Presentation for 2nd Conference Rapid Microbial NGS and Bioinformatics: Translation Into Practice Hamburg/Germany, June 9-11, 2016 http://rami-ngs.org/

1. JoãoAndré Carriço, Mario Ramirez
Microbiology Institute and Instituto de Medicina Molecular,
Faculty of Medicine, University of Lisbon
jcarrico@fm.ul.pt 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)

4. Algorithms
Interfaces
Ontologies

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!

8. Ontologies
Image from http://www.emiliosanfilippo.it/?page_id=1172

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