Rapid outbreak characterisation - UK Genome Sciences 2014 - wed 3 sep 2014
Download as PPTX, PDF•1 like•1,013 views
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.
Rapid outbreak characterisation - UK Genome Sciences 2014 - wed 3 sep 2014
- 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 [email protected] Twitter @torstenseemann Blog TheGenomeFactory.blogspot.com Web bioinformatics.net.au Contact