Diversity and Evolution of Microbes Through Phylogenomics
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Talk by Jonathan Eisen of UC Davis at the J. Craig Venter Institute 1/18/11.
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Diversity and Evolution of Microbes Through Phylogenomics
- 1. Phylogenomics and the Diversity and Diversification of Microbes Jonathan A. Eisen UC Davis JCVI West January 18, 2010
- 2. My Obsessions Jonathan A. Eisen UC Davis JCVI West January 18, 2010
- 4. Social Networking in Science HOME PAGE MY TIMES TODAY'S PAPER VIDEO MOST POPULAR TIMES TOPICS Welcome, fcollins Member Center Log Out Sunday, April 1, 2007 Health WORLD U.S. N.Y. / REGION BUSINESS TECHNOLOGY SCIENCE HEALTH SPORTS OPINION ARTS STYLE TRAVEL JOBS REAL ESTATE AUTOS FITNESS & NUTRITION HEALTH CARE POLICY MENTAL HEALTH & BEHAVIOR Scientist Reveals Secret of the Ocean: It's Him By NICHOLAS WADE Published: April 1, 2007 PRINT nytimes.com/sports Maverick scientist J. Craig Venter has done it again. It was just a few years SINGLE-PAGE ago that Dr. Venter announced that the human genome sequenced by Celera SAVE Genomics was in fact, mostly his own. And now, Venter has revealed a second SHARE twist in his genomic self-examination. Venter was discussing his Global SHARE Ocean Voyage, in which he used his personal yacht to collect ocean water samples from around the world. He then used large filtration units to collect How good is your bracket? Compare your tournament picks to choices from members of The New York Times sports microbes from the water samples which were then brought back to his high desk and other players. tech lab in Rockville, MD where he used the same methods that were used to Also in Sports: The Bracket Blog - all the news leading up to the Final sequence the human genome to study the genomes of the 1000s of ocean Four dwelling microbes found in each sample. In discussing the sampling methods, Venter let slip his Bats Blog: Spring training updates Play Magazine: How to build a super athlete latest attack on the standards of science – some of the samples were in fact not from the ocean, but were from microbial habitats in and on his body. “The human microbiome is the next frontier,” Dr. Venter said. “The ocean voyage was just a cover. My main goal has always been to work on the microbes that live in and on people. And now that my genome is nearly complete, why not use myself as the model for human microbiome studies as well. ” It is certainly true that in the last few years, the microbes that live in and on people have become a hot research topic. So hot that the same people who were involved in the race to sequence the human
- 6. Phylogenomics of Novelty Origin of New Causes and Effects Functions and of Variation in Processes Processes •From within • Causes •New genes •Mutation rates •Changes in old genes • Repair and •Changes in pathways recombination processes •From outside • Recombination rates •Lateral transfer •Effects •Symbioses •Evolvability •Communities •Ecology Species Evolution •Genome Evolution •Phylogenetic history •Vertical vs. horizontal descent •Needed to track gain/loss of processes, infer convergence
- 7. Simpler Description • How do new functions originate in microbes? • How do these processes vary both within and between species? • What are the effects of this variation in evolvability on biology, ecology, etc?
- 8. Examples (Blasts from the TIGR past)
- 9. Wolbachia pipientis wMel • Wolbachia are obligate, maternally transmitted intracellular symbionts • Wolbachia infect many invertebrate species – Many cause male specific deleterious effects – Model system for studying sex ratio changes in hosts – Some are mutualistic (e.g., in filarial nematodes) • wMel selected as model system because it infects Drosophila melanogaster
- 10. Genome Completed Wu et al., PLoS Biology 2004
- 11. Wolbachia Overrun by Mobile Elements Repeat Size Copies Protein motifs/families IS Family Possible Terminal Inverted Repeat Sequence Class (Median) 1 1512 3 Transposase IS4 5’ ATACGCGTCAAGTTAAG 3’ 2 360 12 - New 5’ GGCTTTGTTGCATCGCTA 3’ 3 858 9 Transposase IS492/IS110 5’ GGCTTTGTTGCAT 3’ 4 1404.5 4 Conserved hypothetical, New 5’ ATACCGCGAWTSAWTCGCGGTAT 3’ phage terminase 5 1212 15 Transposase IS3 5’ TGACCTTACCCAGAAAAAGTGGAGAGAAAG 3’ 6 948 13 Transposase IS5 5’ AGAGGTTGTCCGGAAACAAGTAAA 3’ 7 2405.5 8 RT/maturase - 8 468 45 - - 9 817 3 conserved hypothetical, ISBt12 transposase 10 238 2 ExoD - 11 225 2 RT/maturase - 12 1263 4 Transposase ??? 13 572.5 2 Transposase ??? None detected 14 433 2 Ankyrin - 15 201 2 - - 16 1400 6 RT/maturase - 17 721 2 transposase IS630 18 1191.5 2 EF-Tu - 19 230 2 hypothetical - Wu et al., PLoS Biology 2004
- 12. Glassy Winged Sharpshooter • Obligate xylem feeder • Can transmit Pierce’s Disease agent • Potential bioterror agent • Needs to get amino- acids and other nutrients from symbionts like aphids
- 13. Sulcia makes amino acids Baumannia makes vitamins and cofactors Wu et al. 2006 PLoS Biology 4: e188. Collaboration with Nancy Moran’ s Lab
- 14. Higher Evolutionary Rates in Clade Wu et al. 2006 PLoS Biology 4: e188. Collaboration with Nancy Moran’ s Lab
- 15. Variation in Evolution Rates MutS MutL + + + + + + + + _ _ _ _ Wu et al. 2006 PLoS Biology 4: e188. Collaboration with Nancy Moran’ s Lab
- 16. Evolution and Genome Processing • Probably exists as a defense mechanism • Analogous to RIPPING and heterochromatin silencing • Presence of repetitive DNA in MAC but not TEs suggests the mechanism involves targeting foreign DNA • Thus unlike RIPPING ciliate processing does not limit diversification by duplication Eisen et al. 2006. PLoS Biology.
- 17. The X-Files Pseudomonas Streps B. subt vs. Staph 13623200 3000 9952000 2500 13622725 9950425 2000 Series1 1500 9948850 Series1 13622250 1000 9947275 13621775 500 9945700 0 13621300 2632200 2632700 2633200 2633700 2634200 2634700 2635200 2635700 2636200 2636700 0 2125 4250 6375 8500 0 625 1250 1875 2500 M. tb vs. M. leprae Pyrococcus Thermoplasmas 4000000 Mycobacterium tuberculosis 3000000 2000000 1000000 0 0 1000000 2000000 3000000 Mycobacterium leprae
- 18. Data for “Phylogenomics of Novelty” studies?
- 25. As of 2002
- 26. As of 2002 Proteobacteria TM6 OS-K • At least 40 Acidobacteria Termite Group OP8 phyla of Nitrospira Bacteroides bacteria Chlorobi Fibrobacteres Marine GroupA WS3 Gemmimonas Firmicutes Fusobacteria Actinobacteria OP9 Cyanobacteria Synergistes Deferribacteres Chrysiogenetes NKB19 Verrucomicrobia Chlamydia OP3 Planctomycetes Spriochaetes Coprothmermobacter OP10 Thermomicrobia Chloroflexi TM7 Deinococcus-Thermus Dictyoglomus Aquificae Thermudesulfobacteria Thermotogae OP1 Based on OP11 Hugenholtz, 2002
- 27. As of 2002 Proteobacteria TM6 OS-K • At least 40 Acidobacteria Termite Group OP8 phyla of Nitrospira Bacteroides bacteria Chlorobi Fibrobacteres Marine GroupA • Genome WS3 Gemmimonas Firmicutes sequences are Fusobacteria Actinobacteria mostly from OP9 Cyanobacteria Synergistes three phyla Deferribacteres Chrysiogenetes NKB19 Verrucomicrobia Chlamydia OP3 Planctomycetes Spriochaetes Coprothmermobacter OP10 Thermomicrobia Chloroflexi TM7 Deinococcus-Thermus Dictyoglomus Aquificae Thermudesulfobacteria Thermotogae OP1 Based on OP11 Hugenholtz, 2002
- 28. As of 2002 Proteobacteria TM6 OS-K • At least 40 Acidobacteria Termite Group OP8 phyla of Nitrospira Bacteroides bacteria Chlorobi Fibrobacteres Marine GroupA • Genome WS3 Gemmimonas Firmicutes sequences are Fusobacteria Actinobacteria mostly from OP9 Cyanobacteria Synergistes three phyla Deferribacteres Chrysiogenetes NKB19 • Some other Verrucomicrobia Chlamydia OP3 phyla are Planctomycetes Spriochaetes only sparsely Coprothmermobacter OP10 Thermomicrobia sampled Chloroflexi TM7 Deinococcus-Thermus Dictyoglomus Aquificae Thermudesulfobacteria Thermotogae OP1 Based on OP11 Hugenholtz, 2002
- 29. As of 2002 Proteobacteria TM6 OS-K • At least 40 Acidobacteria Termite Group OP8 phyla of Nitrospira Bacteroides bacteria Chlorobi Fibrobacteres Marine GroupA • Genome WS3 Gemmimonas Firmicutes sequences are Fusobacteria Actinobacteria mostly from OP9 Cyanobacteria Synergistes three phyla Deferribacteres Chrysiogenetes NKB19 • Some other Verrucomicrobia Chlamydia OP3 phyla are Planctomycetes Spriochaetes only sparsely Coprothmermobacter OP10 Thermomicrobia sampled Chloroflexi TM7 Deinococcus-Thermus Dictyoglomus Aquificae Thermudesulfobacteria Thermotogae OP1 Based on OP11 Hugenholtz, 2002
- 30. Need for Tree Guidance Well Established • Common approach within some eukaryotic groups • Many small projects funded to fill in some bacterial or archaeal gaps • Phylogenetic gaps in bacterial and archaeal projects commonly lamented in literature
- 31. Proteobacteria • NSF-funded TM6 OS-K • At least 40 Tree of Life Acidobacteria Termite Group phyla of OP8 Project Nitrospira Bacteroides bacteria Chlorobi • A genome Fibrobacteres Marine GroupA • Genome WS3 from each of Gemmimonas sequences are Firmicutes eight phyla Fusobacteria mostly from Actinobacteria OP9 Cyanobacteria Synergistes three phyla Deferribacteres Chrysiogenetes NKB19 • Some other Verrucomicrobia Chlamydia OP3 phyla are only Planctomycetes Spriochaetes sparsely Coprothmermobacter OP10 Thermomicrobia sampled Chloroflexi TM7 Deinococcus-Thermus • Solution I: Dictyoglomus Eisen, Ward, Aquificae Thermudesulfobacteria sequence more Robb, Nelson, et Thermotogae phyla OP1 al OP11
- 32. Organisms Selected Phylum Species selected Chrysiogenes Chrysiogenes arsenatis (GCA) Coprothermobacter Coprothermobacter proteolyticus (GCBP) Dictyoglomi Dictyoglomus thermophilum (GD T ) Thermodesulfobacteria Thermodesulfobacterium commune (GTC) Nitrospirae Thermodesulfovibrio yellowstonii (GTY) Thermomicrobia Thermomicrobium roseum (GTR ) Deferribacteres Geovibrio thiophilus (GGT) Synergistes Synergistes jonesii (GSJ)
- 35. Major Lineages of Actinobacteria 2.5 Actinobacteria 2.5.1 Acidimicrobidae 2.5.1 Acidimicrobidae 2.5.1.1 Unclassified 2.5.1.2 "Microthrixineae 2.5.1.1 Unclassified 2.5.1.3 Acidimicrobineae 2.5.1.3.1 Unclassified 2.5.1.2 "Microthrixineae 2.5.1.3.2 Acidimicrobiaceae 2.5.1.4 BD2-10 2.5.1.3 Acidimicrobineae 2.5.1.5 EB1017 2.5.2 Actinobacteridae 2.5.1.4 BD2-10 2.5.2.1 Unclassified 2.5.2.10 Ellin306/WR160 2.5.1.5 EB1017 2.5.2.11 Ellin5012 2.5.2.12 Ellin5034 2.5.2 Actinobacteridae 2.5.2.13 Frankineae 2.5.2.13.1 Unclassified 2.5.2.1 Unclassified 2.5.2.13.2 Acidothermaceae 2.5.2.10 Ellin306/WR160 2.5.2.13.3 2.5.2.13.4 Ellin6090 Frankiaceae 2.5.2.11 Ellin5012 2.5.2.13.5 2.5.2.13.6 Geodermatophilaceae Microsphaeraceae 2.5.2.12 Ellin5034 2.5.2.13.7 2.5.2.14 Sporichthyaceae Glycomyces 2.5.2.13 Frankineae 2.5.2.15 2.5.2.15.1 Intrasporangiaceae Unclassified 2.5.2.14 Glycomyces 2.5.2.15.2 2.5.2.15.3 Dermacoccus Intrasporangiaceae 2.5.2.15 Intrasporangiaceae 2.5.2.16 2.5.2.17 Kineosporiaceae Microbacteriaceae 2.5.2.16 Kineosporiaceae 2.5.2.17.1 2.5.2.17.2 Unclassified Agrococcus 2.5.2.17 Microbacteriaceae 2.5.2.17.3 2.5.2.18 Agromyces Micrococcaceae 2.5.2.18 Micrococcaceae 2.5.2.19 2.5.2.2 Micromonosporaceae Actinomyces 2.5.2.19 Micromonosporaceae 2.5.2.20 2.5.2.20.1 Propionibacterineae Unclassified 2.5.2.2 Actinomyces 2.5.2.20.2 2.5.2.20.3 Kribbella Nocardioidaceae 2.5.2.20 Propionibacterineae 2.5.2.20.4 2.5.2.21 Propionibacteriaceae Pseudonocardiaceae 2.5.2.21 Pseudonocardiaceae 2.5.2.22 2.5.2.22.1 Streptomycineae Unclassified 2.5.2.22 Streptomycineae 2.5.2.22.2 2.5.2.22.3 Kitasatospora Streptacidiphilus 2.5.2.23 Streptosporangineae 2.5.2.23 2.5.2.23.1 Streptosporangineae Unclassified 2.5.2.3 Actinomycineae 2.5.2.23.2 2.5.2.23.3 Ellin5129 Nocardiopsaceae 2.5.2.4 Actinosynnemataceae 2.5.2.23.4 2.5.2.23.5 Streptosporangiaceae Thermomonosporaceae 2.5.2.5 Bifidobacteriaceae 2.5.2.3 Actinomycineae 2.5.2.4 Actinosynnemataceae 2.5.2.6 Brevibacteriaceae 2.5.2.5 Bifidobacteriaceae 2.5.2.6 Brevibacteriaceae 2.5.2.7 Cellulomonadaceae 2.5.2.7 Cellulomonadaceae 2.5.2.8 Corynebacterineae 2.5.2.8 Corynebacterineae 2.5.2.8.1 Unclassified 2.5.2.8.2 Corynebacteriaceae 2.5.2.9 Dermabacteraceae 2.5.2.8.3 Dietziaceae 2.5.2.8.4 Gordoniaceae 2.5.3 Coriobacteridae 2.5.2.8.5 Mycobacteriaceae 2.5.2.8.6 Rhodococcus 2.5.3.1 Unclassified 2.5.2.8.7 Rhodococcus 2.5.2.8.8 Rhodococcus 2.5.3.2 Atopobiales 2.5.2.9 Dermabacteraceae 2.5.2.9.1 Unclassified 2.5.3.3 Coriobacteriales 2.5.2.9.2 Brachybacterium 2.5.2.9.3 Dermabacter 2.5.3.4 Eggerthellales 2.5.3 Coriobacteridae 2.5.3.1 Unclassified 2.5.4 OPB41 2.5.3.2 Atopobiales 2.5.3.3 Coriobacteriales 2.5.5 PK1 2.5.3.4 Eggerthellales 2.5.4 OPB41 2.5.6 Rubrobacteridae 2.5.5 PK1 2.5.6 Rubrobacteridae 2.5.6.1 Unclassified 2.5.6.1 Unclassified 2.5.6.2 "Thermoleiphilaceae 2.5.6.2 "Thermoleiphilaceae 2.5.6.2.1 Unclassified 2.5.6.2.2 Conexibacter 2.5.6.3 MC47 2.5.6.2.3 XGE514 2.5.6.3 MC47 2.5.6.4 Rubrobacteraceae 2.5.6.4 Rubrobacteraceae
- 36. Proteobacteria • NSF-funded TM6 OS-K • At least 40 Tree of Life Acidobacteria Termite Group phyla of bacteria OP8 Project Nitrospira • Genome Bacteroides • A genome Chlorobi Fibrobacteres sequences are Marine GroupA from each of WS3 Gemmimonas mostly from eight phyla Firmicutes Fusobacteria three phyla Actinobacteria OP9 Cyanobacteria • Some other Synergistes Deferribacteres Chrysiogenetes phyla are only NKB19 Verrucomicrobia sparsely Chlamydia OP3 Planctomycetes sampled Spriochaetes Coprothmermobacter • Still highly OP10 Thermomicrobia Chloroflexi biased in terms TM7 Deinococcus-Thermus Dictyoglomus of the tree Aquificae Eisen & Ward, PIs Thermudesulfobacteria Thermotogae OP1 OP11
- 37. Proteobacteria • NSF-funded TM6 OS-K • At least 40 Tree of Life Acidobacteria Termite Group phyla of bacteria OP8 Project Nitrospira • Genome Bacteroides • A genome Chlorobi Fibrobacteres sequences are Marine GroupA from each of WS3 Gemmimonas mostly from eight phyla Firmicutes Fusobacteria three phyla Actinobacteria OP9 Cyanobacteria • Some other Synergistes Deferribacteres Chrysiogenetes phyla are only NKB19 Verrucomicrobia sparsely Chlamydia OP3 Planctomycetes sampled Spriochaetes Coprothmermobacter • Same trend in OP10 Thermomicrobia Chloroflexi Archaea TM7 Deinococcus-Thermus Dictyoglomus Aquificae Eisen & Ward, PIs Thermudesulfobacteria Thermotogae OP1 OP11
- 38. Proteobacteria • NSF-funded TM6 OS-K • At least 40 Tree of Life Acidobacteria Termite Group phyla of bacteria OP8 Project Nitrospira • Genome Bacteroides • A genome Chlorobi Fibrobacteres sequences are Marine GroupA from each of WS3 Gemmimonas mostly from eight phyla Firmicutes Fusobacteria three phyla Actinobacteria OP9 Cyanobacteria • Some other Synergistes Deferribacteres Chrysiogenetes phyla are only NKB19 Verrucomicrobia sparsely Chlamydia OP3 Planctomycetes sampled Spriochaetes Coprothmermobacter • Same trend in OP10 Thermomicrobia Chloroflexi Eukaryotes TM7 Deinococcus-Thermus Dictyoglomus Aquificae Eisen & Ward, PIs Thermudesulfobacteria Thermotogae OP1 OP11
- 39. Proteobacteria • NSF-funded TM6 OS-K • At least 40 Tree of Life Acidobacteria Termite Group phyla of bacteria OP8 Project Nitrospira • Genome Bacteroides • A genome Chlorobi Fibrobacteres sequences are Marine GroupA from each of WS3 Gemmimonas mostly from eight phyla Firmicutes Fusobacteria three phyla Actinobacteria OP9 Cyanobacteria • Some other Synergistes Deferribacteres Chrysiogenetes phyla are only NKB19 Verrucomicrobia sparsely Chlamydia OP3 Planctomycetes sampled Spriochaetes Coprothmermobacter • Same trend in OP10 Thermomicrobia Chloroflexi Viruses TM7 Deinococcus-Thermus Dictyoglomus Aquificae Eisen & Ward, PIs Thermudesulfobacteria Thermotogae OP1 OP11
- 40. Phylogenomics of Novelty Origin of New Causes and Effects Functions and of Variation in Processes Processes •From within • Causes •New genes •Mutation rates •Changes in old genes • Repair and •Changes in pathways recombination processes •From outside • Recombination rates •Lateral transfer •Effects •Symbioses •Evolvability •Communities •Ecology Species Evolution •Genome Evolution •Phylogenetic history •Vertical vs. horizontal descent •Needed to track gain/loss of processes, infer convergence
- 41. Phylogenomics of Novelty Origin of New Causes and Effects Functions and of Variation in Processes Processes •From within • Causes •New genes •Mutation rates •Changes in old genes • Repair and •Changes in pathways recombination processes •From outside • Recombination rates •Lateral transfer •Effects •Symbioses •Evolvability •Communities •Ecology Species Evolution •Genome Evolution •Phylogenetic history •Vertical vs. horizontal descent •Needed to track gain/loss of processes, infer convergence
- 44. Proteobacteria • GEBA TM6 OS-K • At least 40 Acidobacteria • A genomic Termite Group OP8 phyla of bacteria encyclopedia Nitrospira Bacteroides • Genome Chlorobi of bacteria Fibrobacteres Marine GroupA sequences are and archaea WS3 Gemmimonas mostly from Firmicutes Fusobacteria three phyla Actinobacteria OP9 Cyanobacteria • Some other Synergistes Deferribacteres Chrysiogenetes phyla are only NKB19 Verrucomicrobia sparsely Chlamydia OP3 Planctomycetes sampled Spriochaetes Coprothmermobacter OP10 • Solution: Really Thermomicrobia Chloroflexi Fill in the Tree TM7 Deinococcus-Thermus Dictyoglomus Aquificae Thermudesulfobacteria Eisen & Ward, PIs Thermotogae OP1 OP11
- 46. GEBA Pilot Project Overview • Identify major branches in rRNA tree for which no genomes are available • Identify those with a cultured representative in DSMZ • DSMZ grew > 200 of these and prepped DNA • Sequence and finish 100+ (covering breadth of bacterial/archaea diversity) • Annotate, analyze, release data • Assess benefits of tree guided sequencing • 1st paper Wu et al in Nature Dec 2009
- 47. GEBA Pilot Project: 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)
- 48. GEBA Phylogenomic Lesson 1 The rRNA Tree of Life is a Useful Tool for Identifying Phylogenetically Novel Genomes
- 49. rRNA Tree of Life Bacteria Archaea Eukaryotes FIgure from Barton, Eisen et al. “Evolution”, CSHL Press. Based on tree from Pace NR, 2003.
- 50. Network of Life Bacteria Archaea Eukaryotes Figure from Barton, Eisen et al. “Evolution”, CSHL Press. Based on tree from Pace NR, 2003.
- 51. Network of Life Bacteria Archaea Eukaryotes Figure from Barton, Eisen et al. “Evolution”, CSHL Press. Based on tree from Pace NR, 2003.
- 52. “Whole Genome” Concatenation Tree w/ AMPHORA See Wu and Eisen, Genome Biology 2008 9: R151 http://bobcat.genomecenter.ucdavis.edu/AMPHORA/
- 53. Wanted: Good Visualization Experts Zimmer. New York Times. 2009
- 54. Compare PD in Trees From Wu et al. 2009 Nature 462, 1056-1060
- 55. PD of rRNA, Genome Trees Similar From Wu et al. 2009 Nature 462, 1056-1060
- 56. 16s Says Hyphomonas is in Rhodobacteriales Badger et al. 2005 Int J System Evol Microbiol 55: 1021-1026.
- 57. WGT and individual gene trees: Its Related to Caulobacterales Badger et al. 2005 Int J System Evol Microbiol 55: 1021-1026.
- 58. GEBA Phylogenomic Lesson 2 Phylogeny-driven genome selection helps discover new genetic diversity
- 59. Network of Life Bacteria Archaea Eukaryotes FIgure from Barton, Eisen et al. “Evolution”, CSHL Press. Based on tree from Pace NR, 2003.
- 60. Protein Family Rarefaction Curves • Take data set of multiple complete genomes • Identify all protein families using MCL • Plot # of genomes vs. # of protein families
- 61. Wu et al. 2009 Nature 462, 1056-1060
- 62. Wu et al. 2009 Nature 462, 1056-1060
- 63. Wu et al. 2009 Nature 462, 1056-1060
- 64. Wu et al. 2009 Nature 462, 1056-1060
- 65. Wu et al. 2009 Nature 462, 1056-1060
- 66. Synapomorphies exist Wu et al. 2009 Nature 462, 1056-1060
- 67. also supported by the GOS diversity seen at the nucleotide environmental settin level across the different sampling sites [30]. Averaged over stood [57,119–121]. A the sites, 14% of the GOS sequence reads from a site are viral sequences (unp unique (at 70% nucleotide identity) to that site [30]. protein clusters cont Figure 11. Rate of Cluster Discovery for Mammals Compared to That for Microbes The x-axis denotes the number of sequences (in thousands), and the y-axis denotes the number of clu are considered for the ‘‘Mammalian’’ dataset, and the plot shows the number of clusters that are hit w ‘‘Mammalian Random’’ dataset, the order of the sequences from the ‘‘Mammalian’’ dataset is rand Yooseph et al. PLoS subsets of2007 similar to that of the mammalian set are considered. datasets, random Biology size doi:10.1371/journal.pbio.0050016.g011
- 68. Structural Novelty • Of the 17000 protein families in the GEBA56, 1800 are novel in sequence (Wu) • Structural modeling suggests many are structurally novel too (D'haeseleer) • 372 being crystallized by the PSI (Kerfeld)
- 69. Phylogenetic Distribution Novelty: Bacterial Actin Related Protein 2 "# & * & 3) 4& &!"#*)$*),+% 5 "# .- 6& 1- !"#$%,$-%)( $- 0& 7"# 0(1.8- 9&!"#$''+-+,',! 5 "# ) * & 0 !"#&$,%+)+-+ :1, $/ !"#$% ! "# & * & $% &!"#$%&'(%() ’() (( + "# - .(/ 01 !"#*+,**'+( , ; "# & * 0 !"#%*+$--( 01, - < "# .- 3.1% !"#%',&'-+) $- &0 ') 2 "# * - .- 1 !"#$'(-%%+&$ $& = "# $.1001 !"#-*$+$(&( !&'( $++ > 0$1, / % 0 !"#&$**+),)-! "# 1.& *$ $++ ; "# 01, & * 0 !"#*+,$*'( - '* 5 "# ) * & 0 !"#&$,%+%-%% :1, $/ $++ 5 "# .- 6& 1- !"#',&+$)* $- 0& !&') ? "# % ) A10(- . !"#&%'%&*%* @- 1* $++ B "# %0#"#%*,-&*'( A1% / )* 2 "# - ) ’) .@1* 0 !"#*-&'''(+ * 5 "# .- 6& 1- !"#',&&*&* $- 0& !&'* $++ ? "# % ) A10(- . !"#$)),)*%, @- 1* $++ ; "# 01, & * 0 !"#*+,$*),! - ; "# $C.1$- / @ !"#&&),(*((- ) +!&' 5 "# .- 6& 1- !"#$++-&%%! $- 0& ), ."# 1(- * 0 !"#$'-+*$((&! , !&', (( ! "# & 1 !"#$-,(%'+-! (C1% 1* (% 5 "# .- 6& 1- !"#$,+$(,& $- 0& $++ 5 "# ) * & 0 !"#&$,%+-,(,! :1, $/ !&'- -) ? "# 0$) & / @ !"#''-+&%$- 4& 4- )% ? "# % ) A10(- . !"#$)),),%) @- 1* () 5 "# .- 6& 1- !"#',&,$$% $- 0& $++ ? "# 0- * & C1* & !"#&$-*$ $(&$ !&'. $++ D"# 61 !"#$-&'*)%&+! 01(& ! "# & 1!"#$-%$ $),) (C1% 1* !&'/ ? "# % ) A1(- . !"#$((&+,*- @- 1* $++ < "# 0$/ %0 !"#&&'&%'*(, @/ / !&'(0 +/*! Haliangium ochraceum DSM 14365 Patrik D’haeseleer, Adam Zemla, Victor Kunin Wu et al. 2009 Nature 462, 1056-1060 See also Guljamow et al. 2007 Current Biology.
- 70. GEBA Phylogenomic Lesson 3 Phylogeny driven genome selection (and phylogenetics in general) improves genome annotation
- 71. Predicting Function • Key step in genome projects • More accurate predictions help guide experimental and computational analyses • Many diverse approaches • All improved both by “phylogenomic” type analyses that integrate evolutionary reconstructions and understanding of how new functions evolve
- 72. Most/All Functional Prediction Improves w/ Better Phylogenetic Sampling • 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 prediction Kostas Natalia Thanos Nikos Iain Mavrommatis Ivanova Lykidis Kyrpides Anderson
- 73. GEBA Phylogenomic Lesson 4 Metadata and individual genome papers important
- 74. Genome Marker Papers w/ metadata
- 75. GEBA Phylogenomic Lesson 5 Improves analysis of genome data from uncultured organisms
- 76. Metagenomics shotgun clone
- 77. Rusch et al. PLoS Biology 2007
- 79. Example I: Phylotyping with rRNA and other genes
- 80. Uses of rRNA sequences The Hidden Majority Richness estimates Hugenholtz 2002 Bohannan and Hughes 2003
- 81. Weighted % of Clones 0 0.1250 0.2500 0.3750 0.5000 Al ph ap ro te Be ob ta ac pr te ot ria G eo am ba m ct ap er ro ia Ep te si ob lo ac np te ro ria D te el ob ta ac pr te ot ria eo C ba ya ct no er b ia ac te Fi ria rm ic ut Ac e s tin ob ac te C ria hl o ro bi C FB Major Phylogenetic Group Sargasso Phylotypes C hl o ro fle Sp xi iro ch ae Fu te so s D ba ei ct no er c oc ia cu s- Eu Th ry erm ar ch us C ae re ot na a rc ha eo ta Shotgun Sequencing Allows Use of Other Markers EFG Venter et al., Science 304: 66-74. 2004 EFTu rRNA RecA RpoB HSP70
- 82. Weighted % of Clones 0 0.1250 0.2500 0.3750 0.5000 Al ph ap ro te Be ob ta ac pr te ot ria G eo am ba m ct ap er ro ia Ep te si ob lo ac np te ro ria D te el ob ta ac pr te ot ria eo C ba ya ct no er b ia ac te Fi ria rm ic ut Ac e s tin ob ac te C ria hl o ro bi without good C FB Major Phylogenetic Group Sargasso Phylotypes C Cannot be done hl o ro fle Sp xi iro ch ae Fu te so s D ba ei ct no er c ia sampling of genomes oc cu s- Eu Th ry erm ar ch us C ae re ot na a rc ha eo ta Shotgun Sequencing Allows Use of Other Markers EFG Venter et al., Science 304: 66-74. 2004 EFTu rRNA RecA RpoB HSP70
- 85. Binning challenge A T B U C V D W E X F Y G Z
- 86. Binning challenge A T B U C V D W E X F Y G Best binning method: reference genomes Z
- 87. Binning challenge A T B U C V D W E X F Y G Best binning method: reference genomes Z
- 88. Binning challenge A T B U C V D W E X F Y G No reference genome? What do you do? Z
- 89. Weighted % of Clones 0 0.1250 0.2500 0.3750 0.5000 Al ph ap ro te Be ob ta ac pr te ot ria G eo am ba m ct ap er ro ia Ep te si ob lo ac np te ro ria D te el ob ta ac pr te ot ria eo C ba ya ct no er b ia ac te Fi ria rm ic ut Ac e s tin ob ac te C ria hl o ro bi C FB Major Phylogenetic Group Sargasso Phylotypes C hl o ro fle Sp xi iro ch Phylogenetic Binning ae Fu te so s D ba ei ct no er c oc ia cu s- Eu Th ry erm ar ch us C ae re ot na a rc ha eo ta EFG Venter et al., Science 304: 66-74. 2004 EFTu rRNA RecA RpoB HSP70
- 90. Weighted % of Clones 0 0.1250 0.2500 0.3750 0.5000 Al ph ap ro te Be ob ta ac pr te ot ria G eo am ba m ct ap er ro ia Ep te si ob lo ac np te ro ria D te el ob ta ac pr te ot ria eo C ba ya ct no er b ia ac te Fi ria rm ic ut Ac e s tin ob ac te C ria hl o ro bi without good C FB Major Phylogenetic Group Sargasso Phylotypes C Cannot be done hl o ro fle Sp xi iro ch ae Fu te so s D ba ei ct no er c ia sampling of genomes oc cu s- Eu Th ry erm ar ch us C ae re ot na a rc ha eo ta Shotgun Sequencing Allows Use of Other Markers EFG Venter et al., Science 304: 66-74. 2004 EFTu rRNA RecA RpoB HSP70
Notes de l'éditeur
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- The Wolbachia genome revealed an unexpectedly high amount of repetitive DNA and mobile genetic elements (which were never seen before in a small-genomed intracellular species)\n
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- It has been less than 10 years since the first genome was determined\n
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- Functional prediction using a gene tree is just like predicting the biology of a species using a species tree\n
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- Functional prediction using a gene tree is just like predicting the biology of a species using a species tree\n
- Functional prediction using a gene tree is just like predicting the biology of a species using a species tree\n
- Functional prediction using a gene tree is just like predicting the biology of a species using a species tree\n
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- This is a tree of a rRNA gene that was found on a large DNA fragment isolated from the Monterey Bay. This rRNA gene groups in a tree with genes from members of the gamma Proteobacteria a group that includes E. coli as well as many environmental bacteria. This rRNA phylotype has been found to be a dominant species in many ocean ecosystems.\n
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- Phylogenetic analysis of rRNAs led to the discovery of archaea\n
- Extension of rRNA analysis to uncultured organisms using PCR\n
- This is a tree of a rRNA gene that was found on a large DNA fragment isolated from the Monterey Bay. This rRNA gene groups in a tree with genes from members of the gamma Proteobacteria a group that includes E. coli as well as many environmental bacteria. This rRNA phylotype has been found to be a dominant species in many ocean ecosystems.\n\n clone from the Sargasso Sea. This shows that this \n
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- Gets better with more markers - but we do not have lots of sequences for these markers. We can get them from genomes. The more diverse the genomes, thebeter the marker set will be\n
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- Gets better with more markers - but we do not have lots of sequences for these markers. We can get them from genomes. The more diverse the genomes, thebeter the marker set will be\n
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- Gets better with more markers - but we do not have lots of sequences for these markers. We can get them from genomes. The more diverse the genomes, thebeter the marker set will be\n
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- It has been less than 10 years since the first genome was determined\n
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