1. Bioinformatics for Clinical Microbiology and Molecular Epidemiology: From Databases to Population Genetics João André Carriço 7 July 2010 Ciência 2010

2. Bacterial populations Evolution Mutation Recombination Individual variability Heterogeneity (genomics) and population flexibility Response to selective forces Dynamics Drift Adaptation

3. Pathogen bacterial populations Colonization Infection Virulence factors “Epidemicity” Interactions Host Microbiota Human imposed selection Antimicrobials Vaccination

4. Bioinformatics for Microbiology Epidemiological Information Systems Data analysis Data visualization Simulation Models

5. Bioinformatics for Microbiology 1.Project SEQTYPEME 2.GoeBURST/Phyloviz 3.Comparing Classifications 4.Large Scale Bacterial Population Simulations

6. 1. Project SEQTYPEME Sequence-Based typing methods Databases Used in worldwide and local epidemiology studies Known Problems Non-standard data interfaces Human intensive curation - long turnaround time Difficult data access in machine readable formats Proposed solutions for increased flexibility Decoupling of databases and interfaces by: Use of REST architecture Creation of an ontology for the microbial typing field Web services for sequence analysis and curation Use of semantic web approaches for data storage and querying (RDF and SPARQL) Project Participants: IMM, INESC-ID, ITQB, FCT

7. 1. Project SEQTYPEME Sequence-Based typing methods Databases Used in worldwide and local epidemiology studies Ultimate Project Goal: Better databases for epidemiological surveillance, strain tracking and evolutionary studies Prototype being implemented: (ISEL and INESC-ID collaboration) Triple store Server side JENAJERSEY REST Client side GWT website JAVA library Human interface Machine interface Outbreak detection Antibiotic resistance control Vaccine development

8. 2. goeBURST Identify bacterial clones Multilocus sequence typing Internal sequence of 7 housekeeping genes Expansion and diversification of successful clones Allows us to establish phylogenetic relationships between clones

9. goeBURST A global optimal solution to this problem Francisco et al, BMC Bioinformatics,2009 http:// goeBURST.phyloviz.net Can be used by any sequence-based typing method that generates allelic profiles: MLST MLVA …. 2. goeBURST

10. Phyloviz Expands goeBURST allowing visual data integration exploration (www.phyloviz.net)

11. 3. Comparing Classifications Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 Sample 6 Sample 7 Sample 8 Sample 9 Method 1Method 2 How to compare classifications of the same entities? Identify existing coefficients Propose new coefficients Propose new CI 95% Carriço et al, JCM, 2006 Pinto et al, BMC Bioinformatics, 2007 Pinto et al, Plos ONE, 2008

12. www.comparingpartitions.info 3. Comparing Classifications

13. 4. Large Scale Bacterial Population Simulations TIME (generations) Analysing models of bacterial evolution on dynamic graphs Influence of social behavior on Bacterial spread and evolution Development of vaccination strategies Studies on Sampling Bias Testing and Validation of sequence based typing methods Analysing models of bacterial evolution on dynamic graphs Influence of social behavior on Bacterial spread and evolution Development of vaccination strategies Studies on Sampling Bias Testing and Validation of sequence based typing methods

14. U nidade M icrobiologia M olecular e I nfecção Clinical Microbiology Basic Microbiology Bioinformatics Epidemiology Genomics Population biology Modeling

15. Acknowledgements UMMI Mário Ramirez José Melo-Cristino Ana Severiano Pedro Monteiro FCUL Francisco Pinto INESC-ID Alexandre Francisco Cátia Vaz ISEL João Almeida João Tiple Funding agencies Fundação para a Ciência e a Tecnologia União Europeia – 7 th Framework program

16. To Know More: http://im.fm.ul.pt