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Explore the intersection of bioinformatics and microbiology in clinical settings. Learn about bacterial populations, evolution, mutation, and more. Discover Project SEQTYPEME and GoeBURST tools for population genetics and outbreak detection.
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Ciência 2010 Bioinformatics for Clinical Microbiology and Molecular Epidemiology: From Databases to Population Genetics João André Carriço 7 July 2010
Bacterial populations Evolution Mutation Response to selective forces Recombination Individual variability Heterogeneity (genomics) and population flexibility Dynamics Drift Adaptation
Pathogen bacterial populations Colonization “Epidemicity” Interactions Host Human imposed selection Microbiota Infection Antimicrobials Virulence factors Vaccination
Bioinformatics for Microbiology Epidemiological Information Systems Data visualization Simulation Models Data analysis
Bioinformatics for Microbiology • Project SEQTYPEME • GoeBURST/Phyloviz • Comparing Classifications • Large Scale Bacterial Population Simulations
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
1. Project SEQTYPEME Sequence-Based typing methods Databases Used in worldwide and local epidemiology studies Prototype being implemented: (ISEL and INESC-ID collaboration) Ultimate Project Goal: Better databases for epidemiological surveillance, strain tracking and evolutionary studies Client side Server side Outbreak detection Antibiotic resistance control Vaccine development GWT website Human interface Machine interface Triple store JENA JERSEY REST JAVA library
2. goeBURST Identify bacterial clones Multilocus sequence typing Internal sequence of 7 housekeeping genes Allows us to establish phylogenetic relationships between clones Expansion and diversification of successful clones
2. goeBURST 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 …. goeBURST A global optimal solution to this problem
Phyloviz (www.phyloviz.net) Expands goeBURST allowing visual data integration exploration
3. Comparing Classifications Method 1 Method 2 How to compare classifications of the same entities? Sample 1 Sample 2 Sample 3 Identify existing coefficients Sample 4 Carriço et al, JCM, 2006 Propose new coefficients Sample 5 Sample 6 Propose new CI95% Pinto et al, BMC Bioinformatics, 2007 Sample 7 Sample 8 Pinto et al, Plos ONE, 2008 Sample 9
3. Comparing Classifications www.comparingpartitions.info
4. Large Scale Bacterial Population Simulations TIME (generations) Studies on Sampling Bias Studies on Sampling Bias Analysing models of bacterial evolution on dynamic graphs Analysing models of bacterial evolution on dynamic graphs Testing and Validation of sequence based typing methods Testing and Validation of sequence based typing methods Influence of social behavior on Bacterial spread and evolution Influence of social behavior on Bacterial spread and evolution Development of vaccination strategies Development of vaccination strategies
Unidade Microbiologia Molecular e Infecção Clinical Microbiology Basic Microbiology Population biology Genomics Epidemiology Modeling Bioinformatics
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 – 7th Framework program