high-throughput sequencing

• 1.
  • PROFESSOR MARK PALLEN

• UNIVERSITY OF BIRMINGHAM

High-throughput sequencingOverview and selected applications 2. Outline

• What is high-throughput sequencing?

• • How it works

• • Key considerations

• Applications

• • Clinical Microbiology

• • Cancer Biology

3. Conventional Sequencing

• Sanger dideoxy chemistry 1970s

• Bacterial genome sequencing 1990s

• • Whole-genome shotgun

• • Clonal populations of template molecules in vector/cloning host

• • Read lengths >500 bp

• • De novoassembly

• Drawbacks

• • Time-consuming, expensive, onerous

• • • Beyond average project grant

• • • Out of reach of university infrastructure

• • Relies on colony propagation and picking

• • • Some sequences cannot be cloned

4. High-throughput Sequencing

• >100x faster, >100x cheaper!

• • A disruptive technology

• Three second-generation technologies in the marketplace

• • 454 (Roche)

• • Solexa (Illumina)

• • SOLiD (ABI)

• Fundamentally new approaches

• • Solid-phase amplification of clonal templates in molecular colonies

• • • Massive increase in number of clones compensates for shorter read length

• • New chemistries for sequence reading

• • • Pyrophosphate detection (PPi release upon base addition): 454

• • • Reversible addition of fluorescent : Solexa

• • • Sequencing by Ligation: SOLiD

5. Recent Developments

• Single-molecule sequencing

• • Pacific Biosciences (PacBio)

• • Nanopore

• Benchtop sequencers

• • Ion Torrent

• • MiSeq

6. Sequencing in Birmingham 7. 454 Life Sciences (Roche) 8. 454 Life Sciences (Roche) 9. Solexa/Illumina Sequencing 10. SOLiD Sequencing Requires emPCR Long run-times Short read-lengths (stuck at 50bp) Sequences in colour space 11. Source: http://www.politigenomics.com/next-generation-sequencing-informatics *Now improved to 1 kb reads and choice of 3, 8 or 20 kb inserts#b=bases, B=bytes Vendor: Roche Illumina ABI Technology: 454 Solexa GA SOLiD Platform: GS20 FLX Ti I II IIx 1 2 3 Reads: (M) 0.5 0.5 1.25 28 100 150 40 115 320 Fragment Read length: 100 200 400* 35 50 100 25 35 50 Run time: (d) 0.25 0.3 0.4 3 3 5 6 5 8 Yield: (Gb#) 0.05 0.1 0.5 1 5 15 1 4 16 Rate: (Gb/d) 0.2 0.33 1.25 0.33 1.67 3 0.34 1.6 2 Images: (TB#) 0.01 0.01 0.03 0.5 1.1 2.8 1.8 2.5 1.9 Paired-end Read length: 200 400 235 250 2100 225 235 250 Insert: (kb) 3.5 3.5* 0.2 0.2 0.2 3 3 3 Run time: (d) 0.3 0.4 6 10 10 12 10 16 Yield: (Gb) 0.1 0.5 2 9 30 2 8 32 Moores law applies! The Sequencing Singularity! Everything published is out of date! 12. Modes and Applications

• For some applications, 454 read length essential, e.g.

• • amplicon sequencing; otherwise assembly will create chimeras

• • differential splicing; translocations

• For other applications read number is more important; read length less so

• • Transcriptomics where 35 b read will identify transcript

• • SNP discovery/screening

13. Modes and Applications

• Modes

• • Basic shotgun library

• • Paired-end or mate-pair shotgun

• • Amplicon sequencing

• Applications

• • Whole genome

• • Metagenome, phylogenetic profiling

• • Transcriptome

• • SNP analysis; Splice variants; Methylation

• • Targeted sequence capture by microarray; Small RNAs

14. Modes and Applications

• Sequencing run is the basic unit

• • Basic cost of 454 or Illumina ~several 1000s per run in consumables & essential on-costs

• • Additions for consumables and/or staff time for

• • • multiple library preparation

• • • some modes, e.g. paired end

• • • data analysis etc

• Run can be subdivided

• • Plate-dividing gaskets (loss of wells)

• • Multiplex identifiers (MIDs or sequence barcodes)

• So cost per sample may be ~10s not 1000s!

• • But logistics of filling a plate may incur delays

15. De novoassembly versus alignment against template (aka re-sequencing) 16. Bacterial Genomic Epidemiology

• Genome sequencing brings the advantages of

• • open-endedness (revealing the unknown unknowns),

• • universal applicability

• • ultimate in resolution

• High-throughput platforms

• • 454, Illumina, PacBio

• • Expense and set-up puts them beyond average lab

• Bench-top sequencing platforms

• • generate data sufficiently quickly and cheaply to have an impact on real-world clinical and epidemiological problems

17. The Birth of Genomic Epidemiology for Bacteria 18. The Birth of Genomic Epidemiology for Bacteria 19. Sequencing in Birmingham @mjpallen @pathogenomenick #AAMTHI 20. Case StudyAcinetobacter baumannii

• Gram-negative bacillus

• Multi-drug resistant

• • colistin and tigecycline as reserve agents

• • moving towards pan-resistance

• Associated with

• • wound infections and ventilator-associated pneumonia

• • bloodstream infections

• • returning military personnel from Iraq and Afghanistan

• • transmission from military to civilian patients

21. Acinetobacter baumannii : problems

• Hard to identify in clinical laboratory

• • Two related genomospecies 3 and 13TU, (now A. pittii and A. nosocomialis) impossible to distinguish phenotypically

• Outbreak strains can be identified by PFGE, VNTR and gene-specific assays

• • BUT mode of spread and transmission chains often uncertain, hindering optimal management of outbreaks and rational design of policies

• Mechanism of resistance hard to identify in individual cases

• Poor understanding of pathogen biology

22. Applications and Questions

• Epidemiology

• • Q1: Can whole-genome sequencing detect differences between isolates within an outbreak?

• • Q2: Can these differences be used to help determine chains of transmission?

• Emergence of Resistance

• • Q3: Can it reveal how resistance emerges?

• Taxonomy and Identification

• • Q4: Can it tell us what defines a species within a genus?

23. AcinetobacterGenomic Epidemiology

• Outbreak in Birmingham Hospital in 2008

• Isolates indistinguishable by current typing methods

24. AcinetobacterGenomic Epidemiology

• 454 whole-genome sequencing of 6 isolates

• SNP detection by mapping reads against draft reference assembly

• SNP filtering for false positives

• SNP validation with Sanger sequencing of PCR amplicons

25. Outbreak isolates distinguishable at only three loci SNP 1SNP 2SNP 3AB0057CAGM1CAGM2TAGM3TATM4TAGC1TTGC2TAG 26. 27. 28. Before and after tigecycline therapy

• Genomes of twoAcinetobacter baumanniiisolates from single patient sequenced

• • AB210 before tigecycline therapy (susceptible); 454 sequenced

• • AB211 after therapy (resistant); Illumina-sequenced

29. Before and after tigecycline therapy

• Eighteen SNPs detected between AB210 and AB211

• • nine non-synonymous

• • including a SNP inadeSwhich accounts for resistance phenotype

• Three contigs i