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Using Data Analytics to Discover the 100 Trillion Bacteria Living Within Each of Us

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Invited Talk, New Applications of Computer Analysis to Biomedical Data Sets, QB3 Seminar, San Francisco, CA - May 28, 2015

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Using Data Analytics to Discover the 100 Trillion Bacteria Living Within Each of Us

  1. 1. “Using Data Analytics to Discover the 100 Trillion Bacteria Living Within Each of Us” Invited Talk New Applications of Computer Analysis to Biomedical Data Sets QB3 Seminar San Francisco, CA May 28, 2015 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 http://lsmarr.calit2.net 1
  2. 2. I Have Turned My Body into a Genomic and Biomarker Observatory One Blood Draw For MeCalit2 64 Megapixel VROOM Over 100 Blood and Stool Biomarker Time Series
  3. 3. Only One of My Blood Measurements Was Far Out of Range--Indicating Chronic Inflammation Normal Range <1 mg/L 27x Upper Limit Complex Reactive Protein (CRP) is a Blood Biomarker for Detecting Presence of Inflammation Episodic Peaks in Inflammation Followed by Spontaneous Drops
  4. 4. Adding Stool Tests Revealed Oscillatory Behavior in an Immune Variable Which is Antibacterial Normal Range <7.3 µg/mL 124x Upper Limit Lactoferrin is a Protein Shed from Neutrophils - An Antibacterial that Sequesters Iron Typical Lactoferrin Value for Active Inflammatory Bowel Disease (IBD)
  5. 5. Dynamical Innate and Adaptive Immune Oscillations From Stool Samples Normal <600 Innate Immune System Normal 50 to 200 Adaptive Immune System
  6. 6. Methods Needed for Biomarker Time Series Correlations - Adding 40 Microbial Ecology Time Series Immune & Inflammation Variables Weekly Symptoms Pharma Therapies Stool Samples
  7. 7. How Will Detailed Knowledge of Microbiome Ecology Radically Change Medicine and Wellness? 99% of Your DNA Genes Are in Microbe Cells Not Human Cells Your Body Has 10 Times As Many Microbe Cells As Human Cells Challenge: Map Out Microbial Ecology and Function in Health and Disease States
  8. 8. For Deep Analysis of Changes in the Gut Microbiome Ecology Our Team Compared a Healthy Population to Patients with Disease 5 Ileal Crohn’s Patients, 3 Points in Time 2 Ulcerative Colitis Patients, 6 Points in Time “Healthy” Individuals Source: Jerry Sheehan, Calit2 Weizhong Li, Sitao Wu, CRBS, UCSD Total of 2.7 Trillion DNA Bases Inflammatory Bowel Disease Patients 250 Subjects 1 Point in Time 7 Points in Time Larry Smarr (Colonic Crohn’s) Example: Inflammatory Bowel Disease (IBD)
  9. 9. 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-years to compute comparative gut microbiomes starting from 2.7 trillion DNA bases of my samples and healthy and IBD controls Illumina HiSeq 2000 at JCVI SDSC Gordon Data Supercomputer
  10. 10. We Found Major State Shifts in Microbial Ecology Phyla Between Healthy and Two Forms of IBD Most Common Microbial Phyla Average Healthy Average Ulcerative Colitis Average Colonic Crohn’s Disease Average Ileal Crohn’s Disease Collapse of Bacteroidetes Explosion of Actinobacteria Explosion of Proteobacteria Hybrid of UC and CD High Level of Archaea Based on ~10,000 Bacteria, Archaea, Viruses Whose Genomes are Known
  11. 11. Dell Analytics Separates The 4 Patient Types in Our Data Using 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
  12. 12. I Built on Dell Analytics to Show Dynamic Evolution of My Microbiome Toward and Away from Healthy State – Colonic Crohn’s Source: Thomas Hill, Ph.D. Executive Director Analytics Dell | Information Management Group, Dell Software
  13. 13. 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 Now Extending 7 to 40 Time Samples
  14. 14. Large Changes in Genus Relative Abundances Observed Over the Seven Smarr Samples (1.5 Years)-Clearly Not Healthy!
  15. 15. Can We Extend These Approaches from Hundreds to Thousands by Using Crowdsourcing Gut Microbiome Sequencing? Source: Ubiome provided kits and taxonomic analysis Graph by Larry Smarr Three Years of Larry Smarr’s Gut Microbiome (38 Samples) Phyla Distribution Analyzed Using Ubiome
  16. 16. Applying Ayasdi to Ubiome Gut Microbiome Genus Relative Abundance Time Series Analysis by Mehrdad Yazdani, Calit2; Devi Ramanan, Ubiome Metric: Variance Normalized Euclidean with PCA Lens of Ecology Change Over All Time Pairs
  17. 17. Sorted by KS stat Apply Ayasdi Statistical Tests Using Ayasdi to Discover Abrupt Changes in Time in Gut Microbiome Ecology Large Ecology Change In Short Time Interval Analysis by Mehrdad Yazdani, Calit2; Devi Ramanan, Ubiome
  18. 18. Abrupt Changes Discovered in Genus Faecalibacterium Relative Abundance Time Series Source: Ubiome provided kits and taxonomic analysis Graph by Larry Smarr 35x
  19. 19. Genetic Sequencing of Humans and Their Microbes Is a Huge Growth Area and the Future Foundation of Medicine Source: @EricTopol Twitter 9/27/2014
  20. 20. Thanks to Our Great Team! UCSD Metagenomics Team Weizhong Li Sitao Wu Calit2@UCSD Future Patient Team Jerry Sheehan Tom DeFanti Kevin Patrick Jurgen Schulze Andrew Prudhomme Philip Weber Fred Raab Joe Keefe Ernesto Ramirez JCVI Team Karen Nelson Shibu Yooseph Manolito Torralba SDSC Team Michael Norman Ilkay Altintas Shweta Purawat Mahidhar Tatineni Robert Sinkovits UCSD Health Sciences Team William J. Sandborn Elisabeth Evans John Chang Brigid Boland David Brenner Dell/R Systems and Dell Analytics Brian Kucic John Thompson Tom Hill

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