Quantified Self On Being A Personal Genomic Observatory
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The keynote by Dr. Larry Smarr at the GSC on April 24, 2013, discusses the concept of the quantified self in relation to personal genomic observatories, highlighting the infrastructure provided by the California Institute for Telecommunications and Information Technology for microbial ecology research. The talk emphasizes the importance of understanding the human microbiome and its relationship to health, as demonstrated through personal metagenomic sequencing experiences, and the distinctions found in microbial diversity among healthy and diseased states. It further explores the need for adequate metadata to comprehensively analyze the human microbiome and its influencing variables.
Quantified Self On Being A Personal Genomic Observatory
- 1. “Quantified Self- On Being a Personal Genomic Observatory” Keynote in the “Humans as Genomic Observatories” Meeting Session in the Genomics Standards Consortium GSC 15 April 24, 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 http://lsmarr.calit2.net 1
- 2. Calit2 Community Cyberinfrastructure for Advanced Microbial Ecology Research and Analysis (CAMERA) 512 Processors ~5 Teraflops Source: Phil Papadopoulos, SDSC, Calit2 ~ 200 Terabytes Storage 1GbE and 10GbE Switched/ Routed Core ~200TB Sun X4500 Storage 10GbE 5000 Users 90 Countries
- 3. Access to Computing Resources Tailored by User’s Requirements and Resources NSF/SDSC Gordon Infrastructure Services Extend CAMERA Computations to 3rd Party Compute Resources UCSD Triton NSF/SDSC Trestles NSF/RCAC Steele NSF/TACC Lonestar NSF/TACC Ranger Core CAMERA HPC Resource EAGER: Multi-Domain, Workflow-Driven Computation System for Microbial Ecology Research and Analysis Source: Jeff Grethe, CRBS, UCSD
- 4. CAMERA A Community Gateway to Data & Analysis Functions Data Data Analysis
- 5. Marine Genome Sequencing Project – CAMERA Anchor Dataset Launched March 13, 2007 Measuring the Genetic Diversity of Ocean Microbes Specify Ocean Data Each Sample ~2000 Microbial Species
- 6. The Human Microbiome Is a Microbial Environment Being Metagenomically Sampled
- 7. CAMERA and NIH Funded Weizhong Li Group’s Metagenomic Computational NextGen Sequencing Pipeline Raw reads Reads QC HQ reads: Filter human Bowtie/BWA against Human genome and mRNAs Unique reads CD-HIT-Dup For single or PE reads Read recruitment Filter errors Further filtered reads Filtered reads Filter duplicate Cluster-based Denoising Assemble Contigs Velvet, SOAPdenovo, Abyss ------- K-mer setting Mapping BWA Bowtie Contigs with Abundance Taxonomy binning FR-HIT against Non-redundant microbial genomes FRV Visualization tRNA-scan rRNA - HMM tRNAs rRNAs ORFs ORF-finder Megagene Cd-hit at 95% Non redundant ORFs Cd-hit at 60% Core ORF clusters Cd-hit at 30% 1e-6 Protein families Function Pathway Annotation Pfam Tigrfam COG KOG PRK KEGG eggNOG Hmmer RPS-blast blast PI: (Weizhong Li, UCSD): NIH R01HG005978 (2010-2013, $1.1M)
- 8. What is a “Healthy” Gut Microbiome? Dominated by Bacteroidetes and Firmicute Phyla Source: “Structure, function and diversity of the healthy human microbiome,” HMP Consortium, Nature, 486, 207-212 (2012)
- 9. To Map My Gut Microbes, I Sent a Stool Sample to the Venter Institute for Metagenomic Sequencing Shipped Stool Sample December 28, 2011 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 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
- 10. Phyla Gut Microbial Abundance Without Viruses: LS, Crohn’s, UC, and Healthy Subjects Source: Weizhong Li, UCSD; Calit2 FuturePatient Expedition Crohn’s Ulcerative LS Healthy Colitis Toward Noninvasive Microbial Ecology Diagnostics
- 11. Almost All Abundant Species (≥1%) in Healthy Subjects Are Severely Depleted in LS Gut Source: Sequencing JCVI; Analysis Weizhong Li, UCSD LS December 28, 2011 Stool Sample
- 12. Top 20 Most Abundant Microbial Species In LS vs. Average Healthy Subject 152x 765x 148x 849x 483x 220x 201x 169x 522x 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
- 13. Comparing 3 LS Time Snapshots (Left) with Healthy, Crohn’s, UC (Right Top to Bottom) Calit2 VROOM-FuturePatient Expedition
- 14. We Find Major Shifts in Microbial Ecology Between Healthy and Two Forms of IBD Collapse of Bacteroidetes Microbiome “Dysbiosis” or “Mass Extinction”? Explosion of Proteobacteria On the IBD Spectrum
- 15. I Have Massive Reduction in the Families of the Bacteroidetes Phylum in My Gut Calit2 FuturePatient Expedition
- 16. 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?
- 17. From War to Gardening “I would like to lose the language of warfare,” said Julie Segre, a senior investigator at the National Human Genome Research Institute. ”It does a disservice to all the bacteria that have co-evolved with us and are maintaining the health of our bodies.”
- 18. From Taxonomy to Function: Analysis of LS Clusters of Orthologous Groups (COGs) Analysis: Weizhong Li & Sitao Wu, UCSD
- 19. What is Adequate Metadata to Define the Environment of the Human Microbiome? • Need the Variables that Determine Relative Abundances of Microbial Species – Genetics of Host – Immune System Variables – Other Environmental Variables (Food, Antibiotics, etc.) • At What Scale Do We Need These Metadata Variables? – SNPs vs. Full Genome – Medical Tests vs. Proteomics, Metabolomics, Transcriptomics – Phenotyping of Signs and Symptoms