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.

1. Whole genome sequencing in
public health microbiology
A/Prof Torsten Seemann
Victorian Life Sciences Computation Initiative (VLSCI)
Microbiological Diagnostic Unit Public Health Laboratory (MDU PHL)
Doherty Centre for Applied Microbial Genomics (DCAMG)
The University of Melbourne
MDU/VIDRL Mini Seminar - Melbourne, AU - Wed 17 June 2015

2. The one true assay?

3. Traditional workflow

4. Whole Genome Sequencing (WGS)
2-10 Mbp
100-300 bp
30-100x depth
Simple?“Analysis” and “Results”

5. The promise of genomics
∷ A single assay
∷ Cheaper
∷ Faster
∷ High throughput
∷ Full single nucleotide resolution

6. Utility of WGS
∷ Diagnostics
: strain level identification
: in silico antibiogram and virulence profile
∷ Surveillance
: in silico genotyping - MLST, serotyping, VNTR, MLVA
: what’s lurking in our hospital/community?
∷ Forensics
: outbreak detection
: source tracking

7. Modern workflow

8. Does it deliver?
∷ In general, YES
∷ But it does NOT replace good epidemiology
∷ WGS is a just another (powerful) tool
∷ Need proper bioinformatics

9. Got my reads. Now what?

10. Aligning to reference
AGTCTGATTAGCTTAGCTTGTAGCGCTATATTAT
AGTCTGATTAGCTTAGAT
ATTAGCTTAGATTGTAG
CTTAGATTGTAGC-C
TGATTAGCTTAGATTGTAGC-CTATAT
TAGCTTAGATTGTAGC-CTATATT
TAGATTGTAGC-CTATATTA
TAGATTGTAGC-CTATATTAT
SNP Deletion

11. Reference based analysis
∷ Implies you have a “close” reference
: need to be careful with draft genomes
∷ Very sensitive
: single mutation precision
∷ Core genome only
: ignores novel DNA in your isolate

12. De novo genome assembly

13. De novo analyses
∷ Does not require a reference
∷ Access to whole pan-genome
: new plasmids
: unexpected antibiotic resistance elements
: virulence factors
∷ Limited by short reads
: misleading results in repeated regions
: not suitable for high-res SNP analysis

14. Best practice
∷ Use both approaches
: reference-based + de novo
∷ Best of both worlds
: and worst of both worlds - interpretation is non-trivial
∷ Still need
: good epidemiology, metadata and domain knowledge!

16. Limitations

17. Sequencing bias
Isolate genome
Sequenced reads
Other isolates in
sequencing run
Contamination
Unsequenced
regions

18. Read length
250 bp - Illumina - $100 8000 bp - Pacbio - $1000

19. Repeats
Repeat copy 1 Repeat copy 2
Collapsed repeat consensus
1 locus
4 contigs

20. Inferring transmission
∷ Identical
sequence
does not imply
transmission
∷ Easier to rule
out than in

21. Cutting edge web tools

22. Real time
tracking of
seasonal
influenza
virus
evolution in
humans
nextflu.org

23. ebola.nextflu.org

24. mers.nextflu.org

25. Drag genomes.
Calculates:
∷ tree
∷ MLST
∷ resistome
Add metadata
∷ source
∷ location
∷ colours
wgsa.net

26. Visualise and explore trees linked to genome
data. Just upload .nwk and .csv!
microreact.org

27. Sharing data

28. Open science
∷ Crowd-sourcing provably works
: EHEC outbreak 2011
: Ebola
: MERS
∷ But only if people share
: sequencing data
: metadata
: software source code for analysis

29. GenomeTrakr
∷ International cooperation
: Led by FDA + NCBI
: >20 collaborating institutes inc. UK PHE, DK DTU, MX
: Salmonella and Listeria
∷ Public SRA BioProject #183844
: Real-time submission of WGS genome reads
: Nightly updates of phylogenomic trees
: Contains ~8000 strains of Salmonella

30. “GenomeTrakka”
∷ A shared online system for all Australian labs
: upload samples
: automated standard/specific analyses
: simple reports and visualization
: easy to submit to international archives (SRA)
∷ Access control
: each lab controls their own data
: jurisdictions can share data in national outbreaks

31. Conclusion

33. Acknowledgements
∷ Slide source material
: Nick Loman
: Jennifer Gardy
: Rob Beiko
∷ Slide feedback
: Jason Kwong
: Dieter Bulach

34. Contact
∷ http://tseemann.github.io
∷ t.seemann@unimelb.edu.au
∷ @torstenseemann

35. The End
Thank you for listening.