How One NGS Core Lab Reduced Sequencing Costs with a Novel ... · Mida Pezeshkian Product Manager,...

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

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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/ucl-genomicsichgenomics@ucl.ac.uk @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