genomics:
the gene-by-gene approach
Complete
Sequence
Annotation
Bacterial Isolate
Genome Sequence
Database
(BIGSDB)
Contigs
Gene sequences
Provenance/phenotyp
e information
Jolley, K. A. & Maiden, M. C. (2010). BIGSdb: Scalable analysis of bacterial
genome variation at the population level. BMC Bioinformatics 11, 595.
5. Data submitters:
currently >1300;
Data curators:
currently >90 MLST
schemes
Sequence
definitions
MLST, rMLST,
antigen genes, core
genome, pan-
genome
GeneA
GeneB
GeneC
GeneD
Allele1: TTTGATACTGTTGCCGAAGGTTT
Allele2: TTTGATACCGTTGCCGAAGGTTT
Allele3: TTTGATTCCGTTGCCGAAGGTTT
>750 citations
Isolate datasets
• provenance
• phenotype
• gene content
• allelic variation
• genomes
Linked to:
Population
annotation
• locus classification
• description
• biochemical
pathway
• Core + accessory
genome analysis
• Association studies
Comparative
genomics
PubMLST
1998*, 2003
Gene-by-gene
analysis using
reference genome or
defined loci
Molecular typing
Species identification
Epidemiology
Vaccine coverage/
impact
Linking genotype
to phenotype
Outbreak investigation
Population structure
>8000 unique visitors/month*http://mlst.zoo.ox.ac.uk
6. PubMLST RESTful API facilitates data exchange
• All data accessible
via JSON API
• Authenticated
(OAuth) access to
protected resources
• Data submission
available soon
http://rest.pubmlst.org
7. WGS determination, interpretation
and dissemination pipeline
Isolate growth
DNA Extraction
Sequencing
(Illumina)
de novo assembly
(VELVET)
Database deposition
(BIGSDB)
Autotagged, web
accessible
sequences
Bacterial cells
Purified DNA
Short-read sequences
Assembled contiguous
sequences
Phenotype & provenance
linkage and annotation
‘Plain language’
dataBratcher, H. B., Bennett, J. S. & Maiden, M. C. J.
(2012). Evolutionary and genomic insights into
meningococcal biology. Future Microbiology 7, 873-885.
Deposited
8. MLST
(7 loci)
16S rRNA
sequences
(1 locus)
Ribosomal MLST
(53 loci)
Strain
Lineage/
Clonal Complex
Species
Family
Order
Class
Phylum
Genus
Whole genome
MLST
(>500 loci)
- Core genome
MLST
- Accessory
genome MLST
Hierarchical genome analysis
Clone
Meroclone
Maiden Maiden, M. C., van
Rensburg, M. J., Bray, J. E.,
Earle, S. G., Ford, S. A., Jolley,
K. A. & McCarthy, N. D. M.C.J.
et al. 2013. MLST revisited:
the gene-by-gene approach to
bacterial genomics. Nat Rev
Microbiol. 2013 Sep 2. doi:
10.1038/nrmicro3093.
PMCID: PMC3980634
9. Neisseria structure and
characterisation
Jolley, K. A., Brehony, C. & Maiden, M. C. (2007). Molecular typing of
meningococci: recommendations for target choice and nomenclature. FEMS
Microbiol Rev 31, 89-96.
Component Phenotypic Genotypic
Capsule Serogroup cps region
OMPS Serotype,
Subtype, etc.
porA, porB,
fetA, etc.
Housekeeping
genes
MLEE MLST
Ribosomes MALDITOF 16s rRNA,
rMLST
Neisseria meningitidis B: P1.7,16: F3-3: ST-32 (cc32)
10. Validation of WGS pipeline
• 108 diverse meningococcal isolates,
sequenced with 54bp Illumina
reads.
• Assembled with VELVET and
uploaded into BIGSDB.
• Comparison of 24 typing loci (total
of 2592 loci) previously
characterised by Sanger sequencing
in all isolates.
• There were 34 (1.3%) allelic
differences found in 20 of the de
novo assembled genomes.
• 30 discrepancies (1.15%)
attributable to Sanger sequence
errors (mislabelling, editing errors).
• 4 discrepancies (0.15%) attributable
to Velvet assembly. These were all
in the same porA allele (a repeat
sequence).
Bratcher, H. B., Corton, C., Jolley, K. A., Parkhill, J. & Maiden, M. C. (2014). A gene-by-
gene population genomics platform: de novo assembly, annotation and genealogical analysis of
108 representative Neisseria meningitidis genomes. BMC Genomics 15, 1138.
11. Genome and phenotype
• Whole genome MLST
(wgMLST).
• Autotagger – runs
regularly – tags all loci with
known alleles (>2200 in
Neisseria database.
• Each unique sequence
given new allele number.
• Loci grouped into
schemes.
• Linkage to phenotype &
other information.
Jolley, K. A. & Maiden, M. C. (2013). Automated extraction of typing information for bacterial pathogens
from whole genome sequence data: Neisseria meningitidis as an exemplar. Euro Surveill 18 (4): 20379.
12. Meningitis Research Foundation
Meningococcus Genome Library
• Charity funded.
• Open access
• All available England
and Wales (& soon
Scotland)
meningococcal isolates.
• Assembled &
annotated contiguous
sequence data.
http://www.meningitis.org/current-projects/genome
13. Isolates in the MRF Genome Library –
England and Wales
0
100
200
300
400
500
600
Z
Y
X
W/Y
W
NG
E
C
B
A
14. National Surveillance: MRF-MGL 2010-2012
Hill, D.M.C., Lucidarme, J., Gray S.J., Newbold , L.S., Ure, R., Brehony, C., Harrison,
O.B., Bray, J.E., Jolley, K.A., Bratcher H.B.,, Parkhill, J., Tang, C.M., Borrow, R., and
Maiden, M.C.J. Genomic epidemiology of age-associated meningococcal lineages in national
surveillance: an observational cohort study. Lancet Infectious diseases, DOI:
http://dx.doi.org/10.1016/S1473-3099(15)00267-4
• A total of 923 isolates from
England, Wales and Northern
Ireland.
• 899 from England and Wales:
• Scanned at >2000 loci;
• 2-313 alleles/locus;
• 219 STs, 22 clonal
complexes;
• 496 rSTs (ribosomal
sequence types);
• Most isolates (78%)
belonged to 6 clonal
complexes.
15. 0
500
1000
1500
2000
2500
3000
1975 ~ 1985 ~ 1995 ~ 1999 2000 2001 ~ 2005 2006 2007 2008 2009 2010 2011 2012
41/44 269 11 32 8 213 23 167 174 22 Other UA NT
Retrospective epidemiology
Hill, D.M.C., Lucidarme, J., Gray S.J., Newbold , L.S., Ure, R., Brehony, C., Harrison,
O.B., Bray, J.E., Jolley, K.A., Bratcher H.B.,, Parkhill, J., Tang, C.M., Borrow, R., and
Maiden, M.C.J. Genomic epidemiology of age-associated meningococcal lineages in national
surveillance: an observational cohort study. Lancet Infectious diseases, DOI:
http://dx.doi.org/10.1016/S1473-3099(15)00267-4
16. Outbreak investigation
Mulhall, RM, Brehony, C, O’Connor, L, Bennett, D, Jolley, KA, Bray, J, Maiden, MCJ,
Cunney, R. Resolution of a protracted serogroup B meningococcal outbreak in a large extended
indigenous Irish Traveller Family in the Republic of Ireland during 2010 to 2013 using non-culture
PCR, WGS and publically accessible web-based tools. In preparation.
17. High resolution international
epidemiology (W:cc11)
0
10
20
30
40
50
60
70
2005 2006 2007 2008 2009 2010 2011 2012 2013
n
year
W:cc11England and Wales2005to 2013
Current UK
UK Hajj
UK
1996 (n=3)
1997 (n=2)
1998 (n=2)
UK
1975 (n=6)
1987 (n=1)
1989 (n=1)
1990 (n=1)
UK
1996 (n=2)
1998 (n=1)
Argentina 2008-2012
Brazil 2008-2011
Current South Africa
Lucidarme, J., Hill, D.M., Bratcher, H.B., GrayS.J, du Plessis, M., Tsang, R.S.W., Vazquez, J.A., Taha, M.-K.,
Mehmet Ceyhan, Jamie Findlow J., Jolley, K.A., Maiden M.C.J., Borrow, R. (2015) Journal of Infection
0
10
20
30
40
50
60
70
80
90
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
NumberofCases
Year
N. meningitidis cases per year among inpatients in Bamako, Mali
(2002-2012)
Group A
meningococcal
cases
Group W135
meningococcal
cases
20. invasive isolate survey: proof of concept for
WGS based surveillance
Epidemiological year 2011/2012
Dominique Caugant, Holly Bratcher, Carina
Brehony, Martin Maiden, IBD-LabNet
26. Surveillance data coverage: PorA &
FetA
0
50
100
150
200
250
300
350
400
450
500
550
600
650
700
750
800
100 66.7 33.3
numberofisolates
percent loci assigned (n=3)
21 partial antigen profiles (2.6%)
216-677 contigs / genome
1-2 loci assigned / isolate
14 no PorA VR1 allele
12 no PorA VR2 allele
6 no FetA VR allele
27. Surveillance data coverage: 5 BAST
loci
0
50
100
150
200
250
300
350
400
450
500
550
600
650
100 80-90 60-70 40-50
numberofisolates
percent loci defined
Over all 597 with partial profile
(74.8%)
14 no PorA VR1 allele
12 no PorA VR2 allele
130 no NadA peptide allele*
3 no fHbp peptide allele
19 no NHBA peptide allele
44 only 2-3 loci identified (5.5%)
average 495 contigs / genome
3 no PorA VR1 allele
11 no PorA VR1, VR2 alleles
3 no fHbp/NadA peptide alleles
19 no NHBA/NadA peptide alleles
32. Scalable genomic epidemiology
Centuries+ decades years months weeks days hours
Evolution emergence epidemiology diagnosis
COLOMBIA2004
(n=37)
Y
32%
B
51%
W-135
3%
C
14%
AFRICAN
MENINGITIS BELT
2003-2004
(n=501)
Other
1,2%
A
79%
W-135
20%
AUSTRALIA 2004
(n=361)
Other
7,2%
C
20%
A
0,3%
B
68%
W-135
3,3%
Y
2,2%
WESTERN
EUROPE 2002
(n=3,982)
A
0,1%
C
29%
Other
1,0%
B
64%
W-135
3,6%
Y
2,3%
RUSSIA 2002-2004
(n=1,899)
B
32%
A
36%
C
22%
Other
10%
CHILE 2003
(n=193)
Other
5%
C
14%
B
78%
W-135
1%
Y
2%
CANADA2003*
(n=148)
W-135
7%
C
24%
B
43%
Other
1%
Y
25%
UNITED STATES 2003
(n=200)
Y
27%
C
21% B
44%
Other
6%
W-135
2%
TAIWAN 2001
(n=43)
Y
19%
A
4,7%
W-135
41%
B
33%
C
2,3%
THAILAND 2001
(n=36)
Other
2%
B
81%
W-135
17%
SAUDI ARABIA
2002
(n=21)
B
10%
W-135
76%
A
14%
BRAZIL 2004
Sao Paulostate
(n=520)
B
36%
C
58%
Other
6%
NEW ZEALAND2004
(n=252)
C
8%
Other
0,8%
B
87%
W-135
3,6%
Y
0,4%
SOUTHAFRICA2003
(n=264)
Other
1%W-135
9%
B
29%
A
34%
C
11%
Y
16%
URUGUAY 2001
(n=53)
C
11%
B
83%
Other
6%
COLOMBIA2004
(n=37)
Y
32%
B
51%
W-135
3%
C
14%
AFRICAN
MENINGITIS BELT
2003-2004
(n=501)
Other
1,2%
A
79%
W-135
20%
AUSTRALIA 2004
(n=361)
Other
7,2%
C
20%
A
0,3%
B
68%
W-135
3,3%
Y
2,2%
WESTERN
EUROPE 2002
(n=3,982)
A
0,1%
C
29%
Other
1,0%
B
64%
W-135
3,6%
Y
2,3%
RUSSIA 2002-2004
(n=1,899)
B
32%
A
36%
C
22%
Other
10%
CHILE 2003
(n=193)
Other
5%
C
14%
B
78%
W-135
1%
Y
2%
CANADA2003*
(n=148)
W-135
7%
C
24%
B
43%
Other
1%
Y
25%
UNITED STATES 2003
(n=200)
Y
27%
C
21% B
44%
Other
6%
W-135
2%
TAIWAN 2001
(n=43)
Y
19%
A
4,7%
W-135
41%
B
33%
C
2,3%
THAILAND 2001
(n=36)
Other
2%
B
81%
W-135
17%
SAUDI ARABIA
2002
(n=21)
B
10%
W-135
76%
A
14%
BRAZIL 2004
Sao Paulostate
(n=520)
B
36%
C
58%
Other
6%
NEW ZEALAND2004
(n=252)
C
8%
Other
0,8%
B
87%
W-135
3,6%
Y
0,4%
SOUTHAFRICA2003
(n=264)
Other
1%W-135
9%
B
29%
A
34%
C
11%
Y
16%
URUGUAY 2001
(n=53)
C
11%
B
83%
Other
6%
0.1
UK 1993
Case 1
Carrier 1
FAM18
USA
1983
Carrier 2
Carrier 3
Cases 3 & 6
Remote
cases 1 & 2
Carrier 4
Carrier 5
33.
34. Contigs Total length Min Max Mean StdDev N50 L50 N90 L90 N95 L95 %GC
mean 306 2,133,479 209 88,174 7,847 12,456 33 22,789 117 5,194 151 2,688 52
max 612 2,278,600 273 258,183 19,478 32,854 80 64,227 289 16,887 370 9,336 52
min 109 2,026,649 200 30,309 3,499 4,877 12 7,569 35 1,670 44 942 51
MRF 2013/2014 assembly statistics
36. MRF 2014/2015 assembly statistics
H Bratcher, C Brehony, M Maiden, D Caugant . IDB-LabNet 2015
mean 323 2,126,265 202 80,153 6,900 10,841 35 19,614 126 4,366 162 2,215
max 516 2,278,600 273 219,677 18,381 28,710 67 58,245 236 15,605 305 9,336
min 116 2,037,538 200 34,143 4,184 5,990 13 8,869 43 1,992 53 1,065
Contigs Total length Min Max Mean StdDev N50 L50 N90 L90 N95 L95
37. Age association of meningococcal
genotypes (MRF-MGL 2010-2012)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
<1 1-4 5-9 10-14 15-19 20-24 25-29 30-39 40-49 50-69 >70
Proportionofcases
Age category
Minor clonal complexes
ND
ST-174 complex
ST-461 complex
ST-162 complex
ST-22 complex
ST-23 complex/Cluster A3
ST-213 complex
ST-60 complex
ST-41/44 complex/Lineage 3
ST-269 complex
ST-32 complex/ET-5 complex
ST-11 complex/ET-37 complex
Hill, D.M.C., Lucidarme, J., Gray S.J., Newbold , L.S., Ure, R., Brehony, C., Harrison,
O.B., Bray, J.E., Jolley, K.A., Bratcher H.B.,, Parkhill, J., Tang, C.M., Borrow, R., and
Maiden, M.C.J. Genomic epidemiology of age-associated meningococcal lineages in national
surveillance: an observational cohort study. Lancet Infectious diseases, DOI:
http://dx.doi.org/10.1016/S1473-3099(15)00267-4
38. Population annotation
Harrison, O.B., Bray, J.A., Maiden, M.C., and Caugant, D.A. (2015) Genomic Analysis of the Evolution
and Global Spread of Hyper-invasive Meningococcal Lineage 5. Ebiomedicine, 2(3), 234-243
doi:10.1016/j.ebiom.2015.01.004.
39. Validation against four reference
genomes
Isolate Loci present in
draft genome
Identical loci Discrepant
loci
Incomplete
loci
Discrepant
bases in
annotated
loci
Z2491 1872/1867
(99.8%)
1801 (96.2%) 19 (1%) 51 (2.7%) 32 (0.002%)
FAM18 1905/1914
(93.2%)
1775 (93.2%) 23 (1.2%) 107 (5.6%) 24 (0.001%)
G2136* 1897/1904
(99.6%)
1757 (92.6%) 47 (2.5%) 93 (4.9%) 90 (0.005%)
H44/76* 1967/1975
(99.2%)
1821 (92.6%) 49 (2.55) 97 (4.9%) 76 (0.004%)
Draft genomes generated by VELVET assembly of Illumina reads and deposited
in BIGSDB without further curation.
Annotations compared with GENOMECOMPARATOR.
* Finished genomes primarily generated with Roche 454 technology.
40. Phenotypic serogroup by country
0
25
50
75
100
125
150
175
200
225
250
numberofisolates
No value
NG
Y
W
W/Y
X
E
C
B
A
H Bratcher, C Brehony, M Maiden, D Caugant . IDB-LabNet 2015
41. Indexing the genome: Neiss loci
gene 122540..122974
/gene="rplK"
/locus_tag="NMC0119"
/db_xref="GeneID:4676186"
CDS 122540..122974
/gene="rplK"
/locus_tag="NMC0119"
/note="binds directly to 23S ribosomal RNA"
/codon_start=1
/transl_table=11
/product="50S ribosomal protein L11"
/protein_id="YP_974250.1"
/db_xref="GI:121634005"
/db_xref="GeneID:4676186"
/translation="MAKKIIGYIKLQIPAGKANPSPPVGPA
LGQRGLNIMEFCKAFNAATQGMEPGLPIPVVITAF
ADKSFTFVMKTPPASILLKKAAGLQKGSSNPLTNK
VGKLTRAQLEEIAKTKEPDLTAADLDAAVRTIAGS
ARSMGLDVEGVV“
Database: RefSeq
Entry: NC_008767
LinkDB: NC_008767
LOCUS NC_008767 2194961 bp DNA circular CON 10-
JUN-2013
DEFINITION Neisseria meningitidis FAM18 chromosome, complete
genome.
pubMLST.org/Neisseria
Sequence definition database
“LOCUS TAG IDENTIFIER”
NMC0119 (FAM18)
NMA0146 (020-06)
NGO1855 (FA 1090)
LOCUS “ALIASES” for
‘seed
sequences
’
42. Bacterial Isolate Genome Sequence Database
(BIGSDB)
CCATCCCGTTGTCGAACAGCAGGTACGCCA
CTTCACCGCCAACCACACCGACCTTGACCAC
AAACACCGCCTCATGCTGCTCACCGGCCCC
AATATGGGCGGCAAATCCACCTACATGCGCA
GGAACCCTCAAAGCCGTTTTCCCGGAAAACC
TATCCACAGCCGAACAGCTCCGCCAAGCCA
TTTTGCCCGAACCTTCCGTCTGGCTGAAAGA
CGGCAATGTCATCAACCACGGTTTTCATCCC
GAACTGGACGAATTGCGCCGCATTCAAAACC
ATGGCGACGAATTTTTGCTGGATTTGGAAGC
CAAGGAACGCGAACGTACCGGTTTGTCCAC
ACTTAAAGTCGAGTTCAACCGCGTTCACGGC
TTTTACATTGAATTGTCCAAAACCCAAGCCG
AACAAGCACCTGCCGACTACCAACGCCGGC
AAACCCTTAAAAACGCCGAACGCTTCATCAC
GCCGGAACTGAAAGCCTTTGAAGACAAAGT
GCTGACTGCTCAAGAGCAAGCCCTCGCCTT
AGAAAAACAACTCTTTGACGGCGTATTGAAA
AACCTTCAGACGGCATTGCCGCAGCTTCAAA
AAGCCGCCAAAGCCGCCGCCGCGCTGGAC
GTGTTGTCCACATTTTCAGCCTTGGCAAAAG
AGCGGAACTTCGTCCGCCCCGAGTTTGCCG
ACAAGTCGCGCTGATTGTTT
AACCTTCAGACGGCATTGCCGCAGCTTCAAA
AAGCCGCCAAAGCCGCCGCCGCGCTGGAC
GTGTTGTCCACATTTTCAGCCTTGGCAAAAG
AGCGGAACTTCGTCCGCCCCGAGTTTGCCG
ACTATCCGGTTATCCACATCGAAAACGGCCG
CCATCCCGTTGTCGAACAGCAGGTACGCCA
CTTCACCGCCAACCACACCGACCTTGACCAC
AGGAACCCTCAAAGCCGTTTTCCCGGAAAAC
CTATCCACAGCCGAACAGCTCCGCCAAGCC
ATTTTGCCCGAACCTTCCGTCTGGCTGAAAG
ACGGCAATGTCATCAACCACGGTTTTCATCC
CGAACTGGACGAATTGCGCCGCATTCAAAAC
CATGGCGACGAATTTTTGCTGGATTTGGAAG
CCAAGGAACGCGAACGTACCGGTTTGTCCA
CACTTAAAGTCGAGTTCAACCGCGTTCACGG
CTTTTACATTGAATTGTCCAAAACCCAAGCC
GCCCCGAGTTTGCCGACTATCCGGTTATCCA
CATCGAAAACGGCCGCCATCCCGTTGTCGA
ACAGCAGGTACGCCACTTCACCGCCAACCA
CACCGACCTTGACCACAAACACCGCCTCATG
CTGCTCACCGGCCCCAATATGGGCGGCAAA
TCCACCTACATGCGCCAAGTCGCGCTGATTG
TTT
AGGAACCCTCAAAGCCGTTTTCCCGGAAAAC
CTATCCACAGCCGAACAGCTCCGCCAAGCC
ATTTTGCCCGAACCTTCCGTCTGGCTGAAAG
ACGGCAATGTCATCAACCACGGTTTTCATCC
CGAACTGGACGAATTGCGCCGCATTCAAAAC
CATGGCGACGAATTTTTGCTGGATTTGGAAG
CCAAGGAACGCGAACGTACCGGTTTGTCCA
CACTTAAAGTCGAGTTCAACCGCGTTCACGG
CTTTTACATTGAATTGTCCAAAACCCAAGCC
GAACAAGCACCTGCCGACTACCAACGCCGG
CAAACCCTTAAAAACGCCGAACGCTTCATCA
CGCCGGAACTGAAAGCCTTTGAAGACAAAGT
GCTGACTGCTCAAGAGCAAGCCCTCGCCTT
AGAAAAACAACTCTTTGACGGCGTATTGAAA
AACCTTCAGACGGCATTGCCGCAGCTTCAAA
AAGCCGCCAAAGCCGCCGCCGCGCTGGAC
GTGTTGTCCACATTTTCAGCCTTGGCAAAAG
AGCGGAACTTCGTCCGCCCCGAGTTTGCCG
ACTATCCGGTTATCCACATCGAAAACGGCCG
CCATCCCGTTGTCGAACAGCAGGTACGCCA
CTTCACCGCCAACCACACCGACCTTGACCAC
AAACACCGCCTCATGCTGCTCACCGGCCCC
AATATGGGCGGCAAATCCACCTACATGCGC
CAAGTCGCGCTGATTGTTT
abcZ
adk
aroE
fumC
gdh
pdhC
pgm
porA
porB
fetA
penA
rpoB
16S
Locus X
Locus Y
Sequence
bin
Jolley, K. A. & Maiden, M. C. (2010). BIGSdb:
Scalable analysis of bacterial genome variation at
the population level. BMC Bioinformatics 11, 595.
Locus
definitions
tables:
annotation
source Locus Allele Provenance
abcZ 2 Country UK
adk 3 Year 2013
aroE 4 serogroup B
gdh 8 Disease carrier
pdhC 4 Age 23
pgm 6 Source Swab
... etc... ... etc ...
43. Acknowledgements
Julia BennettWT
Carly Bliss
Holly BratcherWT
James BrayWT
Carina BrehonyWT
Marianne Clemence
Ali Cody
Fran Colles
Kanny DialloWTF
Sarah Earle
Suzanne Ford
Odile HarrisonWT
Sofia Hauck
Dorothea Hill
Lisa Rebbets
Melissa Jansen van
Rensburg
Keith JolleyWT
Jasna Kovac
Jenny MacLennanWT
Noel McCarthyWTF
Maddi Pearce
Samuel SheppardWTF
Helen Strain
Eleanor Watkins
Helen Wimalarathna
44. Population genomics:
the gene-by-gene approach
Complete
Sequence
Annotation
Bacterial Isolate
Genome Sequence
Database (BIGSDB)
Contigs
Gene sequences
Provenance/phenotype
information
Jolley, K. A. & Maiden, M. C. (2010). BIGSdb: Scalable analysis of bacterial genome variation at
the population level. BMC Bioinformatics 11, 595.
45. Bacterial typing requirements
1. Universal, in that they are applicable to all bacteria.
2. Natural, reflecting genealogical relationships while retaining
the capacity to describe closely related organisms with
distinct properties.
3. Understandable, so that the output and the process by
which the system has been arrived at are transparent, easily
interpreted and reproducible, and where possible the system
should be backwards compatible with previous approaches.
4. Expandable, to account for the incompleteness of our
knowledge of diversity, and flexible enough to accommodate
changes in this knowledge.
46. Bacterial typing requirements
5. Portable, because methods need to be easily carried out in
any laboratory and the data need to be freely exchanged by
the use of generic methodologies, reagents and
bioinformatics pipelines
6. Technology independent, so that the data used are
independent of the means of their collection (this means
that schemes adopted now need to retain their validity as
data improve)
7. Readily available to the entire community
47. Bacterial typing requirements
8. Scalable, so that methods are sufficiently fast and
inexpensive to be useable in real time for large or small
numbers of isolates (this scalability is especially important for
clinical applications and large-scale bacterial population
analyses)
9. Accommodate a wide range of variation so that they can
encompass both close and distant genealogical relationships
10. Broadly accepted by those who use them and open to
contributions from members of the community.
48. Bacterial typing methods
• Universal, in that they are applicable to all bacteria
• Natural, reflecting genealogical relationships while retaining the capacity to describe closely related
organisms with distinct properties
• Understandable, so that the output and the process by which the system has been arrived at are
transparent, easily interpreted and reproducible, and where possible the system should be backwards
compatible with previous approaches
• Expandable, to account for the incompleteness of our knowledge of diversity, and flexible enough to
accommodate changes in this knowledge
• Portable, because methods need to be easily carried out in any laboratory and the data need to be freely
exchanged by the use of generic methodologies, reagents and bioinformatics pipelines
• Technology independent, so that the data used are independent of the means of their collection (this
means that schemes adopted now need to retain their validity as data improve)
• Readily available to the entire community
• Scalable, so that methods are sufficiently fast and inexpensive to be useable in real time for large or small
numbers of isolates (this scalability is especially important for clinical applications and large-scale bacterial
population analyses)
• Able to accommodate a wide range of variation so that they can encompass both close and distant
genealogical relationships
• Broadly accepted by those who use them and open to contributions from members of the community.
49. cnl meningococci & other species
Claus, H., Maiden, M. C., Maag, R., Frosch, M. & Vogel, U. (2002). Many carried
meningococci lack the genes required for capsule synthesis and transport.
Microbiology 148, 1813-1819.
Harrison, O. B., Claus, H., Jiang, Y., Bennett, J. S., Bratcher, H. B., Jolley, K. A.,
Corton, C., Care, R., Poolman, J. T., Zollinger, W. D., Frasch, C. E., Stephens, D. S.,
Feavers, I., Frosch, M., Parkhill, J., Vogel, U., Quail, M. A., Bentley, S. D. & Maiden,
M. C. J. (2013). Description and Nomenclature of Neisseria meningitidis Capsule
Locus. Emerging Infectious Diseases 19, 566-573.
50. First generation genomics:
single locus typing and MLSTaroE
gdh
pgm
adk
pdhC
fumC
porA
fetAabcZ
Maiden, MCJ, Bygraves, JA, Feil, E, Morelli, G, Russell, JE, Urwin, R, Zhang, Q, Zhou, J, Zurth, K,
Caugant, DA, Feavers, IM, Achtman, M & Spratt, BG. 1998. Multilocus sequence typing: a portable
approach to the identification of clones within populations of pathogenic microorganisms. Proc Natl Acad
Sci USA 95, 3140-3145.
Maiden, MC. 2006. Multilocus Sequence Typing of Bacteria. Annu Rev Microbiol 60, 561-588.
Jolley KA, Brehony C, Maiden MC. 2007. Molecular typing of meningococci: recommendations for target
choice and nomenclature. FEMS Microbiol Rev 31, 89-96.
• Neisseria seven-locus ST
summarises 3284bp.
• That is 0.15% of the 2.18Mbp
genome.
• 11,001 STs in PubMLST
database (September 2014).
• 469-750 alleles per locus.
• Many polymorphisms per
locus.
52. GENOMECOMPARATOR: rapid comparative
genomics
Jolley, K. A., Hill, D. M., Bratcher, H. B., Harrison, O. B., Feavers, I. M., Parkhill, J. &
Maiden, M. C. (2012). Resolution of a meningococcal disease outbreak from whole genome
sequence data with rapid web-based analysis methods. J Clin Microbiol. 50(9):3046-53.
SPLITSTREE 4.0
NEIGHBORNET
53. Ribosomal multi-locus sequence
typing, rMLST
Jolley, K. A., Bliss, C. M., Bennett, J. S., Bratcher, H. B., Brehony, C. M., Colles, F. M.,
Wimalarathna, H. M., Harrison, O. B., Sheppard, S. K., Cody, A. J. & Maiden, M. C. (2012).
Ribosomal Multi-Locus Sequence Typing: universal characterisation of bacteria from
domain to strain. Microbiology 158, 1005-1015.
• Isolate characterisation from ‘domain to
strain.
• Indexes the 53 ribosomal genes.
• PubMLST.org/rMLST, provides a look-up table
available on the web.
• Ribosomal sequence types, rSTs related to
appropriate nomenclatures, October 2014:
• 99,996 genome sequences;
• 977 genera;
• 2,531 unique species ;
• rSTs defined for 6 groups, Neisseria and
Campylobacter to clonal complex level.
55. Lineage 5: 40 years of global disease
and reverse vaccinology
1,886 (95%) core loci
52 (3%) accessory
Harrison, O. B., Bray, J. E., Maiden, M. C. J. & Caugant, D. A. Genomic Analysis of the Evolution and
Global Spread of Hyper-invasive Meningococcal Lineage 5. EBioMedicine.
Harrison, O.B., Hill, D.M., Maiden, M.C.J. unpublished.
56. Variability across the lineage 5 (ST-32
complex) genome
229 loci identical
1,600 loci p-distance values below
0.002
Harrison, O.B., Bray, J.A., Maiden, M.C., and Caugant, D.A. (2015)
Genomic Analysis of the Evolution and Global Spread of Hyper-
invasive Meningococcal Lineage 5. Ebiomedicine, 2(3), 234-243
doi:10.1016/j.ebiom.2015.01.004.
57. Meningitis Research Foundation
Meningococcus Genome Library
• Charity funded.
• Open access
• All available England
and Wales (& soon
Scotland)
meningococcal isolates.
• Assembled &
annotated contiguous
sequence data.
http://www.meningitis.org/current-projects/genome
58. MRF-MGL isolates 2010-2012
• A total of 923 isolates from
England, Wales and
Northern Ireland.
• 899 from England and
Wales:
• Scanned at >1600 loci;
• 2-313 alleles/locus;
• 219 STs, 22 clonal
complexes;
• 496 rSTs (ribosomal
sequence types);
• Most isolates (78%)
belonged to 6 clonal
complexes.
ST-41/44 complex
237 isolates
ST-269 complex
171 isolatesST-11 complex, 59 isolates
ST-213 complex
75 isolates
ST-23 complex
120 isolates
ST-32 complex
42 isolates
59. 0
500
1000
1500
2000
2500
3000
1975 ~ 1985 ~ 1995 ~ 1999 2000 2001 ~ 2005 2006 2007 2008 2009 2010 2011 2012
41/44 269 11 32 8 213 23 167 174 22 Other UA NT
Meningococcal clonal complexes and
disease: England and Wales
Hill, D.M.C., Lucidarme, J., Gray S.J., Newbold , L.S., Ure, R., Brehony, C., Harrison,
O.B., Bray, J.E., Jolley, K.A., Bratcher H.B.,, Parkhill, J., Tang, C.M.,, Borrow, R., and
Maiden, M.C.J. Genomic epidemiology of age-associated meningococcal lineages in national
surveillance: an observational cohort study. Submitted.
61. MRF-MGL isolates:
genogroups by epidemiological year
0
100
200
300
400
500
600
07/2010-06/2011 07/2011-06/2012 07/2012-06/2013 07/2013-06/2014 07/2014-06/2015
Numberofisolates
Epidemiological Year
Y
X
W/Y
W
NG
E
C
B
A
64. Contiguous sequences (contigs.)
Data sources
First generation ‘Next generation’
Archival
Short-read
sequence
data
DNA
Sequence on
preferred platform
(e.g. Illumina)
Bacteria
l isolate
Complete, assembled closed
genomes with annotation, available
from public databases (e.g. IMGD)
Clinical
specimen
TGGAGCAGATCGAGGAGAGCGAGTTCGACGC
TGGAGCAGATCGAGGAGAGCGAGTTCGACGC
TGGAGCAGATCGAGGAGAGCGAGTTCGACGC
TGGAGCAGATCGAGGAGAGCGAGTTCGACGC
TGGAGCAGATCGAGGAGAGCGAGTTCGACGC
TGGAGCAGATCGAGGAGAGCGAGTTCGACGC
TGGAGCAGATCGAGGAGAGCGAGTTCGACGC
TGGAGCAGATCGAGGAGAGCGAGTTCGACGC
TGGAGCAGATCGAGGAGAGCGAGTTCGACGC
Assemble with
preferred software
(e.g. VELVET)
65. wgMLST ST-32 complex isolates
2063 CDS,
1,894 present in all
isolates
Harrison, O.B. Maiden M.C., Caugant, D.A. Unpublished,
66. Rapid automated genome
assembly
506 Isolates
Illumina Genome Analyzer GAIIx
Read Lengths: 100 Nucleotides
Average Input FASTQ Filesize:
586MB
(258 million nucleotides)
Average Number of Reads: 2.58
million
K-mer Range: 21-99
Median Final K-mer: 81
Median N50: 37,503
Average Number of Contigs: 209
Average Program Time: 22 mins 31
secs
Total Program Time: 58 hours
Filesize (MB)
ProgramTime(hh:mm:ss)
Total AutoAssembler.pl Program Time
Using 10 Threads Per Assembly
James Bray, unpublished
67. BIGSDB automated annotation
MLST definitions CCATCCCGTTGTCGAACAGCAGGTACGCCA
CTTCACCGCCAACCACACCGACCTTGACCAC
AAACACCGCCTCATGCTGCTCACCGGCCCC
AATATGGGCGGCAAATCCACCTACATGCGCA
GGAACCCTCAAAGCCGTTTTCCCGGAAAACC
TATCCACAGCCGAACAGCTCCGCCAAGCCA
TTTTGCCCGAACCTTCCGTCTGGCTGAAAGA
CGGCAATGTCATCAACCACGGTTTTCATCCC
GAACTGGACGAATTGCGCCGCATTCAAAACC
ATGGCGACGAATTTTTGCTGGATTTGGAAGC
CAAGGAACGCGAACGTACCGGTTTGTCCAC
ACTTAAAGTCGAGTTCAACCGCGTTCACGGC
TTTTACATTGAATTGTCCAAAACCCAAGCCG
AACAAGCACCTGCCGACTACCAACGCCGGC
AAACCCTTAAAAACGCCGAACGCTTCATCAC
GCCGGAACTGAAAGCCTTTGAAGACAAAGT
GCTGACTGCTCAAGAGCAAGCCCTCGCCTT
AGAAAAACAACTCTTTGACGGCGTATTGAAA
AACCTTCAGACGGCATTGCCGCAGCTTCAAA
AAGCCGCCAAAGCCGCCGCCGCGCTGGAC
GTGTTGTCCACATTTTCAGCCTTGGCAAAAG
AGCGGAACTTCGTCCGCCCCGAGTTTGCCG
ACAAGTCGCGCTGATTGTTT
AACCTTCAGACGGCATTGCCGCAGCTTCAAA
AAGCCGCCAAAGCCGCCGCCGCGCTGGAC
GTGTTGTCCACATTTTCAGCCTTGGCAAAAG
AGCGGAACTTCGTCCGCCCCGAGTTTGCCG
ACTATCCGGTTATCCACATCGAAAACGGCCG
CCATCCCGTTGTCGAACAGCAGGTACGCCA
CTTCACCGCCAACCACACCGACCTTGACCAC
AGGAACCCTCAAAGCCGTTTTCCCGGAAAAC
CTATCCACAGCCGAACAGCTCCGCCAAGCC
ATTTTGCCCGAACCTTCCGTCTGGCTGAAAG
ACGGCAATGTCATCAACCACGGTTTTCATCC
CGAACTGGACGAATTGCGCCGCATTCAAAAC
CATGGCGACGAATTTTTGCTGGATTTGGAAG
CCAAGGAACGCGAACGTACCGGTTTGTCCA
CACTTAAAGTCGAGTTCAACCGCGTTCACGG
CTTTTACATTGAATTGTCCAAAACCCAAGCC
GCCCCGAGTTTGCCGACTATCCGGTTATCCA
CATCGAAAACGGCCGCCATCCCGTTGTCGA
ACAGCAGGTACGCCACTTCACCGCCAACCA
CACCGACCTTGACCACAAACACCGCCTCATG
CTGCTCACCGGCCCCAATATGGGCGGCAAA
TCCACCTACATGCGCCAAGTCGCGCTGATTG
TTT
AGGAACCCTCAAAGCCGTTTTCCCGGAAAAC
CTATCCACAGCCGAACAGCTCCGCCAAGCC
ATTTTGCCCGAACCTTCCGTCTGGCTGAAAG
ACGGCAATGTCATCAACCACGGTTTTCATCC
CGAACTGGACGAATTGCGCCGCATTCAAAAC
CATGGCGACGAATTTTTGCTGGATTTGGAAG
CCAAGGAACGCGAACGTACCGGTTTGTCCA
CACTTAAAGTCGAGTTCAACCGCGTTCACGG
CTTTTACATTGAATTGTCCAAAACCCAAGCC
GAACAAGCACCTGCCGACTACCAACGCCGG
CAAACCCTTAAAAACGCCGAACGCTTCATCA
CGCCGGAACTGAAAGCCTTTGAAGACAAAGT
GCTGACTGCTCAAGAGCAAGCCCTCGCCTT
AGAAAAACAACTCTTTGACGGCGTATTGAAA
AACCTTCAGACGGCATTGCCGCAGCTTCAAA
AAGCCGCCAAAGCCGCCGCCGCGCTGGAC
GTGTTGTCCACATTTTCAGCCTTGGCAAAAG
AGCGGAACTTCGTCCGCCCCGAGTTTGCCG
ACTATCCGGTTATCCACATCGAAAACGGCCG
CCATCCCGTTGTCGAACAGCAGGTACGCCA
CTTCACCGCCAACCACACCGACCTTGACCAC
AAACACCGCCTCATGCTGCTCACCGGCCCC
AATATGGGCGGCAAATCCACCTACATGCGC
CAAGTCGCGCTGATTGTTT
abcZ
adk
aroE
fumC
gdh
pdhC
pgm
porA
porB
fetA
penA
rpoB
16S
Locus X
Locus Y
MLST definitions
database
External
definitions
databases
Sequence
bin
Jolley, K. A. & Maiden, M. C. (2010). BIGSdb:
Scalable analysis of bacterial genome variation at
the population level. BMC Bioinformatics 11, 595.