Common languages in genomic epidemiology: from ontologies to algorithms
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The document discusses advancements in high-throughput sequencing (HTS) genomics, emphasizing its applications in global outbreak detection and public health interventions. It outlines the integration of various algorithms for microbial typing and the importance of ontologies in enabling a standardized vocabulary for data interoperability in genomic epidemiology. Additionally, it highlights collaborative projects and resources aimed at enhancing data analysis and sharing within the field.
Common languages in genomic epidemiology: from ontologies to algorithms
- 1. JoãoAndré Carriço, Mario Ramirez Microbiology Institute and Instituto de Medicina Molecular, Faculty of Medicine, University of Lisbon [email protected] twitter: @jacarrico RAMI-NGS, Hamburg, Germany, 9-11 June 2016
- 2. Moving fromTyping into High Throughput Sequencing (HTS) Genomics : Increase in discrimination Extra information to be extracted the genome (resistance profiles, virulence factors, genome organization) Global Outbreak detection / Surveillance Direct application in public health Source attribution -> intervention
- 3. Image credits: 1) http://www.iissiidiology.net/en/publications/104-ayfaar-interpersonal-and-true-human-relationship-harmonization-mechanisms 2) http://blog.f1000research.com/2014/04/04/reproducibility-tweetchat-recap/ Data Integration Harmonization Reproducibility 1)
- 5. Read mapping algorithms Bowtie2 BWA SOAP2 Saruman mr/mrsFAST …. (And a lot more ) Algorithms Hatem M et all BMC Bioinformatics 2013..14:184 DOI: 10.1186/1471-2105-14-184 + a plethora of parameters for each of them + a (proper) choice of reference
- 6. Gene-by-gene approach allele call algorithms: BIGSdb ( Jolley, K.A. & Maiden, M. C. J. BMC Bioinf 11, 595 (2010).) Enterobase (https://enterobase.warwick.ac.uk/) GEP (Genome Profiler) (JCM. 2015 May;53(5):1765-7) Ridom Seqsphere Bionumerics (Applied Maths) Mostly assembly based (yes it is a lot of work … ) Assembly algorithms have some parameters (mostly k-mer sizes) Lots of heuristics for allele definition.. Algorithms
- 7. Gene by gene approaches: What is a locus? What is an allele? It depends on the algorithm(s) used! Algorithms However the results are largely congruent!
- 9. “Formal representation of knowledge as a set of concepts within a domain, and the relationships between those concepts” –Wikipedia Domain modeling: represents all the concepts involved in in microbial typing by sequence-based methods Provides a shared vocabulary, where the concepts should be unambiguous Enables a machine-readable format that can be used for software and algorithms automatically interact with multiple databases Ontologies
- 10. Ontologies
- 11. GenEpiO: Combining Different Epi, Lab, Genomics and Clinical Data Fields. Lab Analytics Genomics, PFGE Serotyping, Phage typing MLST, AMR Clinical Data Patient demographics, Medical History, Comorbidities, Symptoms, Health Status Reporting Case/Investigation Status GenEpiO (Genomic Epidemiology Application Ontology) See draft version at https://github.com/Public-Health-Bioinformatics/IRIDA_ontology Original slide from Emma Griffiths Ontologies
- 12. Public Health Surveillance Case Cluster Analysis Result Reporting Infectious Disease Epidemiology (from case to Intervention) Lab Surveillance (from sample to strain typing results) Evidence Collection & Outbreak Investigation Sample Collection & Processing Sequence Data Generation & Processing Bioinformatics Analysis Result Reporting Whole Genome Sequencing (SO, ERO, OBI etc) Quality Control (OBI, ERO) Anatomy (FMA) Environment (Envo) Food (FoodOn) Clinical Sampling (OBI) Custom LIMS Quality Control (OBI, ERO) AMR (ARO) Virulence (PATO) Phylogenetic Clustering (EDAM) Mobile Elements (MobiO) Quality Control (OBI, ERO) AMR (ARO) LOINC Surveillance (SurvO) Demographics (SIO) Patient History (SIO) Symptoms (SYMP) Exposures (ExO) Source Attribution (IDO) Travel (IDO) Transmission (TRANS) Food (FoodOn) Geography (OMRSE) Outbreak Protocols Surveillance (SurvO) Food (FoodOn) Surveillance (SurvO) Mobile Elements (MobiO) Infectious Disease (IDO) Typing (TypON) Nomenclature &Taxonomy (NCBItaxon) Original slide from Emma Griffiths /IRIDA http://foodontology.github.io/foodon/ (pipeline) NGSOnto
- 13. Provides machine-readable web-based interface,i.e.,the algorithms (not humans) can: retrieve, submit , update data /analysis results launch analysis/algorithms Interfaces http://www.clker.com/cliparts/q/P/V/D/5/R/cog-allgrey-hi.png
- 14. BIGSdb Enterobase Offer an Restful API for data retrieving, submission and data analysis Interfaces
- 15. Interfaces
- 16. Interfaces https://online.phyloviz.net/ API: *account creation *profile + metadata upload *running goeBURST *retrieving a link Private or Public data sharing Scalable to thousands of nodes Tree Analysis tools: Interactive distance matrix NLV graph
- 17. Transparency of analytical methods Better definition of concepts (Clinical/Lab/Analysis) Better tool/database interoperability • Reproducibility of results • Added value of analysis • Custom interfaces for non-bionf specialists
- 19. UMMI Members Bruno Gonçalves Mickael Silva Miguel MAchado Mário Ramirez José Melo-Cristino INESC-ID Alexandre Francisco Cátia Vaz Marta Nascimento EFSA INNUENDO Project (https://sites.google.com/site/innuendocon/) Mirko Rossi FP7 PathoNGenTrace (http://www.patho-ngen-trace.eu/): Dag Harmsen (Univ. Muenster) Stefan Niemann (Research Center Borstel) Keith Jolley, James Bray and Martin Maiden (Univ.Oxford) Joerg Rothganger (RIDOM) Hannes Pouseele (Applied Maths) Genome Canada IRIDA project (www.irida.ca) Franklin Bristow, Thomas Matthews, Aaron Petkau, Morag Graham and Gary Van Domselaar (NLM , PHAC) Ed Taboada and Peter Kruczkiewicz (Lab Foodborne Zoonoses, PHAC) Fiona Brinkman (SFU) William Hsiao (BCCDC) INTEGRATED RAPID INFECTIOUS DISEASE ANALYSIS