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The world leader in serving science
Mohit Gupta
Targeted Sequencing for All on Ion GeneStudioTM S5 Sequencers: GPUs Make it happen
For Research Use Only. Not for use in diagnostic procedures.
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Why Targeted Sequencing More cost effective, more time efficient and simpler to analyze
Targeted Sequencing Whole Exome Whole Genome
Variants generated per run 10s to 100s ~50,000 ~3,000,000
Likely number of variants for follow-up 1-10s 1-10s 1-10s
Time to analyze Hours to Days Days to Weeks Weeks to Months
Total cost including analysis $ $$ $$$
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Sequencing turnaround time is highly critical
Infectious Disease Research
Ecoli Ebola Zika
Aneuploidy Detection
Pre-implantation Genetic Screening
Cancer Research
Liquid Biopsy
Forensics Research
Crime Scene
Inherited Disease
Research
Exome Trio
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NGS Oncology Research Solutions: Targeted Sequencing with quick turnaround time
Oncology research specimen
Routine research Translational
FFPE-based solutions
Liquid biopsy solutions
Immuno-oncology solutions
+ +
Full characterization
of oncology Research samples
Oncomine™ Focus Assay
Oncomine™ Comprehensive
Assay
Oncomine™ BRCA Research
Assay
Oncomine™ Pan-Cancer Cell-
Free Assay
Oncomine™ cfDNA Assays
for Lung, Breast, Colon
Oncomine™ Tumor Mutation
Load Assay
Ion AmpliSeq™ Immune
Repertoire Assay Plus, TCR β
Oncomine™ Immune
Response Research Assay
Heme-oncology solutions
Liquid biopsy solutions
Immuno-oncology solutions
Oncomine™ Lung cfTNA Research
Assay
Oncomine™ Breast cfDNA
Research Assay v2
FFPE-based solutions
Heme-oncology solutions
+
Oncomine™ Myeloid
Research Assay
New Heme Assay
(in develoment)
The content provided herein may relate to products that have not been officially released and are subject to change without notice.
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Low Cost, Simple, Scalable, Real Time Sequencing
Wafer Semiconductor Manufacturing
Chip Semiconductor Packaging
Millions of Sensors
Semiconductor Design
Sensor Plate
Silicon Substrate Drain Source Bulk
∆ V
Sensing Layer
H+
Single Sensor Chemical to Digital Sequence
TCGTACC…
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Sensor Plate
Silicon Substrate Drain Source Bulk
dNTP
To column receiver
∆ pH
∆ Q
∆ V
Sensing Layer
H+
RothbergJ.M.etalNaturedoi:10.1038/nature10242
Transistor as a pH meter
Compute Intensive signal processing
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Scaling Well Density with Ion Chip Generations
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Ion NGS Evolution
Hard to Use
Easy to Use
2010 2012 2014 2016
Ion Chef
Ion Proton Ion PGM
Ion S5TM
Ion S5TM XL System
2018
Ion GeneStudio™ S5
Series
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Ion GeneStudioTM S5 Series
Ion GeneStudioTM S5 Ion GeneStudioTM S5 Plus
Ion GeneStudioTM S5 Prime
Ion 510™ Chip 2–3 M reads Up to 400 bp
Ion 520™ Chip 3–6 M reads Up to 600 bp
Ion 530™ Chip 15–20 M reads Up to 600 bp
Ion 540™ Chip 60–80 M reads Up to 200 bp
Ion 550™ Chip 100–130 M reads
Up to 200 bp
Fast. Flexible. Powerful.
* Throughputs based on 200bp sequencing
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Internal Data Shows Reliable Output from 2 to 130 M Reads
Number of runs 8 8 5 6 9
Mean read length 203 203 203 181 169
Raw read accuracy 99.5% 99.5% 99.5% 99.02% 99.1%
0
5
10
15
20
25
0
20
40
60
80
100
120
140
510 520 530 540 550
Thro
ughp
ut (G
b)
Rea
ds (M
)
total reads Throughput
*Ion 510, 520 and 530 Chips sequenced 1/6th of AmpliSeq™ Exome pool **Ion 540 and 550 sequenced complete AmpliSeq Exome pool
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Maximum Sequencing Output Per Day and Turn-Around Time
* Based off 540 chip – sequencing (2.5 hours) and analysis (varies) time
Output (max/day): 15 Gb/80 M 30 Gb/160 M 50 Gb/260 M
Chips (max/day): 1 x 540 2 x 540 or 1 x 550 2 x 550
Speed* 19 hrs 10 hrs 6.5 hrs
Ion GeneStudio™ S5 Ion GeneStudio™ S5 Plus
Ion GeneStudio™ S5 Prime
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Data Processing Pipeline
Data acquisition
and compression
Signal
Processing BaseCalling, Alignment
and Variant Calling
1 TB 300 GB
20 TB (Ion 550TM chip)
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GPU to the rescue
• Removed main hotspot in signal processing pipeline • Speedups of 270x over a CPU core!
0 20 40 60 80 100 120 140 160
CPU
GPU
time in s
bead find
CPU processing first 20 flows
per block CPU processing after flow 20 time spent in fitting
…
…
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GPU’s Impact
• Multiple sequencing runs a day generating • 260M 200bp reads with Ion 550TM chip (Nvidia Quadro P5000)
• Swift pace of Research and Development • Accelerated product innovation • Lower cost per sample for applications like Liquid Biopsy and NIPT
with GPU
CPU only
On Instrument Analysis Time with and without GPU
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Signal Processing
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Signal Processing Flow
Reading flow data
Writing signal values
Raw Data Processing
Post Fit Processing
Parameter Estimation
unique to each well
Regional Parameter Estimation
(Common to all wells)
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Mathematical model
• Sophisticated model • Background correction • Incorporation • Buffering
• Regional Parameters • Enzyme kinetics, nucleotide rise,
diffusion etc.
• Well Parameters • Hydrogen ions generated,
buffering, DNA copies etc.
Decay in H+ Incorporation
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GPU Acceleration
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Pipeline Execution Model
• Multiple Concurrent Processes
• 96 blocks • depending on hardware 4 to 6
processes in parallel • work on available data during
experiment
*Heat-map and timing from a S5TM XL 540TM with Nvidia Tesla K40 GPU
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BkgModel Worker BkgModel Worker BkgModel Worker BkgModel Worker BkgModel Worker BkgModel Worker Thread
ImgLoader ImgLoader ImgLoader Raw Data Loader Thread
CPU Queue
Gen Traces
1 36
RegionFit PostFit Xtalk/Clonal
6
ImgLoader ImgLoader ImgLoader 1.well writer
accumulate traces for 20 flows
1
sync
Single Flow Fit
bead
s
frames flows (20)
copy
…
CPU Execution Model
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GPU memory
frames
flows (20)
…
beads
host memory
bead
s
frames flows (20)
copy
BkgModel Worker BkgModel Worker BkgModel Worker BkgModel Worker BkgModel Worker BkgModel Worker Thread
ImgLoader ImgLoader ImgLoader Raw Data Loader Thread
CPU Queue
Gen Traces
1 36
RegionFit PostFit Xtalk/Clonal
GPU Queue
6
ImgLoader ImgLoader ImgLoader 1.well writer
GPU Worker
StreamManager SEU
StreamEU
GPU transp. Input
SingleFlowFit
transp. Output
accumulate traces for 20 flows
36
1
sync
page locked memory
… frames
flows (20)
bead
s
GPU Accelerated Pipeline
…
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GPU Execution Model
• Stream based execution allows for overlap of • Kernel execution • PCIe transfers • host side data reorganization • CPU pre and post processing
• Queue system for heterogeneous execution • Balances execution of different tasks between
CPU and GPU
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GPU Speedup with different architectures – Fitting Algorithm
Algorithm Speedup over a single CPU core
129
185
223
273
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Raw Data Explosion with Ion Chip Generations
• Limited On-Instrument storage • Disk writes become huge bottlenecks
*Based on 500 flow run acquired at 15fps for 7s
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• Sequencing raw data undergoes several compression stages before it hits the disk
• Variable Frame Rate: Heavy averaging before nucleotide hits the well and during wash step
• T0: Adaptive change after VFC to tightly compress data around the timepoint nucleotide hits the well
• PCA: Reduced basis using orthogonal transformation to capture maximum variance in the data
• Delta: Store the difference from the previous frame
Raw Data Compression
Variable Frame Rate
Compression (VFC)
T0 Compression
PCA Compression
Four-fold reduction
Delta Compression
Variable Frame Rate Compression
(VFC)
T0 Compression
PCA Compression
Delta Compression
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PCA Compression on Ion 550TM raw data
Statistical Procedure that uses an orthogonal transformation to convert set of linearly correlated observations into a set of linearly uncorrelated variables
PCA Compression flow for well traces. The final loadings are used to get back the traces in chosen eigenvector space.
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Sample Throughput for different applications on Ion 550TM Chip
• Four-fold (4TB -> 1TB) reduction in data allows to double sample throughput for different applications
• Potentially sample throughput can be quadrupled • Instrument has to be re-initialized after two runs (Reagents run out) • Turnaround time might be more than 24 hrs if not connected to compute cluster
8
32 32 32
16
Inherited Disease Gene Expression Oncology (Solid Tumor)
Oncology (Liquid Biopsy)
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• We have peaked the timing improvement from GPU acceleration for current algorithms • Out of the box thinking in algorithms • GPU Friendly algorithms
• Variant Calling • Core Algorithm to evaluate every candidate variant can be GPU accelerated • Will further shorten the sample to variant workflow
Future Directions
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Thank You
NVIDIA specifically the DevTech Team
My supervisor Eugene Ingerman
and The entire Ion Torrent R&D team
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