“Using Supercomputers and Data Analytics to Discover the Differences in Health and Disease”

Briefing forDell Analytics Team

Calit2’s Qualcomm InstituteUniversity of California, San Diego

April 7, 2016

Dr. Larry SmarrDirector, California Institute for Telecommunications and Information Technology

Harry E. Gruber Professor, Dept. of Computer Science and Engineering

Jacobs School of Engineering, UCSDhttp://lsmarr.calit2.net

1

We Gathered Raw Illumina Reads on 275 Humansand Generated a Time Series of My Gut Microbiome

5 Ileal Crohn’s Patients, 3 Points in Time

2 Ulcerative Colitis Patients, 6 Points in Time

“Healthy” Individuals

Source: Jerry Sheehan, Calit2Weizhong Li, Sitao Wu, CRBS, UCSD

Total of 27 Billion ReadsOr 2.7 Trillion Bases

Inflammatory Bowel Disease (IBD) Patients250 Subjects

1 Point in Time

7 Points in Time

Each Sample Has 100-200 Million Illumina Short Reads (100 bases)

Larry Smarr(Colonic Crohn’s)

To Map Out the Dynamics of Autoimmune Microbiome Ecology Couples Next Generation Genome Sequencers to Big Data Supercomputers

Source: Weizhong Li, UCSD

Our Team Used 25 CPU-yearsto Compute

Comparative Gut MicrobiomesStarting From

2.7 Trillion DNA Bases of My Samples

and Healthy and IBD Controls

Illumina HiSeq 2000 at JCVI

SDSC Gordon Data Supercomputer

To Expand IBD Project the Knight/Smarr Labs Were Awarded ~ 1 CPU-Century Supercomputing Time

• Smarr Gut Microbiome Time Series– From 7 Samples Over 1.5 Years – To 50 Samples Over 4 Years

• IBD Patients: From 5 Crohn’s Disease and 2 Ulcerative Colitis Patients to ~100 Patients– 50 Carefully Phenotyped Patients Drawn from Sandborn BioBank– 43 Metagenomes from the RISK Cohort of Newly Diagnosed IBD patients

• New Software Suite from Knight Lab– Re-annotation of Reference Genomes, Functional / Taxonomic Variations– Novel Compute-Intensive Assembly Algorithms from Pavel Pevzner

8x Compute Resources Over Prior Study

Next Step Programmability, Scalability, and Reproducibility using bioKepler

www.kepler-project.org

www.biokepler.org

National Resources

(Gordon) (Comet)

(Stampede)(Lonestar)

Cloud Resources

Optimized

Local Cluster Resources

Source: Ilkay

Altintas, SDSC

Using HPC and Data Analyticsto Discover Microbial Diagnostics for Disease Dynamics

• Can Data Distinguish Between Health and Disease Subtypes?

• Can Data Track the Time Development of the Disease State?

• Can Data Create Novel Microbial Diagnostics for Identifying Health and Disease States?

• Can Data Discover Functional Microbiome Gene Changes Between Health and Disease?

Can Data Distinguish Between Health and Disease Subtypes?

Dell Analytics Separates The 4 Patient Types in Our DataUsing Our Microbiome Species Data

Source: Thomas Hill, Ph.D.Executive Director Analytics

Dell | Information Management Group, Dell Software

Healthy

Ulcerative Colitis

Colonic Crohn’s

Ileal Crohn’s

Can Data Track the Time Development of the Disease State?

I Built on Dell Analytics to Show Dynamic Evolution of My Microbiome Toward and Away from Healthy State – Colonic Crohn’s

Healthy

Ileal Crohn’s

Seven Time Samples Over 1.5 Years

Colonic Crohn’s

Source: Thomas Hill, Ph.D.Executive Director Analytics

Dell | Information Management Group, Dell Software

Variation in My Gut Microbiome by 16S Families – 40 Samples Over 3.5 Years

Data from Justine Debelius & Jose Navas, Knight Lab, UCSD; Larry Smarr Analysis, January 2016

Larry Smarr Gut Microbiome Ecology Shifted After Drug Therapy Between Two Time-Stable Equilibriums Correlated to Physical Symptoms

Lialda &

Uceris

12/1/13 to 1/1/14

12/1/13-1/1/14

Frequent IBD SymptomsWeight Loss

5/1/12 to 12/1/14Blue Balls on Diagram

to the Right

Few IBD SymptomsWeight Gain

1/1/14 to 1/1/16Red Balls on Diagram

to the Right

Principal Coordinate Analysis of Microbiome Ecology

PCoA by Justine Debelius and Jose Navas, Knight Lab, UCSD

Weight Data from Larry Smarr, Calit2, UCSD

Ant

ibio

ticsPrednisone

1/1/12 to 5/1/12

5/1/12

Weekly Weight (Red Dots Stool Sample)

Few IBD SymptomsWeight Gain

1/1/14 to 1/1/16Red Balls on Diagram

to the Right

Can Data Create Novel Microbial Diagnostics for Identifying Health and Disease States?

Dell Analytics Tree Graphs Classifies the 4 Health/Disease States With Just 3 Microbe Species

Source: Thomas Hill, Ph.D.Executive Director Analytics

Dell | Information Management Group, Dell Software

Our Relative Abundance Results Across ~300 People Show Why Dell Analytics Tree Classifier Works

UC 100x Healthy

LS 100x UC

We Produced Similar Results for ~2500 Microbial Species

Healthy 100x CD

Ayasdi Enables Discovery of Differences Between Healthy and Disease States Using Microbiome Species

Healthy LS

Ileal Crohn’s Ulcerative Colitis

Using Multidimensional Scaling Lens with Correlation Metric

High in Healthy and LS

High in Healthy and Ulcerative Colitis

High in Both LS and Ileal Crohn’s Disease

Analysis by Mehrdad Yazdani, Calit2

Can Data Discover Functional Microbiome Gene Changes Between Health and Disease?

We Computed the Relative Abundance of Microbial Gene Families -~10,000 KEGG Orthologous Genes, Across Healthy and IBD Subjects

How Large is the Microbiome’s Genetic ChangeBetween Health and Disease States?

In a “Healthy” Gut Microbiome:Large Taxonomy Variation, Low Protein Family Variation

Source: Nature, 486, 207-212 (2012)

Over 200 People

Ratio of HE11529 to Ave HETest to see How Much Variation There is Within Healthy

Most KEGGs Are Within 10xOf Healthy for a Random HE

Ratio of Random HE11529 to Healthy Average for Each Nonzero KEGG

Similar to HMP Healthy Results

Our Research Shows Large Changes in Protein Families Between Health and Disease – Ileal Crohns

KEGGs Greatly IncreasedIn the Disease State

KEGGs Greatly DecreasedIn the Disease State

Over 7000 KEGGs Which Are Nonzero in Health and Disease States

Ratio of CD Average to Healthy Average for Each Nonzero KEGG

Note Hi/LowSymmetry

Similar Results for UC and LS

We Found a Set of Ayasdi Lenses That Separate Out the 43 Extreme KEGGs Common to the Disease States

K00108(choline_dehydrogenase)K00673(arginine_N-succinyltransferase)K00867(type_I_pantothenate_kinase)K01169(ribonuclease_I_(enterobacter_ribonuclease))K01484(succinylarginine_dihydrolase)K01682(aconitate_hydratase_2)K01690(phosphogluconate_dehydratase)K01825(3-hydroxyacyl-CoA_dehydrogenase_/_enoyl-CoA_hydratase_/3-hydroxybutyryl-CoA_epimerase_/_enoyl-CoA_isomerase_[EC:1.1.1.354.2.1.17_5.1.2.3_5.3.3.8])K02173(hypothetical_protein)K02317(DNA_replication_protein_DnaT)K02466(glucitol_operon_activator_protein)K02846(N-methyl-L-tryptophan_oxidase)K03081(3-dehydro-L-gulonate-6-phosphate_decarboxylase)K03119(taurine_dioxygenase)K03181(chorismate--pyruvate_lyase)K03807(AmpE_protein)K05522(endonuclease_VIII)K05775(maltose_operon_periplasmic_protein)K05812(conserved_hypothetical_protein)K05997(Fe-S_cluster_assembly_protein_SufA)K06073(vitamin_B12_transport_system_permease_protein)K06205(MioC_protein)K06445(acyl-CoA_dehydrogenase)K06447(succinylglutamic_semialdehyde_dehydrogenase)K07229(TrkA_domain_protein)K07232(cation_transport_protein_ChaC)K07312(putative_dimethyl_sulfoxide_reductase_subunit_YnfH_(DMSO_reductaseanchor_subunit))K07336(PKHD-type_hydroxylase)K08989(putative_membrane_protein)K09018(putative_monooxygenase_RutA)K09456(putative_acyl-CoA_dehydrogenase)K09998(arginine_transport_system_permease_protein)K10748(DNA_replication_terminus_site-binding_protein)K11209(GST-like_protein)K11391(ribosomal_RNA_large_subunit_methyltransferase_G)K11734(aromatic_amino_acid_transport_protein_AroP)K11735(GABA_permease)K11925(SgrR_family_transcriptional_regulator)K12288(pilus_assembly_protein_HofM)K13255(ferric_iron_reductase_protein_FhuF)K14588()K15733()K15834()

L-Infinity Centrality Lens Using Norm Correlation

as Metric (Resolution: 242, Gain: 5.7)

Entropy & Variance LensUsing Angle as Metric

(Resolution: 30, Gain 3.00)

Analysis by Mehrdad Yazdani, Calit2

Disease Arises from Perturbed Protein Family Networks:Dynamics of a Prion Perturbed Network in Mice

Source: Lee Hood, ISB 23

Our Next Goal is to Create Such Perturbed Networks in Humans

Calit2’s Qualcomm Institute Has Developed Interactive Scalable Visualization for Biological Networks

20,000 Samples60,000 OTUs

18 Million Edges

Runs Native on 64Million Pixels

Center for Microbiome Innovation

Seminars Faculty Hiring Education

UCSD Microbial Sciences Initiative

InstrumentCores

Seed GrantsFellowships

Chancellor Khosla Launched the UC San Diego Microbiome and Microbial Sciences Initiative October 29, 2015

Thanks to Our Great Team!

Calit2@UCSD Future Patient TeamJerry SheehanTom DeFanti Joe Keefe John GrahamKevin PatrickMehrdad YazdaniJurgen Schulze Andrew Prudhomme Philip Weber Fred RaabErnesto Ramirez

JCVI TeamKaren Nelson Shibu Yooseph Manolito Torralba

AyasdiDevi RamananPek Lum

UCSD Metagenomics TeamWeizhong Li Sitao Wu

SDSC TeamMichael Norman Mahidhar Tatineni Robert Sinkovits Ilkay Altintas

UCSD Health Sciences TeamDavid BrennerRob Knight Lab Justine Debelius Jose Navas Bryn Taylor Gail Ackermann Greg HumphreyWilliam J. Sandborn Lab Elisabeth Evans John Chang Brigid Boland

Dell/R SystemsBrian KucicJohn Thompson Thomas Hill