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“Discovering Yourself with
Computational Bioinformatics”
Rutgers Discovery Informatics Institute (RDI2
) Distinguished Seminar
Rutgers University
New Brunswick, NJ
May 9, 2013
Dr. Larry Smarr
Director, California Institute for Telecommunications and Information
Technology
Harry E. Gruber Professor,
Dept. of Computer Science and Engineering
Jacobs School of Engineering, UCSD
1
Abstract
For over a decade, Calit2 has had a driving vision that healthcare is being
transformed into “digitally enabled genomic medicine.” Combined with
advances in nanotechnology and MEMS, a new generation of body sensors is
rapidly developing. As these real-time data streams are stored in the cloud,
cross population comparisons becomes increasingly possible and the
availability of biofeedback leads to behavior change toward wellness. To put a
more personal face on the "patient of the future," I have been increasingly
quantifying my own body over the last ten years. In addition to external markers
I also currently track over 100 blood biomarkers and dozens of molecular and
microbial variables in my stool. Using my saliva 23andme.com obtained 1
million single nucleotide polymorphisms (SNPs) in my human DNA. My gut
microbiome has been metagenomically sequenced by the J. Craig Venter
Institute, yielding 25 billion DNA bases. I will show how one can discover
emerging disease states before they develop serious symptoms using this Big
Data approach. Hundreds of thousands of supercomputer CPU-hours were
used in this voyage of self-discovery.
Where I Believe We are Headed: Predictive,
Personalized, Preventive, & Participatory Medicine
www.newsweek.com/2009/06/26/a-doctor-s-vision-of-the-future-of-medicine.html
I am Lee Hood’s Lab Rat!
Calit2 Has Been Had a Vision of
“the Digital Transformation of Health” for a Decade
• Next Step—Putting You On-Line!
– Wireless Internet Transmission
– Key Metabolic and Physical Variables
– Model -- Dozens of Processors and 60 Sensors /
Actuators Inside of our Cars
• Post-Genomic Individualized Medicine
– Combine
–Genetic Code
–Body Data Flow
– Use Powerful AI Data Mining Techniques
www.bodymedia.com
The Content of This Slide from 2001 Larry Smarr
Calit2 Talk on Digitally Enabled Genomic Medicine
The Calit2 Vision of Digitally Enabled Genomic Medicine
is an Emerging Reality
5
July/August 2011 February 2012
LifeChips: the merging of two major industries, the
microelectronic chip industry with the life science
industry
LifeChips medical devices
Lifechips--Merging Two Major Industries:
Microelectronic Chips & Life Sciences
65 UCI Faculty
Temporary Tattoo Biosensors
Can Measure pH and Lactate in Sweat
www.jacobsschool.ucsd.edu/news/news_releases/release.sfe?id=1353
From the UCSD Jacobs School of Engineering
Laboratory for Nanobioelectronics-Prof. Joe Wang
CitiSense –UCSD NSF Grant for Fine-Grained
“Exposome” Sensing Using Cell Phones
CitiSenseCitiSense
contributecontribute
distributedistribute
sense
sense
““display”
display”
discover
discover
retrieve
retrieve
Seacoast Sci.
4oz
30 compounds
EPA
CitiSense Team
PI: Bill Griswold
Ingolf Krueger
Tajana Simunic Rosing
Sanjoy Dasgupta
Hovav Shacham
Kevin Patrick
C/A
L
S
W
F
Intel MSP
CitiSense Atmospheric Sensor Platform:
Sensors Will Miniaturize and Diversify
www.jacobsschool.ucsd.edu/news/news_releases/release.sfe?id=1353
By Measuring the State of My Body and “Tuning” It
Using Nutrition and Exercise, I Became Healthier
2000
Age
41
2010
Age
61
1999
1989
Age
51
1999
I Arrived in La Jolla in 2000 After 20 Years in the Midwest
and Decided to Move Against the Obesity Trend
I Reversed My Body’s Decline By
Quantifying and Altering Nutrition and Exercise
http://lsmarr.calit2.net/repository/LS_reading_recommendations_FiRe_2011.pdf
Challenge-Develop Standards to Enable MashUps
of Personal Sensor Data Across Private Clouds
Withing/iPhone-
Blood Pressure
Zeo-Sleep
Azumio-Heart Rate
EM Wave PC-
Stress
MyFitnessPal-
Calories Ingested
FitBit -
Daily Steps &
Calories Burned
From Measuring Macro-Variables
to Measuring Your Internal Variables
www.technologyreview.com/biomedicine/39636
From One to a Billion Data Points Defining Me:
The Exponential Rise in Body Data in Just One Decade!
Billion: My Full DNA,
MRI/CT Images
Million: My DNA SNPs,
Zeo, FitBit
Hundred: My Blood VariablesOne:
My WeightWeight
Blood
Variables
SNPs
Microbial Genome
Improving Body
Discovering Disease
Visualizing Time Series of
150 LS Blood and Stool Variables, Each Over 5-10 Years
Calit2 64 megapixel VROOM
Only One of My Blood Measurements
Was Far Out of Range--Indicating Chronic Inflammation
Normal Range<1 mg/L
Normal
27x Upper Limit
Antibiotics
Antibiotics
Episodic Peaks in Inflammation
Followed by Spontaneous Drops
Complex Reactive Protein (CRP) is a Blood Biomarker
for Detecting Presence of Inflammation
High Values of Lactoferrin (Shed from Neutrophils)
From Stool Sample Suggested Inflammation in Colon
Normal Range
<7.3 µg/mL
124x Upper Limit
Antibiotics Antibiotics
Typical
Lactoferrin
Value for
Active
IBD
Stool Samples Analyzed
by www.yourfuturehealth.com
Lactoferrin is a Sensitive and Specific Biomarker for
Detecting Presence of Inflammatory Bowel Disease (IBD)
Descending Colon
Sigmoid Colon
Threading Iliac Arteries
Major Kink
Confirming the IBD (Crohn’s) Hypothesis:
Finding the “Smoking Gun” with MRI Imaging
I Obtained the MRI Slices
From UCSD Medical Services
and Converted to Interactive 3D
Working With Calit2er Jurgen
Schulze’s DeskVOX Software
Transverse Colon
Liver
Small Intestine
Diseased Sigmoid Colon
Cross Section
MRI Jan 2012
An MRI Shows Sigmoid Colon Wall Thickened
Indicating Probable Diagnosis of Crohn’s Disease
Why Did I Have an Autoimmune Disease like IBD?
Despite decades of research,
the etiology of Crohn's disease
remains unknown.
Its pathogenesis may involve
a complex interplay between
host genetics,
immune dysfunction,
and microbial or environmental factors.
--The Role of Microbes in Crohn's Disease
Paul B. Eckburg & David A. Relman
Clin Infect Dis. 44:256-262 (2007) 
So I Set Out to Quantify All Three!
I Wondered if Crohn’s is an Autoimmune Disease,
Did I Have a Personal Genomic Polymorphism?
From www.23andme.com
SNPs Associated with CD
Polymorphism in
Interleukin-23 Receptor Gene
— 80% Higher Risk
of Pro-inflammatory
Immune Response
NOD2
ATG16L1
IRGM
Now Comparing
163 Known IBD SNPs
with 23andme SNP Chip
Crohn’s May be a Related Set of Diseases
Driven by Different SNPs
Me-Male
CD Onset
At 60-Years Old
Female
CD Onset
At 20-Years Old
NOD2 (1)
rs2066844
Il-23R
rs1004819
Autoimmune Disease Overlap
from SNP GWAS
Gut Lees, et al.
60:1739-1753
(2011)
Imagine Crowdsourcing 23andme SNPs
For Even a Small Portion of Crohnology!
www.crohnology.com
But the Human Genome Contains
Less Than 1% of the Bodies Genes
http://commonfund.nih.gov/hmp/
The Total Number of These Bacterial
Cells is 10 Times the Number
of Human Cells in Your Body
But How Can You Determine
Which Microbes Are Within You?
“The emerging field
of metagenomics,
where the DNA of entire
communities of microbes
is studied simultaneously,
presents the greatest opportunity
-- perhaps since the invention of
the microscope –
to revolutionize understanding of
the microbial world.” –
National Research Council
March 27, 2007
NRC Report:
Metagenomic
data should
be made
publicly
available in
international
archives as
rapidly as
possible.
Infrastructure Services Extend
CAMERA Computations to
3rd
Party Compute Resources
Infrastructure Services Extend
CAMERA Computations to
3rd
Party Compute Resources
NSF/SDSC
Gordon
UCSD Triton
NSF/SDSC
Trestles
NSF/RCAC
Steele
NSF/TACC
Lonestar
NSF/TACC
Ranger
Core CAMERA HPC
Resource
Calit2 Community Cyberinfrastructure for Advanced
Microbial Ecology Research and Analysis (CAMERA)
Source:
Jeff Grethe,
CRBS, UCSD
>5000 Users
>90 Countries
CAMERA and NIH Funded Weizhong Li Group’s Metagenomic
Computational NextGen Sequencing Pipeline
Raw readsRaw reads
Reads QC
HQ reads:HQ reads:
Filter human
Bowtie/BWA against
Human genome and
mRNAs
Bowtie/BWA against
Human genome and
mRNAs
Unique readsUnique reads
CD-HIT-Dup
For single or PE reads
CD-HIT-Dup
For single or PE reads
Further filtered
reads
Further filtered
reads
Filtered readsFiltered reads
Filter duplicate
Cluster-based
Denoising
Cluster-based
Denoising
ContigsContigs
Assemble
Velvet,
SOAPdenovo,
Abyss
-------
K-mer setting
Velvet,
SOAPdenovo,
Abyss
-------
K-mer setting
Contigs with
Abundance
Contigs with
Abundance
Mapping
BWA BowtieBWA Bowtie
Taxonomy binningTaxonomy binning
Filter errorsRead recruitment
FR-HIT against
Non-redundant
microbial genomes
FR-HIT against
Non-redundant
microbial genomes
VisualizationVisualization
FRV
tRNAs
rRNAs
tRNAs
rRNAs
tRNA-scan
rRNA - HMM
ORFsORFs
ORF-finder
Megagene
Non redundant
ORFs
Non redundant
ORFs
Core ORF clustersCore ORF clusters
Cd-hit at 95%
Cd-hit at 60%
Protein familiesProtein families
Cd-hit at 30% 1e-6
Function
Pathway
Annotation
Function
Pathway
Annotation
Pfam
Tigrfam
COG
KOG
PRK
KEGG
eggNOG
Pfam
Tigrfam
COG
KOG
PRK
KEGG
eggNOG
Hmmer
RPS-blast
blast
PI: (Weizhong Li, UCSD):
NIH R01HG005978 (2010-2013, $1.1M)
We Used SDSC’s Gordon Data-Intensive Supercomputer
to Analyze a Wide Range of Gut Microbiomes
• Analyzed Healthy and IBD Patients:
– LS, 13 Crohn's Disease &
11 Ulcerative Colitis Patients,
+ 150 HMP Healthy Subjects
• Gordon Compute Time
– ~1/2 CPU-Year Per Sample
– > 200,000 CPU-Hours so far
• Gordon RAM Required
– 64GB RAM for Most Steps
– 192GB RAM for Assembly
• Gordon Disk Required
– 8TB for All Subjects
– Input, Intermediate and Final Results
Enabled by
a Grant of Time
on Gordon from
SDSC Director Mike Norman
Venter Sequencing of
LS Gut Microbiome:
230 M Reads
101 Bases Per Read
23 Billion DNA Bases
2012 Was
the Year of Human Microbiome
When We Think About Biological Diversity
We Typically Think of the Wide Range of Animals
But All These Animals Are in One SubPhylum Vertebrata
of the Chordata Phylum
All images from Wikimedia Commons.
Photos are public domain or by Trisha Shears & Richard Bartz
Think of These Phyla of Animals When
You Consider the Biodiversity of Microbes Inside You
All images from WikiMedia Commons.
Photos are public domain or by Dan Hershman, Michael Linnenbach, Manuae, B_cool
Phylum
Annelida
Phylum
Echinodermata
Phylum
Cnidaria
Phylum
Mollusca
Phylum
Arthropoda
Phylum
Chordata
Most Biological Diversity on Earth
is in the Microbial World
Source: Carl Woese, et al
Last Slide
Evolutionary Distance Derived from
Comparative Sequencing of 16S or 18S Ribosomal RNA
Red Circles Are Dominate
Human Gut Microbes
June 8, 2012 June 14, 2012
Intense Scientific Research is Underway
on Understanding the Human Microbiome
From Culturing Bacteria to Sequencing Them
To Map My Gut Microbes, I Sent a Stool Sample to
the Venter Institute for Metagenomic Sequencing
Gel Image of Extract from Smarr Sample-Next is Library Construction
Manny Torralba, Project Lead - Human Genomic Medicine
J Craig Venter Institute
January 25, 2012
Shipped Stool Sample
December 28, 2011
I Received
a Disk Drive April 3, 2012
With 35 GB FASTQ Files
Weizhong Li, UCSD
NGS Pipeline:
230M Reads
Only 0.2% Human
Required 1/2 cpu-yr
Per Person Analyzed!
Sequencing
Funding
Provided by
UCSD School of
Health Sciences
We Computationally Align 230M Illumina Short Reads
With a Reference Genome Set & Then Visually Analyze
Additional Phenotypes Added from NIH HMP
For Comparative Analysis
5 Ileal Crohn’s, 3 Points in Time
6 Ulcerative Colitis, 1 Point in Time
35 “Healthy” Individuals
1 Point in Time
We Find Major Shifts in Microbial Ecology
Between Healthy and Two Forms of IBD
Collapse of
Bacteroidetes
Explosion of
Proteobacteria
Microbiome “Dysbiosis”
or “Mass Extinction”?
On the IBD Spectrum
Almost All Abundant Species (≥1%) in Healthy Subjects
Are Severely Depleted in LS Gut
Top 20 Most Abundant Microbial Species
In LS vs. Average Healthy Subject
152x
765x
148x
849x
483x
220x
201x
522x
169x
Number Above
LS Blue Bar is Multiple
of LS Abundance
Compared to Average
Healthy Abundance
Per Species
Source: Sequencing JCVI; Analysis Weizhong Li, UCSD
LS December 28, 2011 Stool Sample
Major Changes in LS Microbiome Before and After
1 Month Antibiotic & 2 Month Prednisone Therapy
Reduced 45x
Reduced 90x
Therapy Greatly Reduced Two Phyla,
But Massive Reduction in Bacteroidetes
And Large % Proteobacteria Remain
Small Changes
With No Therapy
How Does One Get Back
to a “Healthy” Gut Microbiome?
Integrative Personal Omics Profiling
Using 100x My Quantifying Biomarkers
• Michael Snyder,
Chair of Genomics
Stanford Univ.
• Genome 140x
Coverage
• Blood Tests 20
Times in 14 Months
– tracked nearly
20,000 distinct
transcripts coding
for 12,000 genes
– measured the
relative levels of
more than 6,000
proteins and 1,000
metabolites in
Snyder's blood
Cell 148, 1293–1307, March 16, 2012
Proposed UCSD/JCVI
Integrated Omics Pipeline
Source: Nuno Bandiera, UCSD
UCSD Center for Computational Mass Spectrometry
Becoming Global MS Repository
ProteoSAFe: Compute-intensive
discovery MS at the click of a button
MassIVE: repository and
identification platform for all
MS data in the world
Source:
Nuno Bandeira,
Vineet Bafna,
Pavel Pevzner,
Ingolf Krueger,
UCSD
proteomics.ucsd.edu
A “Big Data Freeway System” Connecting Users
to Remote Campus Clusters & Scientific Instruments
Phil Papadopoulos, SDSC, Calit2, PI
Arista Enables SDSC’s Massively Parallel
10G Switched Data Analysis Resource
The Protein Data Bank (PDB)
Usage Is Growing Over Time
• More than 300,000 Unique Visitors per Month
• Up to 300 Concurrent Users
• ~10 Structures are Downloaded per Second 7/24/365
• Increasingly Popular Web Services Traffic
Source: Phil Bourne and Andreas Prlić, PDB
• Why is it Important?
– Enables PDB to Better Serve Its Users by Providing
Increased Reliability and Quicker Results
• How Will it be Done?
– By More Evenly Allocating PDB Resources
at Rutgers and UCSD
– By Directing Users to the Closest Site
• Need High Bandwidth Between Rutgers & UCSD Facilities
PDB Plans to Establish
Global Load Balancing
Source: Phil Bourne and Andreas Prlić, PDB
Integrating Systems Biology Data: Cytoscape
On Vroom-64MPixels Connected at 50Gbps
Calit2 Collaboration with Trey Idekar Group
www.cytoscape.org
“A Whole-Cell Computational Model
Predicts Phenotype from Genotype”
A model of
Mycoplasma genitalium,
•525 genes
•Using 1,900 experimental
observations
•From 900 studies,
•They created the
software model,
•Which requires 128
computers to run
Early Attempts at Modeling the Systems Biology of
the Gut Microbiome and the Human Immune System
Next Challenge:
Building a Multi-Cellular Organism Simulation
OpenWorm is an attempt to build a complete cellular-level simulation of 
the nematode worm Caenorhabditis elegans. Of the 959 cells in the 
hermaphrodite, 302 are neurons and 95 are muscle cells. 
The simulation will model electrical activity in all the muscles and 
neurons. An integrated soft-body physics simulation will also model 
body movement and physical forces within the worm and from its 
environment.
www.artificialbrains.com/openworm
A Vision for Healthcare
in the Coming Decades
Using this data, the planetary computer will be able
to build a computational model of your body
and compare your sensor stream with millions of others.
Besides providing early detection of internal changes
that could lead to disease,
cloud-powered voice-recognition wellness coaches could provide
continual personalized support on lifestyle choices, potentially
staving off disease
and making health care affordable for everyone.
ESSAY
An Evolution Toward a Programmable Universe
By LARRY SMARR
Published: December 5, 2011

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Discovering Yourself with Computational Bioinformatics

  • 1. “Discovering Yourself with Computational Bioinformatics” Rutgers Discovery Informatics Institute (RDI2 ) Distinguished Seminar Rutgers University New Brunswick, NJ May 9, 2013 Dr. Larry Smarr Director, California Institute for Telecommunications and Information Technology Harry E. Gruber Professor, Dept. of Computer Science and Engineering Jacobs School of Engineering, UCSD 1
  • 2. Abstract For over a decade, Calit2 has had a driving vision that healthcare is being transformed into “digitally enabled genomic medicine.” Combined with advances in nanotechnology and MEMS, a new generation of body sensors is rapidly developing. As these real-time data streams are stored in the cloud, cross population comparisons becomes increasingly possible and the availability of biofeedback leads to behavior change toward wellness. To put a more personal face on the "patient of the future," I have been increasingly quantifying my own body over the last ten years. In addition to external markers I also currently track over 100 blood biomarkers and dozens of molecular and microbial variables in my stool. Using my saliva 23andme.com obtained 1 million single nucleotide polymorphisms (SNPs) in my human DNA. My gut microbiome has been metagenomically sequenced by the J. Craig Venter Institute, yielding 25 billion DNA bases. I will show how one can discover emerging disease states before they develop serious symptoms using this Big Data approach. Hundreds of thousands of supercomputer CPU-hours were used in this voyage of self-discovery.
  • 3. Where I Believe We are Headed: Predictive, Personalized, Preventive, & Participatory Medicine www.newsweek.com/2009/06/26/a-doctor-s-vision-of-the-future-of-medicine.html I am Lee Hood’s Lab Rat!
  • 4. Calit2 Has Been Had a Vision of “the Digital Transformation of Health” for a Decade • Next Step—Putting You On-Line! – Wireless Internet Transmission – Key Metabolic and Physical Variables – Model -- Dozens of Processors and 60 Sensors / Actuators Inside of our Cars • Post-Genomic Individualized Medicine – Combine –Genetic Code –Body Data Flow – Use Powerful AI Data Mining Techniques www.bodymedia.com The Content of This Slide from 2001 Larry Smarr Calit2 Talk on Digitally Enabled Genomic Medicine
  • 5. The Calit2 Vision of Digitally Enabled Genomic Medicine is an Emerging Reality 5 July/August 2011 February 2012
  • 6. LifeChips: the merging of two major industries, the microelectronic chip industry with the life science industry LifeChips medical devices Lifechips--Merging Two Major Industries: Microelectronic Chips & Life Sciences 65 UCI Faculty
  • 7. Temporary Tattoo Biosensors Can Measure pH and Lactate in Sweat www.jacobsschool.ucsd.edu/news/news_releases/release.sfe?id=1353 From the UCSD Jacobs School of Engineering Laboratory for Nanobioelectronics-Prof. Joe Wang
  • 8. CitiSense –UCSD NSF Grant for Fine-Grained “Exposome” Sensing Using Cell Phones CitiSenseCitiSense contributecontribute distributedistribute sense sense ““display” display” discover discover retrieve retrieve Seacoast Sci. 4oz 30 compounds EPA CitiSense Team PI: Bill Griswold Ingolf Krueger Tajana Simunic Rosing Sanjoy Dasgupta Hovav Shacham Kevin Patrick C/A L S W F Intel MSP
  • 9. CitiSense Atmospheric Sensor Platform: Sensors Will Miniaturize and Diversify www.jacobsschool.ucsd.edu/news/news_releases/release.sfe?id=1353
  • 10. By Measuring the State of My Body and “Tuning” It Using Nutrition and Exercise, I Became Healthier 2000 Age 41 2010 Age 61 1999 1989 Age 51 1999 I Arrived in La Jolla in 2000 After 20 Years in the Midwest and Decided to Move Against the Obesity Trend I Reversed My Body’s Decline By Quantifying and Altering Nutrition and Exercise http://lsmarr.calit2.net/repository/LS_reading_recommendations_FiRe_2011.pdf
  • 11. Challenge-Develop Standards to Enable MashUps of Personal Sensor Data Across Private Clouds Withing/iPhone- Blood Pressure Zeo-Sleep Azumio-Heart Rate EM Wave PC- Stress MyFitnessPal- Calories Ingested FitBit - Daily Steps & Calories Burned
  • 12. From Measuring Macro-Variables to Measuring Your Internal Variables www.technologyreview.com/biomedicine/39636
  • 13. From One to a Billion Data Points Defining Me: The Exponential Rise in Body Data in Just One Decade! Billion: My Full DNA, MRI/CT Images Million: My DNA SNPs, Zeo, FitBit Hundred: My Blood VariablesOne: My WeightWeight Blood Variables SNPs Microbial Genome Improving Body Discovering Disease
  • 14. Visualizing Time Series of 150 LS Blood and Stool Variables, Each Over 5-10 Years Calit2 64 megapixel VROOM
  • 15. Only One of My Blood Measurements Was Far Out of Range--Indicating Chronic Inflammation Normal Range<1 mg/L Normal 27x Upper Limit Antibiotics Antibiotics Episodic Peaks in Inflammation Followed by Spontaneous Drops Complex Reactive Protein (CRP) is a Blood Biomarker for Detecting Presence of Inflammation
  • 16. High Values of Lactoferrin (Shed from Neutrophils) From Stool Sample Suggested Inflammation in Colon Normal Range <7.3 µg/mL 124x Upper Limit Antibiotics Antibiotics Typical Lactoferrin Value for Active IBD Stool Samples Analyzed by www.yourfuturehealth.com Lactoferrin is a Sensitive and Specific Biomarker for Detecting Presence of Inflammatory Bowel Disease (IBD)
  • 17. Descending Colon Sigmoid Colon Threading Iliac Arteries Major Kink Confirming the IBD (Crohn’s) Hypothesis: Finding the “Smoking Gun” with MRI Imaging I Obtained the MRI Slices From UCSD Medical Services and Converted to Interactive 3D Working With Calit2er Jurgen Schulze’s DeskVOX Software Transverse Colon Liver Small Intestine Diseased Sigmoid Colon Cross Section MRI Jan 2012
  • 18. An MRI Shows Sigmoid Colon Wall Thickened Indicating Probable Diagnosis of Crohn’s Disease
  • 19. Why Did I Have an Autoimmune Disease like IBD? Despite decades of research, the etiology of Crohn's disease remains unknown. Its pathogenesis may involve a complex interplay between host genetics, immune dysfunction, and microbial or environmental factors. --The Role of Microbes in Crohn's Disease Paul B. Eckburg & David A. Relman Clin Infect Dis. 44:256-262 (2007)  So I Set Out to Quantify All Three!
  • 20. I Wondered if Crohn’s is an Autoimmune Disease, Did I Have a Personal Genomic Polymorphism? From www.23andme.com SNPs Associated with CD Polymorphism in Interleukin-23 Receptor Gene — 80% Higher Risk of Pro-inflammatory Immune Response NOD2 ATG16L1 IRGM Now Comparing 163 Known IBD SNPs with 23andme SNP Chip
  • 21. Crohn’s May be a Related Set of Diseases Driven by Different SNPs Me-Male CD Onset At 60-Years Old Female CD Onset At 20-Years Old NOD2 (1) rs2066844 Il-23R rs1004819
  • 22. Autoimmune Disease Overlap from SNP GWAS Gut Lees, et al. 60:1739-1753 (2011)
  • 23. Imagine Crowdsourcing 23andme SNPs For Even a Small Portion of Crohnology! www.crohnology.com
  • 24. But the Human Genome Contains Less Than 1% of the Bodies Genes http://commonfund.nih.gov/hmp/ The Total Number of These Bacterial Cells is 10 Times the Number of Human Cells in Your Body
  • 25. But How Can You Determine Which Microbes Are Within You? “The emerging field of metagenomics, where the DNA of entire communities of microbes is studied simultaneously, presents the greatest opportunity -- perhaps since the invention of the microscope – to revolutionize understanding of the microbial world.” – National Research Council March 27, 2007 NRC Report: Metagenomic data should be made publicly available in international archives as rapidly as possible.
  • 26. Infrastructure Services Extend CAMERA Computations to 3rd Party Compute Resources Infrastructure Services Extend CAMERA Computations to 3rd Party Compute Resources NSF/SDSC Gordon UCSD Triton NSF/SDSC Trestles NSF/RCAC Steele NSF/TACC Lonestar NSF/TACC Ranger Core CAMERA HPC Resource Calit2 Community Cyberinfrastructure for Advanced Microbial Ecology Research and Analysis (CAMERA) Source: Jeff Grethe, CRBS, UCSD >5000 Users >90 Countries
  • 27. CAMERA and NIH Funded Weizhong Li Group’s Metagenomic Computational NextGen Sequencing Pipeline Raw readsRaw reads Reads QC HQ reads:HQ reads: Filter human Bowtie/BWA against Human genome and mRNAs Bowtie/BWA against Human genome and mRNAs Unique readsUnique reads CD-HIT-Dup For single or PE reads CD-HIT-Dup For single or PE reads Further filtered reads Further filtered reads Filtered readsFiltered reads Filter duplicate Cluster-based Denoising Cluster-based Denoising ContigsContigs Assemble Velvet, SOAPdenovo, Abyss ------- K-mer setting Velvet, SOAPdenovo, Abyss ------- K-mer setting Contigs with Abundance Contigs with Abundance Mapping BWA BowtieBWA Bowtie Taxonomy binningTaxonomy binning Filter errorsRead recruitment FR-HIT against Non-redundant microbial genomes FR-HIT against Non-redundant microbial genomes VisualizationVisualization FRV tRNAs rRNAs tRNAs rRNAs tRNA-scan rRNA - HMM ORFsORFs ORF-finder Megagene Non redundant ORFs Non redundant ORFs Core ORF clustersCore ORF clusters Cd-hit at 95% Cd-hit at 60% Protein familiesProtein families Cd-hit at 30% 1e-6 Function Pathway Annotation Function Pathway Annotation Pfam Tigrfam COG KOG PRK KEGG eggNOG Pfam Tigrfam COG KOG PRK KEGG eggNOG Hmmer RPS-blast blast PI: (Weizhong Li, UCSD): NIH R01HG005978 (2010-2013, $1.1M)
  • 28. We Used SDSC’s Gordon Data-Intensive Supercomputer to Analyze a Wide Range of Gut Microbiomes • Analyzed Healthy and IBD Patients: – LS, 13 Crohn's Disease & 11 Ulcerative Colitis Patients, + 150 HMP Healthy Subjects • Gordon Compute Time – ~1/2 CPU-Year Per Sample – > 200,000 CPU-Hours so far • Gordon RAM Required – 64GB RAM for Most Steps – 192GB RAM for Assembly • Gordon Disk Required – 8TB for All Subjects – Input, Intermediate and Final Results Enabled by a Grant of Time on Gordon from SDSC Director Mike Norman Venter Sequencing of LS Gut Microbiome: 230 M Reads 101 Bases Per Read 23 Billion DNA Bases
  • 29. 2012 Was the Year of Human Microbiome
  • 30. When We Think About Biological Diversity We Typically Think of the Wide Range of Animals But All These Animals Are in One SubPhylum Vertebrata of the Chordata Phylum All images from Wikimedia Commons. Photos are public domain or by Trisha Shears & Richard Bartz
  • 31. Think of These Phyla of Animals When You Consider the Biodiversity of Microbes Inside You All images from WikiMedia Commons. Photos are public domain or by Dan Hershman, Michael Linnenbach, Manuae, B_cool Phylum Annelida Phylum Echinodermata Phylum Cnidaria Phylum Mollusca Phylum Arthropoda Phylum Chordata
  • 32. Most Biological Diversity on Earth is in the Microbial World Source: Carl Woese, et al Last Slide Evolutionary Distance Derived from Comparative Sequencing of 16S or 18S Ribosomal RNA Red Circles Are Dominate Human Gut Microbes
  • 33. June 8, 2012 June 14, 2012 Intense Scientific Research is Underway on Understanding the Human Microbiome From Culturing Bacteria to Sequencing Them
  • 34. To Map My Gut Microbes, I Sent a Stool Sample to the Venter Institute for Metagenomic Sequencing Gel Image of Extract from Smarr Sample-Next is Library Construction Manny Torralba, Project Lead - Human Genomic Medicine J Craig Venter Institute January 25, 2012 Shipped Stool Sample December 28, 2011 I Received a Disk Drive April 3, 2012 With 35 GB FASTQ Files Weizhong Li, UCSD NGS Pipeline: 230M Reads Only 0.2% Human Required 1/2 cpu-yr Per Person Analyzed! Sequencing Funding Provided by UCSD School of Health Sciences
  • 35. We Computationally Align 230M Illumina Short Reads With a Reference Genome Set & Then Visually Analyze
  • 36. Additional Phenotypes Added from NIH HMP For Comparative Analysis 5 Ileal Crohn’s, 3 Points in Time 6 Ulcerative Colitis, 1 Point in Time 35 “Healthy” Individuals 1 Point in Time
  • 37. We Find Major Shifts in Microbial Ecology Between Healthy and Two Forms of IBD Collapse of Bacteroidetes Explosion of Proteobacteria Microbiome “Dysbiosis” or “Mass Extinction”? On the IBD Spectrum
  • 38. Almost All Abundant Species (≥1%) in Healthy Subjects Are Severely Depleted in LS Gut
  • 39. Top 20 Most Abundant Microbial Species In LS vs. Average Healthy Subject 152x 765x 148x 849x 483x 220x 201x 522x 169x Number Above LS Blue Bar is Multiple of LS Abundance Compared to Average Healthy Abundance Per Species Source: Sequencing JCVI; Analysis Weizhong Li, UCSD LS December 28, 2011 Stool Sample
  • 40. Major Changes in LS Microbiome Before and After 1 Month Antibiotic & 2 Month Prednisone Therapy Reduced 45x Reduced 90x Therapy Greatly Reduced Two Phyla, But Massive Reduction in Bacteroidetes And Large % Proteobacteria Remain Small Changes With No Therapy How Does One Get Back to a “Healthy” Gut Microbiome?
  • 41. Integrative Personal Omics Profiling Using 100x My Quantifying Biomarkers • Michael Snyder, Chair of Genomics Stanford Univ. • Genome 140x Coverage • Blood Tests 20 Times in 14 Months – tracked nearly 20,000 distinct transcripts coding for 12,000 genes – measured the relative levels of more than 6,000 proteins and 1,000 metabolites in Snyder's blood Cell 148, 1293–1307, March 16, 2012
  • 42. Proposed UCSD/JCVI Integrated Omics Pipeline Source: Nuno Bandiera, UCSD
  • 43. UCSD Center for Computational Mass Spectrometry Becoming Global MS Repository ProteoSAFe: Compute-intensive discovery MS at the click of a button MassIVE: repository and identification platform for all MS data in the world Source: Nuno Bandeira, Vineet Bafna, Pavel Pevzner, Ingolf Krueger, UCSD proteomics.ucsd.edu
  • 44. A “Big Data Freeway System” Connecting Users to Remote Campus Clusters & Scientific Instruments Phil Papadopoulos, SDSC, Calit2, PI
  • 45. Arista Enables SDSC’s Massively Parallel 10G Switched Data Analysis Resource
  • 46. The Protein Data Bank (PDB) Usage Is Growing Over Time • More than 300,000 Unique Visitors per Month • Up to 300 Concurrent Users • ~10 Structures are Downloaded per Second 7/24/365 • Increasingly Popular Web Services Traffic Source: Phil Bourne and Andreas Prlić, PDB
  • 47. • Why is it Important? – Enables PDB to Better Serve Its Users by Providing Increased Reliability and Quicker Results • How Will it be Done? – By More Evenly Allocating PDB Resources at Rutgers and UCSD – By Directing Users to the Closest Site • Need High Bandwidth Between Rutgers & UCSD Facilities PDB Plans to Establish Global Load Balancing Source: Phil Bourne and Andreas Prlić, PDB
  • 48. Integrating Systems Biology Data: Cytoscape On Vroom-64MPixels Connected at 50Gbps Calit2 Collaboration with Trey Idekar Group www.cytoscape.org
  • 49. “A Whole-Cell Computational Model Predicts Phenotype from Genotype” A model of Mycoplasma genitalium, •525 genes •Using 1,900 experimental observations •From 900 studies, •They created the software model, •Which requires 128 computers to run
  • 50. Early Attempts at Modeling the Systems Biology of the Gut Microbiome and the Human Immune System
  • 51. Next Challenge: Building a Multi-Cellular Organism Simulation OpenWorm is an attempt to build a complete cellular-level simulation of  the nematode worm Caenorhabditis elegans. Of the 959 cells in the  hermaphrodite, 302 are neurons and 95 are muscle cells.  The simulation will model electrical activity in all the muscles and  neurons. An integrated soft-body physics simulation will also model  body movement and physical forces within the worm and from its  environment. www.artificialbrains.com/openworm
  • 52. A Vision for Healthcare in the Coming Decades Using this data, the planetary computer will be able to build a computational model of your body and compare your sensor stream with millions of others. Besides providing early detection of internal changes that could lead to disease, cloud-powered voice-recognition wellness coaches could provide continual personalized support on lifestyle choices, potentially staving off disease and making health care affordable for everyone. ESSAY An Evolution Toward a Programmable Universe By LARRY SMARR Published: December 5, 2011