This document summarizes a presentation on using whole genome sequencing (WGS) for rapid characterization of bacterial outbreaks. The presenter discusses transitioning public health labs from traditional typing methods to WGS-based approaches. Key points include developing automated analysis pipelines to identify bacteria, determine antimicrobial resistance and virulence genes, and construct phylogenomic trees from core genome SNPs. The goal is a cloud-based system allowing labs to securely upload and analyze sequencing data with open source tools integrated in modular pipelines.

1. Rapid bacterial outbreak
characterisation from whole
genome sequencing
Torsten Seemann
Genome Science: Biology, Technology & Bioinformatics - Wed 13 July 2014 - Oxford, UK - #UKGS2014

2. About me
● Victorian Bioinformatics Consortium
o Monash University, Melbourne, Australia
● Microbial genomics
o bacterial pathogens; some parasites, viruses, fungi
● Tool development
o Prokka, Nesoni, VelvetOptimiser, Snippy, ...

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

4. New director
● Professor Ben Howden
o clinician, microbiologist, pathologist
o early adopter of genomics and bioinformatics
● Mandate
o modernise service delivery
o enhance research output and collaboration
o nationally lead the conversion to WGS

5. Outbreak scenario
● Receive samples (human, animal, enviro)
● Extract, culture, isolate
● Identification via phenotype, growth, media
● Typing: MLST, MLVA, PFGE, phage, sero, ...
● Screening: VITEK
● Report back to hospital, state government

6. Traditional typing
● Low resolution
o small subset of genome
 MLST ~7 core genes
 MLVA uses handful of VNTR regions
o requires constant curation of new genotypes
● Labour intensive
o time consuming

7. Whole Genome Sequencing
● Backward compatible
o can derive most traditional genotypes
● High resolution
o all variation, plasmids, AbR & virulence genes
● High throughput
o cheap, fast - one assay replaces many

8. Resistance to change
● Protecting empires
o “this is how we’ve always done it”, job redundancies
● Expense of instruments
o capital purchase, new staff, maintenance
● Lack of bioinformatics support
o infrastructure, software, training
● Legal requirements
o must do PFGE, validation, accreditation

9. A vision for Australia
● A common online system for all labs
o upload samples
o automated standard analysis pipelines
● Access control
o each lab controls their own data
o jurisdictions can share data in national outbreaks
● Deploy on our national research cloud
o no investment or expertise needed
o can deploy private version if desired

10. Suggested pipeline
● Input
o FASTQ files for each isolate
● Per isolate output
o de novo assembly & annotation
o typing (species dependent)
o antibiotic resistance & virulence genes
● Per outbreak output
o annotated phylogenomic tree
o SNP distances, clonality predictions

11. Design goals
● Speed
o multi-threaded wherever possible
● Modular
o Unix-style reusable components
● Deployable on cloud
o Amazon, Nectar (.au), CLIMB (.uk)
● Open source
o Auditable, community contribution

12. Progress
● Currently
o assessing existing components
o implementing new ones - all on GitHub
● No final product yet
o but some components are usable now
● Rolling out in 2015
o labs around Australia will opt in, most are keen

13. Identifying isolates
● De novo assembly approach
o assemble into contigs
o BLAST contigs against all microbial sequences
o best hits, highest coverage
● Assembly free method
o build index of all microbial k-mers w/ taxonomy
o scan k-mers from reads and tally
o Kraken, BioBloomTools, ...

14. Kraken report
1.04 1046 1046 U 0 unclassified
98.96 99624 142 - 1 root
98.81 99473 1 - 131567 cellular organisms
98.81 99472 194 D 2 Bacteria
98.57 99233 111 P 1224 Proteobacteria
98.45 99110 318 C 1236 Gammaproteobacteria
98.07 98728 0 O 91347 Enterobacteriales
98.07 98728 52477 F 543 Enterobacteriaceae
44.95 45256 665 G 561 Escherichia
44.20 44498 33391 S 562 Escherichia coli
8.84 8899 8899 - 1274814 Escherichia coli APEC O78
0.29 287 0 - 244319 Escherichia coli O26:H11
0.29 287 287 - 573235 Escherichia coli O26:H11 str 11368
0.21 216 216 - 316401 Escherichia coli ETEC H10407
0.19 193 0 - 168807 Escherichia coli O127:H6
0.19 193 193 - 574521 Escherichia coli O127:H6 str E2348/69
http://ccb.jhu.edu/software/kraken

15. Assembill
● Decent automated assemblies
o only 3 parameters: outdir + R1.fq.gz + R2.fq.gz
o supports multithreading at all steps
● Main steps
o adaptor removal & quality trimming (Skewer)
o selection of K from k-mer spectra (KmerGenie)
o de novo assembly (Velvet, Spades)
o ordering of contigs against reference (MUMmer)

16. Prokka
● Prokaryotic Annotation
o only 2 parameters: outdir + contigs.fa
o scales to about 32 threads
● Finds
o CDS, tRNA, tmRNA, rRNA, some ncRNA
o CRISPR, signal peptides
● Produces
o Genbank, GFF3, Sequin, FASTA, ...

17. mlst
● Multi-Locus Sequence Typing
o only 2 parameters: scheme + contigs.fa
● Can mass-screen hundreds of assemblies
o comes bundled with PubMLST database
● Output
o tab/comma separated values

18. AbRicate
● Identify known AB resistance genes
o only 1 parameters: contigs.fa
● Only as good as the underlying database
o Bundled with ResFinder
o does not include SNP-based AbR-conferring genes
● Output
o tab/comma separated table

19. Wombac
● Quickly identify core genome SNPs
● Efficiently use all CPUs and RAM
● Re-use previous reference alignments
● Cheap to calculate new core subsets

20. Read alignment
Use BWA MEM
● Do not need to clip reads
● Deduces the fragment library attributes
● Marks multi-mapping reads properly
● Scales linearly to >100 cores
● Outputs SAM directly

21. Sorted BAM
● No intermediate files
o use Unix pipes
● Multiple CPUs with SAMtools > 0.1.19+
o use the -@ command line parameter
bwa → samtools view → samtools sort → BAM

22. SNP calling
● FreeBayes
o set in haploid mode (p=1)
o set regular parameters (mindepth, minfrac)
o call variants in all samples jointly (more power)
o single multi-isolate VCF output
freebayes -p 1 *.bam → all.vcf

23. Parallel Freebayes
● FreeBayes is single threaded
o divide genome into regions
o run separate freebayes in parallel on each region
o merge the results
o scales nearly linearly!
fasta-generate-regions.py ref.fa > regions.txt
freebayes-parallel 32 regions.txt -p 1 *.bam → all.vcf

24. Select core SNPs
● Core SNPs
o position present in every isolate
o more than one allele (not wholly conserved)
o usually ignore indels and other odd genotypes
● Recombination
o not all core SNPs are real
o many result of recombination
o should be filtered out, could alter tree topology

25. Wombac speed
● Example
o 130 E.coli isolates, MiSeq 300bp PE
o With 32 cores, used < 4GB RAM/core
o Took just over 1 hour
● Add a new sample
o Re-use existing alignments
o Will migrate to gVCF method that GATK will use
● Recalculate a core tree on subset

26. Email torsten.seemann@gmail.com
Twitter @torstenseemann
Blog
TheGenomeFactory.blogspot.com
Web bioinformatics.net.au
Contact