My ICEID talk in the Big Data Bioinformatics session at ICEID 2015. My talk was in the middle of Nick Loman and Jennifer Gardy.

1. Approaches to analysing
1000s of bacterial isolates
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
ICEID 2015 - Atlanta, USA - Mon 24 Aug 2015

2. Introduction

3. Melbourne, Australia

4. Microbial genomics
Doherty Centre for
Applied Microbial Genomics

5. Microbiological Diagnostic Unit
∷ Oldest public health lab in Australia
: established 1897 in Melbourne
: large historical isolate collection back to 1950s
∷ National reference laboratory
: Salmonella, Listeria, EHEC
∷ WHO regional reference lab
: vaccine preventable invasive bacterial pathogens

6. The shift to WGS
∷ New director
: Prof Ben Howden - clinician, microbiologist, pathologist
∷ New building
: Doherty Institute for Immunity and Infection
∷ Mandate
: modernise service delivery
: nationally lead the conversion to WGS
: enhance research output and collaboration

7. The one true assay?

8. Traditional workflow

9. Modern workflow

10. Does it deliver?
Yes!Bioinformatics Epidemiology
Technology
Microbiology
This means
scientists
not just software
Domain
expertise
Always changing...

11. Acquiring genome data

12. Sequencing a sample
∷ Simple preparation
∷ Multiplexing
∷ Robotics
∷ Reliable instruments
Isolation is a bottleneck
→ Direct sequencing?

13. Where is the genome data?

14. What data do we really have?
Isolate genome
Sequenced reads
Other isolates in
sequencing run
Contamination
Unsequenced
regions

15. The effect of read length
250 bp - Illumina - $100 8000 bp - Pacbio - $1000

16. The problem with repeats
Repeat copy 1 Repeat copy 2
Collapsed repeat consensus
1 locus
4 contigs

17. Processing the data

18. De novo genome assembly

19. Compare to already assembled genomes
AGTCTGATTAGCTTAGCTTGTAGCGCTATATTAT
AGTCTGATTAGCTTAGAT
ATTAGCTTAGATTGTAG
CTTAGATTGTAGC-C
TGATTAGCTTAGATTGTAGC-CTATAT
TAGCTTAGATTGTAGC-CTATATT
TAGATTGTAGC-CTATATTA
TAGATTGTAGC-CTATATTAT
SNP Deletion
Reference
Reads

20. 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!

21. Applications

22. Backward compatibility
∷ MLST
∷ NG-MAST
∷ Resistome
∷ Virulome
∷ MLVA
∷ VNTR
∷ Serotyping
∷ PFGE
∷ Phage typing

23. New assays
∷ Species identification
: build a “signature” from k-mer/oligos in the reads
: compare to database of known signatures
: strain level accuracy
∷ Features
: very fast screening, < 1 minute per isolate
: identify contamination, mixed samples
: discover wrongly labelled samples!

24. Phylogenomics

25. Reference based analysis
∷ Implies you have a “close” reference
: need to be careful with draft genomes
∷ Very sensitive
: single mutation precision
∷ May not be complete
: ignores novel DNA in your isolate

27. The pan genome

28. The core genome
Core is common to all & has similar sequence.

29. Core
∷ Common DNA
∷ Vertical evolution
∷ Genotyping
∷ Phylogenetics
∷ Novel DNA
∷ Lateral transfer
∷ Plasmids
∷ Mobile elements
∷ Partly unexploited
Pan

30. Pan genome analysis
Rows are genomes, columns are genes.

31. Conclusion

32. Bioinformatics challenges
∷ Metagenomics
: avoiding the isolation bottleneck
∷ Incremental update of data analyses
: both core and pan genome
: phylogenetic trees
∷ Data distribution
: finding and getting appropriate comparator isolates

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

35. The End
Thank you for listening.