Genome Biology for Programmers Lecture Series: Illumina Sequencing

Chris DaumJGI Illumina Group Lead

April 1, 2011

Outline

• Workflow Overview• Process Science

– Sample Prep & qPCR quantification– Cluster Generation– Sequencing

• Sequencer instruments: GA & HiSeq• Illumina Developments• Illumina quality & continuous improvement

Illumina Workflow

Sample Preparation

AnalysisAnalysisClustering SequencingSample Quantification

Sample PreparationLibrary Preparation – Main Goals:

• Prepares sample nucleic acids for sequencing

• Many library types and creation procedures exist

• However, all preparation results in the same general template structure:– Double-stranded DNA flanked by two different adapters– Variables include:

• Sequencing Application & Starting material (e.g. gDNA, mRNA, Mate Pair, Active Chromatin, ChIP-Seq)

• Insert Size• Adaptor type• Index for multiplexing

Example Sample Prep Workflow:TruSeq Paired-end Library

DNA RNA

Library Quantification - qPCR

• Real-time qPCR allows accurate quantification of DNA templates:

– qPCR is based on the detection of a fluorescent reporter molecule that increases as PCR product accumulates with each cycle of amplification

– By using primers specific to the Illumina universal adapters in a qPCR reaction containing library template, only cluster-forming templates will be amplified and quantified

Library Quantification - qPCR

Take home: qPCR mimics what is happening on the surface of the flowcell during cluster generation and allows for determining optimal loading concentrations.

Phases of qPCR: Geometric phase – amplicons doubling every cycle; greatest precision & accuracy for quantitation

Cq – Cycle of Quantification

Threshold of florescence for amplicon to produce a Cq

Cycl

e Th

resh

old

Log initial concentration

Plot Standard curve using controls and determine concentration of library

Cluster Generation

• Process occurs on cBot instrument:

– Aspirates DNA samples into flow cell

– Automates the formation of amplified clonal clusters from the DNA single molecules

– 1000x amplification generates clusters

– Hybridizes sequencing primer(s)

Illumina cBot• Cluster Generation 2.0

– Automated system significantly reduces workload for generation of flowcells

– Compact design saves lab space

– Reagent cartridge reduces prep time

Flowcell

Cluster Generation Prep• Prepare reagents and denature & dilute library:

• The goal is to have the perfect cluster density to maximize yield (bp), this is achieved via optimized loading concentrations as determined by qPCR

• Considerations:– Too low density: Fewer clusters, less sequence generated– Too high density: Overlapping clusters, removed by analysis filters, poor quality

Cluster Generation Chemistry

• Cluster generation Chemistry:– Hybridization– Amplification– Linearization– Blocking– Primer hybridization

Cluster Generation Chemistry

• Hybridize Sample fragments & extend:

Cluster Generation Chemistry• Bridge Amplification:

Cluster Generation Chemistry

• Linearization, Blocking & Sequencing Primer Hybridization:

Sequencing

• Main Goals:

– Translate the chemical information of the nucleotides into fluorescence information which can be captured optically

– The optical information is then transformed into text, which can be searched, aligned, or otherwise mined for biologically relevant data

Sequencing Workflow

HiSeq Run Type

Approx. Run Days

1x50 Flowcells 22x100 Flowcells 92x150 Flowcells 13

Sequencing by Synthesis

• Clustered Flowcell is loaded on Illumina sequencer:

Sequencing Chemistry: First Cycle Base Incorporation

• To initiate the first sequencing cycle, add all 4 fluorescently labeled reversible terminators and DNA polymerase enzyme to the flowcell.

• The complementary nucleotide will be added to the first position of each cluster.

• A laser is then used to excite the attached fluorophore.

Sequencing Chemistry: First Cycle Imaging

Sequencing Chemistry: Cycle 2 and so on…

Sequencing Read 2

• Resynthesis of second strand for Read 2 occurs on sequencer without removing flowcell:

Paired-End Sequencing: When performing a paired-end run, after the initial cycles (Read1), an additional cluster generation is performed on the analyzer, and the template is sequenced in the opposite direction, as depicted in the figures below.

Index for Multiplex Sequencing• Sample multiplexing involves 3 reads:

– A: Sample Read 1 is sequenced

– B: Read 1 product removed and Index Read is sequenced

– C: Template strand used to generate complementary strand, and sample Read 2 is sequenced

• Analysis software identifies the index sequence from each cluster so that the sample reads 1 & 2 can be assigned to single sample

Illumina HiSeq2000 Sequencer

Nifty Lights

HiSeq2000 Reagents

1 HiSeq = 2 GAs

HiSeq2000 Fluidics

Fluidics were the Achilles heel of the GA, and now 2X in the HiSeq

HiSeq2000 Fluidics

FY11 Service Metrics: Pareto

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Pareto: FY11 Service Requests

HiSeq: Temperature control

• 3 mechanisms:– Heat extraction via liquid coolant– Flow cell temperature control via Peltier– Maintain reagents temperature via cooled compartment

Flow cell sits on Peltier blocks, and is water cooled (heat extraction from underneath)

Reagent Chiller:• All reagents cooled at 4C• Condensation Pump runs every 4 min

for 30 sec

HiSeq Flowcell Loading

HiSeq Imaging

HiSeq Optics

HiSeq Lasers

HiSeq Software Interface

HiSeq Software Interface

HiSeq – Real Time Metrics

HiSeq vs GA

Cost & Throughput Comparison

Run Type 1x36 2x36 2x76 2x150 1x50 2x50 2x100 2x150

Seq Prep Reagents 2,292$ 4,012$ 4,012$ 4,012$ 2,442$ 3,747$ 3,747$ 3,737$

Seq Reagents 864$ 1,728$ 3,456$ 6,912$ 1,436$ 2,872$ 5,175$ 6,611$

Seq Prep & Seq Total 3,156$ 5,740$ 7,468$ 10,924$ 3,878$ 6,619$ 8,922$ 10,348$

Avg. Bases (Gb) 8.0 19.1 35.9 70.4 20.8 41.6 83.3 124.9

Avg. Reads (Millions) 222.2 265.0 236.3 234.6 416.0 416.0 416.4 416.3

Cost per lane 451$ 820$ 1,067$ 1,561$ 554$ 946$ 1,275$ 1,478$

Cost per 1 Gb 395$ 301$ 208$ 155$ 186$ 159$ 107$ 83$

Cost per Million reads 14$ 22$ 32$ 47$ 9$ 16$ 21$ 25$

GAIIx HiSeq

Notes:•Throughput metrics are averages from runs performed in FY11 for each of the run types to date•Italicized HiSeq Bases & Reads throughput metrics are estimates based on 2x100 run type since we have limited data on other run types•Only vendor reagent costs shown here; library creation and overhead costs are not included, but are roughly equal and are mostly independent of run type•Cost per million reads goes up with the longer run types, but the readlength increases as well and this makes each read more valuable for some assembly applications•HiSeq 2x150 run type not yet supported & the current HiSeq chemistry has worse quality beyond 80-100bases than compared to GA•The HiSeq platform is still new and we are experiencing a higher number of hardware failures than GA; Illumina does replace reagents for failed runs and we rerun failed flowcells immediately whenever possible.

HiSeq Development

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Coming in early Summer:

HiSeq Development

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HiSeq Development

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Introducing MiSeq

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MiSeq: all-in-one

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MiSeq: Fast, low throughput

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Providing Quality Sequence

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Illumina Process Metrics by week: Cluster & Run failure rates

Cluster Failures

Run failures

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FY10 Q3 Illumina Utilization

Problematic instruments with multiple run failures; 06 is being replaced & 07 had significant service work

Incident Reporting & Resolution (JIRA)

Instrument Status & real-time run monitoring

Instrument Utilization & Efficiency

Throughput Goals & Metrics

Failure Tracking & SPC Charts; RQC

Troubleshooting Procedures

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FY11 Cumulative Flowcells

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FC Goal

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Continuous Improvement - Lean Six Sigma

LLNL – Six Sigma Training

• Tools and methodologies to:– Improve work quality– Improve process efficiencies & eliminate waste– Improve employee and customer satisfaction

• Lean Six Sigma is about:– Eliminating waste and improving process flow– Focusing on reducing variation and improving process yield

by following a problem-solving approach using statistical tools

What is Six Sigma?• A Six Sigma process is literally one that’s

statistically 99.99966% successful.

• This is not always cost effective to achieve, so as a methodology it’s about gaining control of a process and implementing improvements.

What is Six Sigma?

• Six Sigma is a data driven problem solving approach where process inputs (Xs) are identified and optimized to impact the output (Y)

• The output is a function of the inputs and process– Y: Output– f: function– X: variables that must be controlled to consistently predict Y

Y = f(x)