1. HPC Solutions for Life Science Research
Glen Otero, Ph.D.
Bioinformatics & NGS Solution Architect
HPC & Research Computing

2. Accelerating Individual Treatments for Pediatric Cancer
Partnership
Accelerating Individual Treatments for Pediatric Cancer

3. Precision Medicine At Work Today Against “Adult” Cancers
Look inside cancer cells, identify specific vulnerabilities and customize a treatment
Once the molecular-mechanisms of disease are aligned with the right targeted drug we
can dramatically improve patient survival.
We will do this for children with neuroblastoma
Examples of Precision Medicine
Leukemia
Gene Target: ECR-ABL
30% increase in survival rate
41 yrs from indentified cause to targeted treatment
Breast Cancer
Gene Target: HER2
20% increase in survival rate
17 yrs from identified cause to targeted treatment
Melanoma
Gene Target: BRAF
52% response rate and increased survival by six months
10 yrs from identified cause to treatment
Lung Cancer
Gene Target: EGFR
Partial or complete response in 93% of patients with DNA mutation
9 yrs from identified cause to treatment
Medical imaging proof of dramatic tumor reduction after 8 weeks on personalized treatment regimen.
The Good News: More drugs coming, a knowledge explosion, technology advances
The Bad News: Increasing drug development and (hence) healthcare costs, more drugs coming, knowledge-clinical utility gap
N Engl J Med Sep 17;361(12):

4. Why Pediatric Cancer? Neuroblastoma (NB) Pediatric Oncology
Leading cause of disease-related death in children ages 1-14
A child diagnosed with cancer every hour
Cure rates have not improved in the last decade
Pharmaceutical companies fund 60% of adult oncology research and virtually no pediatric cancer research
Neuroblastoma (NB)
Worst clinical outcome of any pediatric cancer
5% chance of cure in children with advanced cancer
Even with remission, the cancer often returns and is untreatable
Despite “modest market”….Let’s provide hope to those who have none while establishing a scalable pipeline for precision medicine
Complexity of cancer: Cancer isn’t just one disease, its hundreds, looking at biology of specific tumor and treating it directly.
Layers of benefit:
Effect care delivery: Each cancer genome is different, rather than treat every kid with NB the same, molecular understanding of disease for custom treatment (position existing drug)
Effect Basic Research: measurements from multiple research, create db to mine, for new drug development for ped
Dell Cloud – drug development, as discover + match molecular features to known drugs, design clinical trials locally and recruit patients

5. Dell & TGen collaboration will result in...
More children offered a chance at a cure for cancer
100% increase in patients treated the first year and a future global platform to treat 100K+ children and adults

6. Applying Precision Treatments for Pediatric Cancer
Neuroblastoma: 15 days for personalized treatment
Patient / Physician diagnosis, treatment, ongoing management
Tumor Sample
Complete molecular
characterization of the diseased tumor
Analytical tool for mapping patient data against database for recommended treatment
Integration of
scientific & clinical evidence for future research
Treatments with a more reasonable chance of a cure
5 business day turnaround – built for scale using current technologies and approaches but….
Overlay Dell capabilities including social media aspect
Workflow
Patient/physician interaction – Educate Patients
Tumor analyzed using next generation sequencing
Dell cloud with infrastructure & processes will act as backend for all data that gets generated at every level of the workflow, every application will be optimized to work with the cloud –data gets integrated, archived
The way cancer develops, there is variability between individuals. What weren’t able to do before is look inside cell and see the differences at the patient level, to determine a custom treatment
2-week time frame (nearly real-time) from biopsy to treatment recommendation – industrialized to scale, set period of time, because some patients don’t have 6 + weeks to wait. Without error with a lot of fidelity
What is the milestone for the sick child? Minimizing trial and errors, chances are these kids wont see treatment that gives them any reasonable chance of a cure
Minimizing trial and errors
Understanding the individual disease
Accelerating targeted treatment options
Creating platform to scale to 100k+ patients
Archival Storage
Analytics HPCC

7. Genomics Data Processing Pipeline

8. TGen RNA-Seq Data AnalysisQC
Visualize average q-score for each sequencing cycle
Trim
Trim off bases starting with the under-performing cycles
Align to Genome
Using Bowtie
Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Map Splice Junctions
Using Tophat
Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq.
Assemble Isoforms and Estimate Abundances
Trapnell C, Williams BA, Pertea G, Mortazavi AM, Kwan G, van Baren MJ, Salzberg SL, Wold B, Pachter L. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation.
Using Cufflinks
Timeline Comparison and Goals
Visualize Regions of Interest
Using IGV
The Broad Institute, Development: Jim Robinson, Helga Thorvaldsdóttir, Marc-Danie Nazaire, Documentation: Judy McLaughlin, PI: Jill Mesirov

9. TGen RNA-Seq Performance
Data analysis time was 7 days. Goal was 5 days.
Worked with TGen to parallelize their RNA-Seq pipeline
Confidential

10. M420s Energy Efficiency Application PowerEdge R820 @ 2.7 GHz
4 servers, 128 cores
PowerEdge 2.3 GHz
32 servers, 512 cores
HPL
EE: 4% higher
Best EE: 15% higher LU
Best EE: ~6% higher
Similar EE to PowerEdge M620
WRF
EE: 12% lower
Best EE: ~13% higher
ANSYS Fluent
Best EE for truck_poly_14m (+16%)
Best EE for truck_111m (+23%)
MILC
EE: ~18% lower
Best EE: ~37% higher
NAMD
(Issues when executing program)
Better EE: ~5 % higher
* EE is shorthand for energy efficiency. The PowerEdge M620 cluster 2.7 GHz) is used as the baseline for comparison. Higher is better for EE.
Confidential

11. Why the M420s 48GB on M420 and 64GB on M620
130, 115 & 95 Watt Processors
20% more GFLOPS/$ with M620s
64% more GFLOPS/U with M420s
15% more GFLOPS/W with M420s
Confidential

12. TGen RNA-Seq Performance
Data analysis time was 7 days. Goal was 5 days.
Worked with TGen to parallelize their RNA-Seq pipeline
One 10U chassis of 32 x M420s can complete RNA-Seq pipeline in 4 hours.
Confidential

13. TGen RNA-Seq Performance
Data analysis time was 7 days. Goal was 5 days.
Worked with TGen to parallelize their RNA-Seq pipeline
One 10U chassis of 32 x M420s can complete RNA-Seq pipeline in 4 hours.
5 days hoped for. 4 hours delivered.
Confidential

14. The Dell Scalable Unit (DSU) for Life Sciences: SANGER (Sequence Analysis ‘N’ GEnomics Research)
Glen Otero, Ph.D.
Life Sciences HPC Solution Architect
Dell | Research Computing Solutions
Mark R. Fernandez, Ph.D.
HPC Computer Scientist
Dell | Research Computing Solutions
Jeff Layton, Ph.D.
HPC Enterprise Technologist
Dell | Research Computing Solutions

15. SANGER
2U Plenum
Actual placement in racks may vary.
NSS-HA Pair
NSS User Data
HSS Metadata Pair
HSS OSS Pair
HSS User Data
Infrastructure:
Dell PE, PC & F10
Challenge: Experiment processing takes too long and delays patient treatment
Solution: Dell NGS Cluster Appliance
Single Rack Solution
9 Teraflops of Sandy Bridge Processors
Lustre File Storage
Intel SW tools
Benefits: Includes everything you need for NGS - compute, storage, software, networking, infrastructure, installation, deployment, training, service & support
Dell NSS (NFS)
(up to 180TB)
Dell HSS (Lustre)
(up to 360TB)
M420 (Compute)
(up to 32 nodes)

16. Genomic Research - Customer Challenges
Data explosion
Data management, access, migration are challenging
Large data and compute requirements
Processing required measured in days and weeks
Resource requirements inhibit clinical / commercial viability
Innovation, iteration and refinement
Genomic software and pipelines rapidly evolving
Rich set of open source, shareware, and commercial applications
Cumbersome infrastructure
Costly, complex deployment, overbearing maintenance
Shared, limited-access resources
Confidentiality and privacy concerns
Biomedical research facilities
University laboratories and core facilities
Government research labs
Non-profit (private) research institutes
Industrial R&D labs in agriculture, energy, pharmaceuticals, biotechnology, chemical
Bioinformatic software development / genomic data services companies
Systems integrators serving end user customers above with turnkey services and solutions
Vertically-oriented cloud service providers serving end user customers with turnkey service
April 23, 2013
NHGRI symposium commemorates 10th anniversary of the Human Genome Project
NHGRI Director Eric Green, M.D., Ph.D. reminded the audience that generating the first human genome sequence required six to eight years of active sequencing and cost about $1 billion. But advances in DNA sequencing technologies have reduced both the cost and the time required to sequence a human genome to just a few thousand dollars and a few days, respectively.
Sequence
Align
Identify Variants
Visualize
Define Therapy
Archive

17. whole genome pipeline analysis overview
High level description of the pipeline and how things are parallelized.

18. SANGER whole genome analysis info
A variant calling pipeline on public NA12878 data from Illumina's Platinum Genome project:
Aligner: bwa; variant caller: GATK
Reference Genome: GRCh37 (Genome Reference Consortium Human build 37)
Software pipeline framework:
How to install pipeline framework:
How to run the whole genome pipeline:
Config file:
Input files
ftp.sra.ebi.ac.uk/vol1/fastq/ERR091/ERR091571/ERR091571_1.fastq.gz
ftp.sra.ebi.ac.uk/vol1/fastq/ERR091/ERR091571/ERR091571_2.fastq.gz
Number of reads: 212 million (~10x coverage)
Separate trials conducted by running 1, 6 and 30 genomes analyses simultaneously
Key points to note here are: 1) genome coverage of data set is only 10x. Not shabby, but most analyses are 30x. 2) data is publicly available; 3) software is open source

19. SANGER genomics workload performance
12 human genomes (30x) processed per day
Consume kWh per genome
Open
Integrated
Efficiency Performance
Architected for genomic workloads
Service and
Support
Performance metrics above based on May 2013 internal Dell HPC lab benchmark testing (SW: bwa, GATK, bcbio-nextgen Ref. Gehome: GRCh37 Input reads: 212M, ~10X coverage), confirming 1) genomes per day throughput on an Active Infrastructure for HPC Life Sciences configuration across 480 cores 2) confirming 38.9 genomes per day throughput compared to 2.01 genomes per day across 16 cores. 3) the complete analysis of a genome in 6.8 hours across 96 cores 4) energy consumption of kW when running 30 concurrent genome analyses, resulting in kilowatts/genome ( kW/30 genomes). 5) confirmed sustained 8.26 Tflops, 88% of a theoretical maximum of 9.4 Tflops, based on May 2013 internal Dell HPC lab benchmark testing. Actual performance will vary based on configuration, usage and manufacturing variability.
Confidential

20. SANGER Realize genomics R&D potential
Best-in-class density and energy efficiency
Open, factory integrated HPC infrastructure
World class support and expertise
Leverage Dell domain expertise and complete solutions
Be in production faster
Reduce time to market
Identify treatments in clinically relevant timeframes
Innovate
Confidential

21. SANGER available in Design Solution Center for testing
Confidential

22. Thanks!
Intel
Ketan Paranjape, Kristina Kermanshahche, John O’Neill, Drew Peterson, Ed Kurtzer
Terascala
Ben Rosen, Larry Bazinett
Bright Computing
Matthijs van Leeuwen, Craig Reagan
TGen
James Lowey, Nelson Kick, Jason Corneveaux, Matt Huentelman
Dell
Jeff Layton, Mark Fernandez, Onur Celebioglu, Nishanth Dandapanthula, Will Cottay, Neil Klosterman, Christine Fronczak
Harvard
Brad Chapman, Oliver Hofmann, Rory Kirchner, John Morrissey

23. NGS that drives clinical actions in real time
TGen pediatric cancer trial
TGen Center for Rare Childhood Disorders

24. Dell and TGen take the fight against pediatric cancer personally
Dell and TGen take the fight against pediatric cancer personally... one child at a time
Glen Otero, Ph.D.
Bioinformatics & NGS Solution Architect
HPC & Research Computing