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Bioinformatic tools for the diagnostic laboratory
A/Prof Torsten Seemann
Victorian Life Sciences Computation Initiative (VLSCI)Microbiological Diagnostic Unit Public Health Laboratory (MDU PHL)
Doherty Applied Microbial Genomics (DAMG)The University of Melbourne
ASA 2016 - Melbourne, AU - Sat 27 Feb 2016
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Doherty Applied Microbial Genomics
Lead bioinformatician ♥ microbial genomics
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Whole genome sequencing
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The currency of genomics
Reads
Reads are stored in FASTQ files
Genome
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Types of sequence reads
100 - 300 bp (paired)
100 - 400 bp
5,000 - 15,000+ bp
5,000 - 50,000+ bp
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What data do we really have?
Isolate genomeSequenced reads
Other isolates in sequencing run
ContaminationSequencing adaptorsSpike-in controls eg. phiX
Unsequenced regions
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Do we have enough data?
∷ Depth: expressed as fold-coverage of genome eg. 25x: means each base sequenced 25 times (on average)
∷ Coverage: the % of genome sequenced with depth > 0
25x
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Genome data itself is of limited value.
Needs “extra” information
□ location: Australia 37.8S,145.0E □ date: 2015 2015-07-20□ source: human 60yo male faecal swab□ etc.
Metadata
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Got my reads, now what?
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Two options
∷ De novo genome assembly: reconstruct original sequence from reads alone: like a giant jigsaw puzzle: “create”
∷ Align to reference: identify where each read fits on a related genome: can not always be uniquely placed: “compare”
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De novo genome assemblyAmplified DNA
Shear DNA
Sequenced reads
Overlaps
Layout
Consensus ↠ “Contigs”
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The effect of read length
250 bp - Illumina - $200 8000 bp - Pacbio - $2000
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The problem with repeatsRepeat copy 1 Repeat copy 2
Collapsed repeat consensus
1 locus
4 contigs
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Align to referenceSeven short 4bp readsAGTC TTAC GGGA CTTT
TAGG TTTA ATAG
Aligned to 31bp referenceAGTCTTTATTATAGGGAGCCATAGCTTTACAAGTC TAGG ATAG TTAC
TTTA GGGA CTTT
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Eight short 4bp readsAGTC TTAC GGGA CTTT
TAGG TTTA ATAG TTAT
Aligned to 31bp referenceAGTCTTTATTATAGGGAGCCATAGCTTTACAAGTC TAGG ATAG TTAC
TTTA GGGA CTTT TTAT TTAT
Ambiguous alignment
D’oh!
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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!
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The one true assay?
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Applications of WGS
∷ Diagnostics: species ⇒ subspecies ⇒ strain 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
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Isolate identification
∷ Can be done in seconds∷ Directly from reads (or subset)
∷ Scan against index of unique k-mers (oligoes)∷ Species level accurate (on average)
∷ Great for quality control !
Kraken,MetaPhlan,OneCodex
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One Codex example metagenome output
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Antibiogram
∷ The “resistome”
∷ Resistance specific genes: we have good databases of these: easy to identify to exact allele eg. blaNDM-9
∷ New alleles conferring resistance: databases are poor (exceptions include M.tb): novel mechanisms arrive de novo
ResFinder, CARD, ARG-Annot
SRST2, ABRicate
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ABRicate example E.faecium outputSTART END GENE COVERAGE COVERAGE_MAP GAPS %COVERAGE %IDENTITY
7140 7902 erm(B) 1-762/762 ========/====== 1 100.00 99.08
8627 9421 aph(3')-III 1-795/795 =============== 0 100.00 100.00
11040 11948 ant(6)-Ia 1-345/909 =====.......... 0 35.00 100.00
15456 16257 lnu(B) 1-804/804 ========/====== 2 99.75 99.63
573128 575046 tet(M) 1-1920/1920 ========/====== 1 99.95 99.95
770130 770792 VanR-B 1-663/663 =============== 0 100.00 99.25
770792 772135 VanS-B 1-1344/1344 =============== 0 100.00 99.63
772306 773112 VanY-B 1-807/807 =============== 0 100.00 100.00
773130 773957 VanW-B 1-828/828 =============== 0 100.00 97.58
773954 774925 VanH-B 1-972/972 =============== 0 100.00 99.38
774918 775946 VanA-B 1-1029/1029 =============== 0 100.00 98.93
775952 776560 VanX-B 1-609/609 =============== 0 100.00 96.72
2352083 2352631 aac(6')-Ii 1-549/549 =============== 0 100.00 99.64
2789984 2791462 msr(C) 1-1479/1479 =============== 0 100.00 98.92
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Virulence profile
∷ The “virulome”
∷ Curated databases : known virulence genes: pathogenicity islands
∷ Caveats: variable representation across organisms
VirulenceFinder,VFDB, MvirDB,
ViPR, PAI DB
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Backward compatibility
MLSTResistomeVirulomeNG-MAST
MLVAVNTR
SerotypingPhage typing
PFGE
SRST2, mlst, ngmaster, lissero,and many more!
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When typing lets us down
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Typing resolution
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Focus on a small “informative” section
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Genotype shows isolates are related
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D’oh!
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Exploiting the whole genome
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A familiar tree
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Every SNP is sacred
∷ Chocolate bar tree: branches were based on phenotypic attributes: size, colour, filling, texture, ingredients, flavour
∷ Genomic trees: want to use every part of the genome sequence: need to find all differences between isolates
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Finding differences
AGTCTGATTAGCTTAGCTTGTAGCGCTATATTATAGTCTGATTAGCTTAGAT
ATTAGCTTAGATTGTAG
CTTAGATTGTAGC-C
TGATTAGCTTAGATTGTAGC-CTATAT
TAGCTTAGATTGTAGC-CTATATT
TAGATTGTAGC-CTATATTA
TAGATTGTAGC-CTATATTAT
SNP Deletion
Reference
Reads
Snippy, VarScan, SAMtools, GATKand many more!
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SNP distance matrix
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Annotated tree
∷ 1 SNP resolution
∷ Distinguishes clades within genotypes
∷ Interpretation is not straightforward
10 SNPs
L. monocytogenes
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Same tree!
Dendrogram
Spanning
Radial
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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
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Inferring transmission
∷ Identical sequence does not imply transmission
∷ Easier to rule out than in
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The pan genome
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Align all your isolate genomes
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Find “common” segments
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The core genome
Core is common to all & has similar sequence.
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Example pan genome Roary, LS-BSR, OrthoMCL, Degust
Rows are genomes, columns are genes.
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Core
∷ Common DNA∷ Vertical evolution
∷ Genotyping∷ Phylogenetics
∷ Novel DNA∷ Lateral transfer∷ Plasmids∷ Mobile elements
∷ Partly unexploited
Accessory
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Progress at MDU-PHL
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Traditional workflow
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Modern workflow
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Nullarbor
∷ Software pipeline: does “reads to report”: cloud image available (mGVL)
∷ Under active development: used at MDU-PHL for past year for routine jobs: also used by USA CDC Enterics, FSS Qld, and research
∷ National access programme underway
null arbor“no trees”
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Doherty Applied Microbial Genomics
■ Non-profit service available□ fixed price per isolate
■ Genome sequencing□ Illumina NextSeq 500
■ Bioinformatics analysis□ Nullarbor
■ Report□ QC, typing, resistome, phylogeny□ plus your raw data
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Sharing is caring
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Open science
∷ Crowd-sourcing provably works: EHEC outbreak 2011: Ebola, MERS, Zika
∷ But only if people share: sequencing data: metadata: software source code for analysis
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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 ~25,000 strains of Salmonella
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“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
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Final thoughts
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Does WGS deliver?
Yes!Bioinformatics Epidemiology
Technology
Microbiology
This meansscientists
not just software
Domain expertise
Always changing...
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Acknowledgements
Ben HowdenTim Stinear
Dieter BulachJason Kwong
Anders G da Silva
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The EndThank you for listening.