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GenomeWebinar
March 27, 2019
How One NGS Core Lab Reduced Sequencing Costs with a Novel Library Normalization Kit
Julia Karow
GenomeWebTony Brooks
UCL GenomicsMida Pezeshkian
Swift Biosciences
Today’s Panelists
Julia Karow
Managing Editor,GenomeWeb(Moderator)
Tony Brooks
Applications Specialist,UCL Genomics
GenomeWebinar March 27, 2019
How One NGS Core Lab Reduced Sequencing Costs with a Novel Library Normalization Kit
Mida Pezeshkian
Product Manager,Swift Biosciences
Please submit any questions in the Q&A panel
GenomeWebinar March 27, 2019
How One NGS Core Lab Reduced Sequencing Costs with a Novel Library Normalization Kit
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GenomeWebinar March 27, 2019
How One NGS Core Lab Reduced Sequencing Costs with a Novel Library Normalization Kit
There are a series of widgets to enhance your webinar experience in the bottom tray of your window.
Applications Specialist,
UCL Genomics
Tony Brooks
GenomeWebinar March 27, 2019
How One NGS Core Lab Reduced Sequencing Costs with a Novel Library Normalization Kit
Swift Normalase
How One NGS Core Lab
Reduced Sequencing
Costs with a Novel
Library Normalization Kit
Tony Brooks – Senior Sequencing Application Specialist
UCL Genomics Core Facility
→ Collaborative research core facility (research
and clinical)
→ Fully economically costed
→ Project design to analysis
→ Four dedicated applications specialists
→ >50,000 samples per annum
→ >700 individual projects per annum (100%
increase in 2yrs)→ Latest genomic technologies and automation
→ Excellent partnerships and collaborations (ie
bioinformatics)
http://www.ucl.ac.uk/ich/research/genetics-genomic-medicine/[email protected] @uclgenomics
What does this mean in practice
35M reads per lane (1 Sample)
200-300M reads per lane (12-16 Samples)
10000M reads per lane (384 Samples)
Quantification of libraries is important
Absolute quantification
Avoid over/under-load of flow-cell
• Underload → Insufficient data / low sensitivity, poor consensus sequence & run failure• Overload → Poor base-call quality, low pass-filter, run failure
Quantification of libraries is important
Relative quantification
Avoid over/under sequencing samples in an experiment
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
7,000,000
8,000,000
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S24 S25 S26 S27 S28 S29 S30
n=30, CV = 26%
Methods for library quantification
Capillary Electrophoresis (Bioanalyzer / TapeStation)
+ Provides molarity (accounting for library size)
+ Detect presence of adaptor-dimer
- Slow (~30mins per 12 samples)
- Reproducibility?
- Inaccurate (±20% for TapeStation)
Methods for library quantification
Dye-based quantification (Qubit)
+ Very accurate, even at low concentrations
+ Very reproducible
- Slow (~60mins per 96 samples)
- Doesn’t account for library size (gives mass/volume)- No information about presence of dimer
Methods for library quantification
qPCR
+ Very accurate, even at really low concentrations (“Gold-Standard?”)+ Very reproducible
- Slow (~60mins per 24 samples (in triplicate) + 15 minutes analysis)
- Doesn’t account for library size- Requires very accurate pipetting and mixing (standard curve)
Pre-Normalase™ workflow – decision tree
Libraries
Qubit Quantification
Normalization
TapeStation
Qubit Quantification
Agreement ±10%?
No
qPCR
Yes
Pool (based on Qubit or qPCR)
Qubit Quantification
Sequence
Wetlab – Project Workflow by Time
Swift Normalase™
Full-length adaptors
R5 Reagent (modified P5/P7 primer mix) requires full-length adaptors
NEB Multiplex Oligos
Nextera (XT)
Agilent QXT
Illumina TruSeq LT/HT
IDT xGEN UMI/UDI adaptors
Kapa Single & Dual Indexes
Libraries
TapeStation QC (Confirm >12nM)
Normalase I
Pool Equal Volume (5µL
each)
Normalase II
Sequence
Example: 24 libraries in approximately 1 hour
Post-Normalase™ workflow – decision tree
Results
6 Kapa mRNA Hyper Prep assays / IDT adaptors on HiSeq 3000 lane
Results
(n=6) Kapa mRNA Hyper Prep / IDT xGen adaptors on HiSeq 3000 lane
Results
(n=12) NEB Low-Input / IDT adaptors on Partial NextSeq run
AUTOMATED NORMALASE I
Results
(n=32) Nonacus Cell3 Exome / NextSeq 500 Run
Results
(n=4) Nonacus Cell3 Exome Pools / NextSeq 500 Run
Results
(n=6) Kapa mRNA Hyper Prep / HiSeq 3000 Lane
Index 1 Index 2 Index 3 Index 4 Index 5 Index 6
Index 1 14.37 0.03 0.03 0.03 0.03 0.02
Index 2 0.05 17.53 0.06 0.04 0.03 0.03
Index 3 0.02 0.06 14.96 0.03 0.05 0.02
Index 4 0.03 0.04 0.08 16.32 0.03 0.11
Index 5 0.02 0.03 0.03 0.15 15.08 0.02
Index 6 0.03 0.02 0.02 0.03 0.04 13.49
Index hopping
Hopping rate: 1.20%
Undetermined: 7.06%
Swift Normalase™
Saving Money
With Normalase
TapeStation (n=24 @ $3/sample = $72)
Time (1hr @ $100/hr = $100)
Normalase Reagents (n=24 @ $7.5/sample = $180
Total: $352
$14.67/sample
Without Normalase
Qubit QC (n=24 @ $2/ sample = $48)
TapeStation (n=24 @ $3/sample = $72)
Time (4hrs @ $100/hr = $400)
Total: $520
$21.67/sample
$7 / sample saving
Summary
• Minimal hands-on time (≤ 10 minutes total)
• Normalase I works with automation
• Library balancing with typical CV < 10%
• Final pool ready to load on sequencer
• Removes the need to adjust for insert size
• Compatible with multiple preps (Illumina / Kapa / NEB / Nonacus)
• Low index hopping rates
• Less chances manual of error due to exact same workflow for each library
• Cost savings due to speed of processing
Product Manager,
Swift Biosciences
Mida Pezeshkian
GenomeWebinar March 27, 2019
How One NGS Core Lab Reduced Sequencing Costs with a Novel Library Normalization Kit
Questions?
Please enter your questions in the Q&A panel on your screen.
Julia Karow
GenomeWebTony Brooks
UCL Genomics
GenomeWebinar March 27, 2019
How One NGS Core Lab Reduced Sequencing Costs with a Novel Library Normalization Kit
Mida Pezeshkian
Swift Biosciences
Thank you for your participation!
Please be sure to fill out our post-webinar survey to let us know how we did!
GenomeWebinar March 27, 2019
How One NGS Core Lab Reduced Sequencing Costs with a Novel Library Normalization Kit