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"Phylogenomic approaches to microbial diversity" Talk by Jonathan Eisen at #IlluminaBayArea meeting

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Talk by Jonathan Eisen at the Bay Area Illumina Users meeting 9/6/12 ""Phylogenomic approaches to microbial diversity"

Talk by Jonathan Eisen at the Bay Area Illumina Users meeting 9/6/12 ""Phylogenomic approaches to microbial diversity"

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  • 1. Phylogenomic Approaches to the Study of Microbial Diversity September 6, 2012 Bay Area Illumina User’s Meeting Jonathan A. Eisen University of California, Davis @phylogenomicsThursday, September 6, 12
  • 2. Phylogenomic Approaches to Studying Microbial Diversity Example 1: Phylotyping and Phylogenetic DiversityThursday, September 6, 12
  • 3. rRNA Phylotyping DNA extraction PCR Makes lots of Sequence PCR copies of the rRNA genes rRNA genes in sample rRNA1 5’...ACACACATAGGTGGAGCTA GCGATCGATCGA... 3’ Sequence alignment = Data matrix rRNA2 rRNA1 A C A C A C 5’..TACAGTATAGGTGGAGCTAG CGACGATCGA... 3’ rRNA2 T A C A G T rRNA3 rRNA3 C A C T G T 5’...ACGGCAAAATAGGTGGATT rRNA4 C A C A G T CTAGCGATATAGA... 3’ E. coli A G A C A G rRNA4 5’...ACGGCCCGATAGGTGGATT Humans T A T A G T CTAGCGCCATAGA... 3’ Yeast T A C A G TThursday, September 6, 12
  • 4. PhylotypingThursday, September 6, 12
  • 5. Phylotyping E. coli Humans YeastThursday, September 6, 12
  • 6. Phylotyping E. coli Humans Yeast OTU2 OTU1 OTU4 OTU3 E. coli Humans YeastThursday, September 6, 12
  • 7. Phylotyping B A Cluster CThursday, September 6, 12
  • 8. Phylotyping B A Cluster C B A OTUs CThursday, September 6, 12
  • 9. Phylotyping B A Cluster C B A OTUs C OTU1 OTU2 OTU3 OTU4Thursday, September 6, 12
  • 10. Phylotyping B A Cluster C B A OTUs C OTU2 OTU1 OTU1 OTU4 OTU3 OTU2 OTU3 E. coli Humans OTU4 YeastThursday, September 6, 12
  • 11. Phylotyping E. coli Humans YeastThursday, September 6, 12
  • 12. Phylotyping Just E. coli Humans Phylogeny YeastThursday, September 6, 12
  • 13. Phylotyping B A Cluster C Just B E. coli Humans Phylogeny A Yeast OTUs C OTU2 OTU1 OTU1 OTU4 OTU3 OTU2 OTU3 E. coli Humans OTU4 YeastThursday, September 6, 12
  • 14. Phylotyping • OTUs • Taxonomic lists • Relative abundance of taxa • Ecological metrics (alpha and beta diversity) • Phylogenetic metrics • Binning • Identification of novel groups • Clades • Rates of change • LGT • Convergence • PD • Phylogenetic ecology (e.g., Unifrac)Thursday, September 6, 12
  • 15. What’s New in PhylotypingThursday, September 6, 12
  • 16. What’s New in Phylotyping I • More PCR products • Deeper sequencing • The rare biosphere • Relative abundance estimates • More samples (with barcoding) • Times series • Spatially diverse sampling • Fine scale samplingThursday, September 6, 12
  • 17. Earth Microbiome ProjectThursday, September 6, 12
  • 18. Thursday, September 6, 12
  • 19. Things You Could Do • Mississippi River: 2320 miles longThursday, September 6, 12
  • 20. Things You Could Do • Mississippi River: 2320 miles long • 1 site / mile • 3 samples / site • 6960 samples • rRNA PCR w/ barcodes • metagenomics w/ barcodes • Miseq Run: • 30 million sequence reads • 4310 sequences / sample • Hiseq 2000 • 6 billion sequence reads • 862,068 sequences / sampleThursday, September 6, 12
  • 21. Things You Could Do • Mississippi River: 12,249,600 feet long • 1 site / 500 feet • 3 samples / site • 73497 samples • rRNA PCR w/ barcodes • metagenomics w/ barcodes • Miseq Run: • 30 million sequence reads • 408 sequences / sample • Hiseq 2000 • 6 billion sequence reads • 81,635 sequences / sampleThursday, September 6, 12
  • 22. What’s New in Phylotyping II • Metagenomics avoids biases of rRNA PCR shotgun sequenceThursday, September 6, 12
  • 23. Metagenomic Phylotyping B A Cluster C Just B E. coli Humans Phylogeny A Yeast OTUs C OTU2 OTU1 OTU1 OTU4 OTU3 OTU2 OTU3 E. coli Humans OTU4 YeastThursday, September 6, 12
  • 24. Phylogenetic Challenge ??Thursday, September 6, 12
  • 25. Phylogenetic Challenge ??Thursday, September 6, 12
  • 26. Phylogenetic Challenge Multiple approachesThursday, September 6, 12
  • 27. Method 1: Each is an islandThursday, September 6, 12
  • 28. Method 1: Each is an island • Build alignment, models, trees for full length seqs • Analyze fragmented reads one at a timeThursday, September 6, 12
  • 29. Method 1: Each is an island • Build alignment, models, trees for full length seqs • Analyze fragmented reads one at a timeThursday, September 6, 12
  • 30. Method 1: Each is an island • Build alignment, models, trees for full length seqs • Analyze fragmented reads one at a timeThursday, September 6, 12
  • 31. STAP ss-rRNA Taxonomy Pip Figure 1. A flow chart of the STAP pipeline. doi:10.1371/journal.pone.0002566.g001 STAP database, and the query sequence is aligned to them using a the CLUSTALW profile alignment algorithm [40] as described w above for domain assignment. By adapting the profile alignment s a t o G t t Each sequence s T c analyzed separately a q c e b b S p a Figure 2. Domain assignment. In Step 1, STAP assigns a domain to t each query sequence based on its position in a maximum likelihood d tree of representative ss-rRNA sequences. Because the tree illustrated ‘ here is not rooted, domain assignment would not be accurate and s reliable (sequence similarity based methods cannot make an accurate s assignment in this case either). However the figure illustrates an important role of the tree-based domain assignment step, namely s automatic identification of deep-branching environmental ss-rRNAs. d doi:10.1371/journal.pone.0002566.g002 a PLoS ONE | www.plosone.org 5 Wu et al. 2008 PLoS OneFigure 1. A flow chart of the STAP pipeline.Thursday, September 6, 12
  • 32. AMPHORA Wu and Eisen Genome Biology 2008 9:R151 doi:10.1186/ gb-2008-9-10-r151 Guide treeThursday, September 6, 12
  • 33. Phylotyping w/ Proteins Wu and Eisen Genome Biology 2008 9:R151 doi:10.1186/gb-2008-9-10-r151Thursday, September 6, 12
  • 34. Method 2: Most in the FamilyThursday, September 6, 12
  • 35. Phylogenetic Challenge xxxxxxxxxxxxxxxxxxxxxxx xxxxxx xxxxxxxxxxxxx xxxxxxxxxxxxxx xxxxxxxxxxxxxx ??Thursday, September 6, 12
  • 36. Method 2: Most in family xxxxxxxxxxxxxxxxxxxxxxx xxxxxx xxxxxxxxxxxxx xxxxxxxxxxxxxx xxxxxxxxxxxxxx One tree for those w/ overlapThursday, September 6, 12
  • 37. rRNA in Sargasso Metagenome Venter et al., Science 304: 66. 2004Thursday, September 6, 12
  • 38. RecA Phylotyping in Sargasso Data Venter et al., Science 304: 66. 2004Thursday, September 6, 12
  • 39. Weighted % of Clones 0 0.125 0.250 0.375 0.500 Al ph ap ro t eo Be ba ta ctThursday, September 6, 12 pr er ot ia eo G 304: 66. 2004 am b ac m t er ap ia ro Ep t eo si ba lo ct Venter et al., Science np er ro ia eo t De ba lta ct pr er ot ia eo ba C EFG ct ya er no ia ba ct er Fi ia rm ic EFTu ut es Ac tin ob ac te ria C hl HSP70 or ob i C Major Phylogenetic Group FB Sargasso Phylotypes C RecA hl or of le xi Sp iro ch ae te s RpoB Fu so ba De ct in er ia oc Sargasso Phylotyping oc cu s- rRNA Th Eu er ry m ar u ch s ae C ot a re na rc ha eo ta
  • 40. STAP, QIIME, Mothur ss-rRNA Taxonomy Pip Combine all into one alignment Figure 1. A flow chart of the STAP pipeline. doi:10.1371/journal.pone.0002566.g001Thursday, September 6, 12
  • 41. Method 3: All in the familyThursday, September 6, 12
  • 42. Phylogenetic Challenge ??Thursday, September 6, 12
  • 43. Phylogenetic Challenge A single tree with everything?Thursday, September 6, 12
  • 44. rRNA analysis B A Cluster C Just B E. coli Humans Phylogeny A Yeast OTUs C OTU2 OTU1 OTU1 OTU4 OTU3 OTU2 OTU3 E. coli Humans OTU4 YeastThursday, September 6, 12
  • 45. PhylOTU Finding Meta Figure 1. PhylOTU Workflow. Computational processes are represented as squares and databases are represented as cylinders in workflow of PhylOTU. See Results section for details. Sharpton TJ, Riesenfeld SJ, Kembel SW, Ladau J, ODwyer JP, Green JL, Eisen JA, Pollard KS. (2011) doi:10.1371/journal.pcbi.1001061.g001 PhylOTU: A High-Throughput Procedure Quantifies Microbial Community Diversity and Resolves Novel Taxa from Metagenomic used toPLoS Comput Biol 7(1): e1001061. doi:10.1371/journal.pcbi.1001061 alignment Data. build the profile, resulting in a multiple PD versus PID clustering, 2) to explore overlap betw sequence alignment of full-length reference sequences and clusters and recognized taxonomic designations, andThursday, September 6, 12 metagenomic reads. The final step of the alignment process is a the accuracy of PhylOTU clusters from shotgun re
  • 46. RecA, RpoB in GOS GOS 1 GOS 2 GOS 3 GOS 4 Wu D, Wu M, Halpern A, Rusch DB, Yooseph S, et al. (2011) Stalking the Fourth Domain in Metagenomic Data: Searching for, Discovering, GOS 5 and Interpreting Novel, Deep Branches in Marker Gene Phylogenetic Trees. PLoS ONE 6(3): e18011. doi:10.1371/journal.pone.0018011Thursday, September 6, 12
  • 47. Phylosift/ pplacer Aaron Darling, Guillaume Jospin, Holly Bik, Erik Matsen, Eric Lowe, and othersThursday, September 6, 12
  • 48. Method 4: All in the genomeThursday, September 6, 12
  • 49. Multiple Genes? A single tree with everything?Thursday, September 6, 12
  • 50. Kembel Combiner Kembel SW, Eisen JA, Pollard KS, Green JL (2011) The Phylogenetic Diversity of Metagenomes. PLoS ONE 6(8): e23214. doi:10.1371/journal.pone.0023214Thursday, September 6, 12
  • 51. typically used as a qualitative measure because duplicate s quences are usually removed from the tree. However, the test may be used in a semiquantitative manner if all clone Kembel Combiner even those with identical or near-identical sequences, are i cluded in the tree (13). Here we describe a quantitative version of UniFrac that w call “weighted UniFrac.” We show that weighted UniFrac b haves similarly to the FST test in situations where both a FIG. 1. Calculation of the unweighted and the weighted UniFr measures. Squares and circles represent sequences from two differe environments. (a) In unweighted UniFrac, the distance between t circle and square communities is calculated as the fraction of t branch length that has descendants from either the square or the circ environment (black) but not both (gray). (b) In weighted UniFra branch lengths are weighted by the relative abundance of sequences the square and circle communities; square sequences are weight twice as much as circle sequences because there are twice as many tot circle sequences in the data set. The width of branches is proportion to the degree to which each branch is weighted in the calculations, an gray branches have no weight. Branches 1 and 2 have heavy weigh since the descendants are biased toward the square and circles, respe tively. Branch 3 contributes no value since it has an equal contributio from circle and square sequences after normalization. Kembel SW, Eisen JA, Pollard KS, Green JL (2011) The Phylogenetic Diversity of Metagenomes. PLoS ONE 6(8): e23214. doi:10.1371/journal.pone.0023214Thursday, September 6, 12
  • 52. Uses of Phylogeny in Genomics and Metagenomics Example 2: Functional Diversity and Functional PredictionsThursday, September 6, 12
  • 53. PHYLOGENENETIC PREDICTION OF GENE FUNCTION EXAMPLE A METHOD EXAMPLE B 2A CHOOSE GENE(S) OF INTEREST 5 3A 1 3 4 2B 2 IDENTIFY HOMOLOGS 5 1A 2A 1B 3B 6 ALIGN SEQUENCES 1A 2A 3A 1B 2B 3B 1 2 3 4 5 6 CALCULATE GENE TREE Duplication? 1A 2A 3A 1B 2B 3B 1 2 3 4 5 6 OVERLAY KNOWN FUNCTIONS ONTO TREE Duplication? 2A 3A 1B 2B 3B 1 2 3 4 5 6 1A INFER LIKELY FUNCTION OF GENE(S) OF INTEREST Ambiguous Duplication? Species 1 Species 2 Species 3 Based on 1A 1B 2A 2B 3A 3B 1 2 3 4 5 6 ACTUAL EVOLUTION (ASSUMED TO BE UNKNOWN) Eisen, 1998 Genome Res 8: Duplication 163-167.Thursday, September 6, 12
  • 54. Diversity of Proteorhodopsins Venter et al., 2004. Science 304: 66.Thursday, September 6, 12
  • 55. Improving Functional Predictions • Same methods discussed for phylotyping improve phylogenomic functional prediction for protein families • Increase in sequence diversity helps tooThursday, September 6, 12
  • 56. NMF in MetagenomesCharacterizing the niche-space distributions of components 0 .1 0 .2 0 .3 0 .4 0 .5 0 .6 0 .2 0 .4 0 .6 0 .8 1 .0 Polyne sia Archipe la gos_ G S 0 4 8 a _ C ora l R e e f India n O ce a n_ G S 1 2 0 _ O pe n O ce a n Polyne sia Archipe la gos_ G S 0 4 9 _ C oa sta l G a la pa gos Isla nds_ G S 0 2 6 _ O pe n O ce a n India n O ce a n_ G S 1 1 9 _ O pe n O ce a n G e ne ra l C a ribbe a n S e a _ G S 0 1 5 _ C oa sta l C a ribbe a n S e a _ G S 0 1 9 _ C oa sta l India n O ce a n_ G S 1 1 4 _ O pe n O ce a n H igh E a ste rn Tropica l Pa cific_ G S 0 2 3 _ O pe n O ce a n M e dium India n O ce a n_ G S 1 1 0 a _ O pe n O ce a n India n O ce a n_ G S 1 0 8 a _ La goon R e e f Low C a ribbe a n S e a _ G S 0 1 8 _ O pe n O ce a n NA G a la pa gos Isla nds_ G S 0 3 4 _ C oa sta l India n O ce a n_ G S 1 2 2 a _ O pe n O ce a n India n O ce a n_ G S 1 2 1 _ O pe n O ce a n C a ribbe a n S e a _ G S 0 1 7 _ O pe n O ce a n India n O ce a n_ G S 1 1 2 a _ O pe n O ce a n India n O ce a n_ G S 1 1 3 _ O pe n O ce a n India n O ce a n_ G S 1 4 8 _ F ringing R e e f C a ribbe a n S e a _ G S 0 1 6 _ C oa sta l S e a India n O ce a n_ G S 1 2 3 _ O pe n O ce a n India n O ce a n_ G S 1 4 9 _ H a rbor G a la pa gos Isla nds_ G S 0 2 7 _ C oa sta l E a ste rn Tropica l Pa cific_ G S 0 2 2 _ O pe n O ce a n W a te r de pth S ites S a rga sso S e a _ G S 0 0 1 c_ O pe n O ce a n G a la pa gos Isla nds_ G S 0 3 5 _ C oa sta l G a la pa gos Isla nds_ G S 0 3 0 _ W a rm S e e p G a la pa gos Isla nds_ G S 0 2 9 _ C oa sta l >4000m G a la pa gos Isla nds_ G S 0 3 1 _ C oa sta l upwe lling India n O ce a n_ G S 1 1 7 a _ C oa sta l sa m ple 2000!4000m G a la pa gos Isla nds_ G S 0 2 8 _ C oa sta l 900!2000m G a la pa gos Isla nds_ G S 0 3 6 _ C oa sta l 100!200m Polyne sia Archipe la gos_ G S 0 5 1 _ C ora l R e e f Atoll N orth Am e rica n E a st C oa st_ G S 0 1 4 _ C oa sta l 20!100m N orth Am e rica n E a st C oa st_ G S 0 0 6 _ E stua ry 0!20m E a ste rn Tropica l Pa cific_ G S 0 2 1 _ C oa sta l N orth Am e rica n E a st C oa st_ G S 0 0 9 _ C oa sta l N orth Am e rica n E a st C oa st_ G S 0 1 1 _ E stua ry N orth Am e rica n E a st C oa st_ G S 0 0 8 _ C oa sta l N orth Am e rica n E a st C oa st_ G S 0 1 3 _ C oa sta l N orth Am e rica n E a st C oa st_ G S 0 0 4 _ C oa sta l N orth Am e rica n E a st C oa st_ G S 0 0 7 _ C oa sta l N orth Am e rica n E a st C oa st_ G S 0 0 3 _ C oa sta l N orth Am e rica n E a st C oa st_ G S 0 0 2 _ C oa sta l N orth Am e rica n E a st C oa st_ G S 0 0 5 _ E m baym e nt Co Co Co Co Co Chlorophyll Salinity Temperature Water Depth Sample Depth Insolation mp mp mp mp mp on on on on on en en en en en t1 t2 t3 t4 t5 (a) (b) (c) Figure 3: a) Niche-space distributions for our five components (H T );Weitz,site- Non-negative c) environmental variables for the sites. w/ matrices Dushoff, ˆ ˆ similarity matrix (H T H); matrix factorization b) the Langille, Neches, The are aligned so that et al. Inrow corresponds to One. site in each matrix. Sites are Jiang the same press PLoS the same Levin, etc ordered by applying spectral reordering to the similarity matrix (see Materials and Methods). Rows are aligned across the three matrices.Thursday, September 6, 12
  • 57. Uses of Phylogeny in Genomics and Metagenomics Example 3: Selecting Organisms for StudyThursday, September 6, 12
  • 58. GEBA http://www.jgi.doe.gov/programs/GEBA/pilot.htmlThursday, September 6, 12
  • 59. GEBA: Components • Project overview (Phil Hugenholtz, Nikos Kyrpides, Jonathan Eisen, Eddy Rubin, Jim Bristow) • Project management (David Bruce, Eileen Dalin, Lynne Goodwin) • Culture collection and DNA prep (DSMZ, Hans-Peter Klenk) • Sequencing and closure (Eileen Dalin, Susan Lucas, Alla Lapidus, Mat Nolan, Alex Copeland, Cliff Han, Feng Chen, Jan-Fang Cheng) • Annotation and data release (Nikos Kyrpides, Victor Markowitz, et al) • Analysis (Dongying Wu, Kostas Mavrommatis, Martin Wu, Victor Kunin, Neil Rawlings, Ian Paulsen, Patrick Chain, Patrik D’Haeseleer, Sean Hooper, Iain Anderson, Amrita Pati, Natalia N. Ivanova, Athanasios Lykidis, Adam Zemla) • Adopt a microbe education project (Cheryl Kerfeld) • Outreach (David Gilbert) • $$$ (DOE, Eddy Rubin, Jim Bristow)Thursday, September 6, 12
  • 60. GEBA Now • 300+ genomes • Rich sampling of major groups of cultured organismsThursday, September 6, 12
  • 61. GEBA Lesson 1Thursday, September 6, 12
  • 62. Protein Family Rarefaction • Take data set of multiple complete genomes • Identify all protein families using MCL • Plot # of genomes vs. # of protein familiesThursday, September 6, 12
  • 63. Wu et al. 2009 Nature 462, 1056-1060Thursday, September 6, 12
  • 64. Wu et al. 2009 Nature 462, 1056-1060Thursday, September 6, 12
  • 65. Wu et al. 2009 Nature 462, 1056-1060Thursday, September 6, 12
  • 66. Wu et al. 2009 Nature 462, 1056-1060Thursday, September 6, 12
  • 67. Wu et al. 2009 Nature 462, 1056-1060Thursday, September 6, 12
  • 68. Synapomorphies existWu et al. 2009 Nature 462, 1056-1060Thursday, September 6, 12
  • 69. GEBA Lesson 2Thursday, September 6, 12
  • 70. Weighted % of Clones 0 0.125 0.250 0.375 0.500 Al ph ap ro t eo Be ba ta ct er pr iaThursday, September 6, 12 ot eo G b am ac m t er ap ia ro Ep teo si ba lo ct np er ro ia eo t De ba lta ct pr er ot ia eo ba C ct ya er no ia ba ct er Fi ia rm ic ut es Ac tin ob ac te ria C hl or ob i C Major Phylogenetic Group FB Sargasso Phylotypes phylotyping & C hl or GEBA benefits of le xi Sp iro ch ae te Fu s so ba De ct in er ia oc oc cu Metagenomic Phylotyping functional prediction s- Th Eu er ry m ar u ch s ae C ot a re na rc ha eo ta Venter et al., Science 304: 66-74. 2004 EFG EFTu rRNA RecA RpoB HSP70
  • 71. GEBA improves genome annotation • Took 56 GEBA genomes and compared results vs. 56 randomly sampled new genomes • Better definition of protein family sequence “patterns” • Greatly improves “comparative” and “evolutionary” based predictions • Conversion of hypothetical into conserved hypotheticals • Linking distantly related members of protein families • Improved non-homology predictionThursday, September 6, 12
  • 72. Weighted % of Clones 0 0.125 0.250 0.375 0.500 Al ph ap ro t eo Be ba ta ct er pr iaThursday, September 6, 12 ot eo G b am ac m t er ap ia ro Ep teo si ba lo ct np er ro ia eo t De ba lta ct pr er ot ia eo ba C ct ya er no ia ba ct er Fi ia rm ic ut es Ac tin ob ac te ria C hl or ob i But not a lot C Major Phylogenetic Group FB Sargasso Phylotypes C hl or of le xi Sp iro ch ae te Fu s so ba De ct in er ia oc oc cu Metagenomic Phylotyping s- Th Eu er ry m ar u ch s ae C ot a re na rc ha eo ta Venter et al., Science 304: 66-74. 2004 EFG EFTu rRNA RecA RpoB HSP70
  • 73. Improving Functional PredictionsThursday, September 6, 12
  • 74. Sifting Families Representative Genomes B A Extract Protein New Genomes Annotation Extract All v. All Protein BLAST Annotation Homology Screen for (MCL) C Clustering Homologs SFams HMMs Align & Build Sharpton et al. submitted Figure 1 HMMsThursday, September 6, 12
  • 75. Improving PhylotypingThursday, September 6, 12
  • 76. More Markers Phylogenetic group Genome Gene Maker Number Number Candidates Archaea 62 145415 106 Actinobacteria 63 267783 136 Alphaproteobacteria 94 347287 121 Betaproteobacteria 56 266362 311 Gammaproteobacteria 126 483632 118 Deltaproteobacteria 25 102115 206 Epislonproteobacteria 18 33416 455 Bacteriodes 25 71531 286 Chlamydae 13 13823 560 Chloroflexi 10 33577 323 Cyanobacteria 36 124080 590 Firmicutes 106 312309 87 Spirochaetes 18 38832 176 Thermi 5 14160 974 Thermotogae 9 17037 684Thursday, September 6, 12
  • 77. Better Reference Tree Morgan et al. submittedThursday, September 6, 12
  • 78. GEBA Lesson 3 We have still only scratched the surface of microbial diversityThursday, September 6, 12
  • 79. PD: All From Wu et al. 2009 Nature 462, 1056-1060Thursday, September 6, 12
  • 80. GEBA uncultured Number of SAGs from Candidate Phyla 406 1 OD1 OP1 OP3 SAR Site A: Hydrothermal vent 4 1 - - Site B: Gold Mine 6 13 2 - Site C: Tropical gyres (Mesopelagic) - - - 2 Site D: Tropical gyres (Photic zone) 1 - - - Sample collections at 4 additional sites are underway. Phil Hugenholtz 76Thursday, September 6, 12
  • 81. GEBA Lesson IV Need Experiments from Across the Tree of Life tooThursday, September 6, 12
  • 82. ConclusionThursday, September 6, 12
  • 83. Thursday, September 6, 12
  • 84. MICROBESThursday, September 6, 12
  • 85. Acknowledgements • $$$ • DOE • NSF • GBMF • Sloan • DARPA • DSMZ • DHS • People, places • DOE JGI: Eddy Rubin, Phil Hugenholtz, Nikos Kyrpides • UC Davis: Aaron Darling, Dongying Wu, Holly Bik, Russell Neches, Jenna Morgan-Lang • Other: Jessica Green, Katie Pollard, Martin Wu, Tom Slezak, Jack Gilbert, Steven Kembel, J. Craig Venter, Naomi Ward, Hans-Peter KlenkThursday, September 6, 12