1 NEXT-GENERATION SEQUENCING (NGS) WORKSHOP November 10-11, 2016 Embassy Suites by Hilton Dallas - DFW Airport South Is NGS for everyone? Lee Ann Baxter-Lowe University of Southern California Children’s Hospital Los Angeles NEXT-GENERATION SEQUENCING (NGS) WORKSHOP November 10-11, 2016 Embassy Suites by Hilton Dallas - DFW Airport South CONFLICT OF INTEREST Lee Ann Baxter-Lowe, Ph.D. Clinical Professor University of Southern California Los Angeles, CA USA I have no financial relationships with commercial interests to disclose. My presentation does not include discussion of off-label or investigational use of drugs. My presentation includes discussion of investigational laboratory tests. • Factors to consider in selecting a platform and approach. • Costs as incentive and barrier • Strategies for integrating NGS into lab workflow Is NGS for everyone?
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• Compatibility with other areas of laboratory medicine
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NGS is becoming mainstream
•Cancer diagnostics
•Infectious disease
•Genetic diseases
Is NGS for my lab?• Typing indications
• Typing volume and turn-around-time
• Local environment
• Cost
Factors to consider in vendor selection
• Lab’s resources
• Platform
• Performance characteristics
• Practical
• Support
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Factors to consider in evaluating products
Lab’s resources for evaluating products
• Conferences (e.g., ASHI meetings)
• Evaluation options
•On-site vs off-site • Lab’s cost for evaluation of product
•How many vendor evaluations can be supported by lab?
Factors to consider in typing approach
Platform beyond performance
• Institutional availability • Institutional compatibility (backup)• Capacity/cost of the chip/flow cell
• Flexibility (reagent alternatives for platform)
• If purchasing • Cost of purchase/lease and maintenance
• Reagents
Factors to consider in vendor selection
Performance characteristics
•Accuracy
•Failure rate
•Ambiguities
•Gene coverage
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What is an acceptable failure rate?
Run Frequency
AcceptableFailure rate
TAT
2/week Highest 3-5 working days
Weekly Low 3-10 working days
Every 2 weeks
0 17-20 working days
Gene coverage
Gene coverage
Whole Gene Coverage
Exon 1 + Exon 2 to Intron 5
Exon 2 to Exon 4
HLA-A (3.1 kb)1 8765432UTR
UTR
1 65432
UTR
UTR
1 65432UTR
UTR
1 65432UTR
UTR
1 65432UTR
UTR
1 432UTR
UTR
1 65432
UTR
UTR
1 432UTR
UTR
1 5432
UTR
UTR
1 765432UTR
UTR
1 8765432UTR
UTR
HLA-B (3.4 kb)
HLA-C (3.4 kb)
HLA-DRB1 (3.7-4.8 kb)
HLA-DQB1 (3.7-4.1 kb)
HLA-DPB1 (5.0 & 5.7 kb)
HLA-DPA1 (4.7 kb)
HLA-DQA1 (5.4-5.8 kb)
HLA-DRB3 (3.8 kb)
HLA-DRB4 (0.4 & 1.3 kb)
HLA-DRB5 (4.0 kb)
NGSgo-AmpX amplification primer
Amplified exon
GENDX
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Gene coverage
Phasing best with long amplicons
Allele 1Allele 2
AT
GC
Allele 3Allele 4
TA
GC
No phasing with short amplicons if intervening sequence is the identical in both alleles
Phasing possible with long ampliconsA G
Every approach has advantages
Amplicon Length
Long Short
Phasing Best
Base call accuracy Best
PCR efficiency Best
Fragmented DNA Best
Long Short
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Practical factors to consider
• Packaging • Amenable to lab’s run volume• Flexibility for selecting loci
• Cost/sample• Software
• User friendliness• Features • Quality metrics
• Ease of use/robust
• Time/run• Platform constraints• Automation
Factors to consider in vendor selection
Support
•Technical
•Bioinformatics
•Validation
•Sales
The bells and whistles….
• Packaging • Amenable to lab’s run volume• Flexibility for selecting loci
• Cost/sample• Software
• User friendliness• Features • Quality metrics
• Ease of use/robust• Time/run• Platform constraints• Automation
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Fundamental NGS metrics
• Coverage• Depth (number of times a base call is made at a
given position)• Uniformity
• Quality Scores• Phred-like quality scores for each base call• Generated by platform-specific algorithms
Assuring the quality of next-generation sequencing in clinical laboratory practiceGargis et al Nature Biotech 2013
Quality Statistics: Depth of Coverage
EU: RUO, ROW: RUO NGSengine
Quality values (Phred-like scores)
• Historically developed to assess Sanger sequencing accuracy • Used multivariate lookup tables• Accurate across sequencing chemistires and instruments
• Algorithm for QV for NGS are system-specific• All QV scores logarithmically related to probability of base calling error
Q = -10 log10 P
Q Value Probability of Error
Accuracy
10 1 in 10 90%
20 1 in 100 99%
30 1 in 1,000 99.9%
40 1 in 10,000 99.99%
50 1 in 100,000 99.999%
SangerNGS
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Duke et al International Journal of Immunogenetics, 42:346-358, 27 JUN 2015Towards allele‐‐‐‐level human leucocyte antigens genotyping – assessing two next‐‐‐‐generation sequencing platforms: Ion Torrent Personal Genome Machine and Illumina MiSeq
Quality score diminishes with read length
PCR Artifact (PCR crossover, chimeric PCR)
Primer extension, but partial length
DenatureAnneal
Partial length product becomes primer for another allele or locus
Extension
Hybrid molecule
Allele imbalance
• PCR efficiency is influenced by DNA sequence
• Numerous factors can contribute to differences in amplification efficiency– Primer mismatch–Denaturation efficiency• GC content• GC clamp
–Sequences that can disrupt polymerase binding
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Noise
Homozygous2 alleles ~50%
Quality Statistics:
Percentage most frequent base call versus rest
Phasing
Allele 1Allele 2
AT
GC
Allele 3Allele 4
TA
GC
Phased A G
Reads
Not Phased
A
Long readsAccuracy
If exceeds length of all reads, phasing will not
be possible
Duke et al International Journal of Immunogenetics, 42:346-358, 27 JUN 2015Towards allele‐‐‐‐level human leucocyte antigens genotyping – assessing two next‐‐‐‐generation sequencing platforms: Ion Torrent Personal Genome Machine and Illumina MiSeq