Phylogenetic approaches to metagenomic analysis #KSMicro talk by Jonathan Eisen
•Télécharger en tant que KEY, PDF•
2 j'aime•2,888 vues
Slides from talk by Jonathan Eisen at the Keystone meeting on "microbial communities as drivers of ecosystem complexity"
Recommandé
Recommandé
Contenu connexe
En vedette
En vedette (20)
Similaire à Phylogenetic approaches to metagenomic analysis #KSMicro talk by Jonathan Eisen
Similaire à Phylogenetic approaches to metagenomic analysis #KSMicro talk by Jonathan Eisen (10)
Plus de Jonathan Eisen
Plus de Jonathan Eisen (20)
Dernier
Dernier (20)
Phylogenetic approaches to metagenomic analysis #KSMicro talk by Jonathan Eisen
- 1. Phylogenetic & Phylogenomic Approaches to Metagenomic Analysis Jonathan A. Eisen UC Davis Keystone Meeting #KSMicro March 26, 2011
- 2. Outline • Introduction to phylogeny • Phylogeny and metagenomics – Phylotyping – Phylogenetic Binning – Functional diversity and prediction – Phylogenetic ecology – Selecting species or genes for study
- 3. T. H. Dobzhansky (1973) “Nothing in biology makes sense except in the light of evolution.”
- 4. Evolutionary Perspective and Comparative Biology • Comparative biology is the analysis of differences and similarities between species. • An evolutionary perspective is useful in such studies because it allows one to focus on how and why similarities and differences came to be. • In other words, biological objects have a history and understanding that history is important
- 5. Phylogeny • Phylogeny is a description of the evolutionary history of relationships among organisms (or their parts). • This is frequently portrayed in a diagram called a phylogenetic tree. • Phylogenies can be more complex than a bifurcating tree (e.g., lateral gene transfer, recombination, hybridization) • History allows one to distinguish homology from convergence; tease apart issues with rate variation
- 6. Uses of Phylogeny in Metagenomics Example 1: Phylotyping
- 7. rRNA survey • Sequence rRNAs • Cluster into OTUs
- 8. rRNA survey OTU1 • Sequence OTU2 rRNAs OTU3 • Cluster into OTU4 OTU5 OTUs OTU6 OTU7 OTU8 OTU9 OTU10
- 9. OTUs on Tree OTU1 OTU5 OTU4 OTU6 OTU2 OTU3 OTU7 OTU9 OTU8 OTU10
- 10. Uses of Tree • Clades • Rates of change • LGT • Convergence • Character history
- 11. rRNA Phylotyping
- 12. rRNA Phylotyping • Note - using a tree does not mean phylogeny always matters per se • But allows one to test whether and how it impacts biology, ecology, etc • When it does = homology • When it does not = convergence, HGT, etc
- 13. rRNA Phylotyping in Sargasso Sea Metagenomic Metagenomic Data Venter et al., Science 304: 66. 2004
- 14. Metagenomic Phylogenetic challenge A single tree with everything
- 15. PhylOTU: A High-Throughput Procedure Quantifies Microbial Community Diversity and Resolves Novel Taxa from Metagenomic Data Thomas J. Sharpton1*, Samantha J. Riesenfeld1, Steven W. Kembel2, Joshua Ladau1, James P. O’Dwyer2,3, Jessica L. Green2, Jonathan A. Eisen4, Katherine S. Pollard1,5 1 The J. David Gladstone Institutes, University of California San Francisco, San Francisco, California, United States of America, 2 Center for Ecology and Evolutionary Biology, University of Oregon, Eugene, Oregon, United States of America, 3 Institute of Integrative and Comparative Biology, University of Leeds, Leeds, United Kingdom, 4 Department of Evolution and Ecology, University of California Davis, Davis, California, United States of America, 5 Institute for Human Genetics & Division of Biostatistics, Finding Metagenomic OTUs University of California San Francisco, San Francisco, California, United States of America Abstract Microbial diversity is typically characterized by clustering ribosomal RNA (SSU-rRNA) sequences into operational taxonomic units (OTUs). Targeted sequencing of environmental SSU-rRNA markers via PCR may fail to detect OTUs due to biases in priming and amplification. Analysis of shotgun sequenced environmental DNA, known as metagenomics, avoids amplification bias but generates fragmentary, non-overlapping sequence reads that cannot be clustered by existing OTU- finding methods. To circumvent these limitations, we developed PhylOTU, a computational workflow that identifies OTUs from metagenomic SSU-rRNA sequence data through the use of phylogenetic principles and probabilistic sequence profiles. Using simulated metagenomic data, we quantified the accuracy with which PhylOTU clusters reads into OTUs. Comparisons of PCR and shotgun sequenced SSU-rRNA markers derived from the global open ocean revealed that while PCR libraries identify more OTUs per sequenced residue, metagenomic libraries recover a greater taxonomic diversity of OTUs. In addition, we discover novel species, genera and families in the metagenomic libraries, including OTUs from phyla missed by analysis of PCR sequences. Taken together, these results suggest that PhylOTU enables characterization of part of the biosphere currently hidden from PCR-based surveys of diversity? Citation: Sharpton TJ, Riesenfeld SJ, Kembel SW, Ladau J, O’Dwyer JP, et al. (2011) PhylOTU: A High-Throughput Procedure Quantifies Microbial Community Diversity and Resolves Novel Taxa from Metagenomic Data. PLoS Comput Biol 7(1): e1001061. doi:10.1371/journal.pcbi.1001061 Editor: Oded Be ` , Technion-Israel Institute of Technology, Israel ´ja Received July 22, 2010; Accepted December 17, 2010; Published January 20, 2011 Copyright: ß 2011 Sharpton et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
- 16. Shotgun Sequencing Allows Use of Alternative Anchors (e.g., RecA) Venter et al., Science 304: 66. 2004
- 17. 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
- 18. AMPHORA Guide tree
- 19. 0 0.1750 0.3500 0.5250 0.7000 Al ph ap r Be ote ta o pr bac G ot t am eo eria m ba ap ct D ro te er ia el ta ob pr Ep ot act si eo er U lo ba ia nc np ct la ro er ss te ia ifi ob ed ac Pr te C ot ria yae o nob a bac C cter hl teriia am a Ac yd ia id ob e Ba act ct er er ia oi Ac de tin te ob s ac Aq ter ui ia Pl fic an ae ct om Sp yc iro ete ch s a Fi ete rm s ic ut C es hl or of le C xi U hl or nc la ob ss i ifi ed Ba ct er ia frr tsf pgk rpsI rplL rplT rplF rplE rplS infC rplP rplA rplK rplB rplN rplD rplC rpsJ rplM rpsE rpsS rpsK rpsB rpsC pyrG rpoB nusA rpsM rpmA dnaG smpB
- 20. rRNA Tree of Life Bacteria Archaea Eukaryotes Figure from Barton, Eisen et al. “Evolution”, CSHL Press. 2007. Based on tree from Pace 1997 Science 276:734-740
- 21. rRNA Tree of Life Bacteria Wu et al. (2011) PLoS ONE 6(3): e18011. doi:10.1371/ journal.pone.0018011 Archaea ?????? Eukaryotes Figure from Barton, Eisen et al. “Evolution”, CSHL Press. 2007. Based on tree from Pace 1997 Science 276:734-740
- 22. rRNA Tree of Life Bacteria Archaea Scanned through GOS data for rRNAs that fit this pattern Eukaryotes Figure from Barton, Eisen et al. “Evolution”, CSHL Press. 2007. Based on tree from Pace 1997 Science 276:734-740
- 23. rRNA Tree of Life Bacteria Archaea Found many, but closer examination revealed all to have issues Eukaryotes Figure from Barton, Eisen et al. “Evolution”, CSHL Press. 2007. Based on tree from Pace 1997 Science 276:734-740
- 24. Mol Evol (1m) 41:110S-1123 PURNALOF OLECULA EVOLUTIO @ S!""'"I',rVcr1.g N"" Yorll", 19')' RecA The RecA Protein as a Model Molecule for Molecular Systematic Studies of Bacteria: Comparison of Trees of RecAs and 16S rRNAs from the Same Species Jonathan A. Eisen Depanmenl of Biological ScierM:es,Stanford Universily. SIaDfORi,CA 9430S-S020. USA (email: jeisen@leI8IM1sranfOld.edu) Received: I July 199.5/ Accepted: 2S July 199.5 Abstract. The evolution of the RecA protein was an- Introduction alyzed using molecularphylogenetictechniques. Phylo- Molecular systematicsbas becomethe primary way to genetic trees of all currently available complete RecA detennineevolutionaryrelationships amongmicroorgan proteins were inferred using multiple maximum parsi- isms because morphologicaland other phenotypicchar mony and distance matrix methods. Comparison and actersareeither absent changetoo rapidly to be usefu or analysisof the treesrevealthat the inferredrelationships for phylogeneticinference(Woese 1987).Not all mole amongtheseproteinsare highly robust The RecA trees culesare equaIly useful for molecularsystematicstudie show consistentsubdivisionscorresponding many of to and the molecule of choice for most such studies o
- 25. Homologs in GOS Data Analysis 1st Done in 2004
- 26. GOS 1 GOS 2 GOS 3 GOS 4 GOS 5
- 29. GOS 1 GOS 2 GOS 3 GOS 4 Thaumarchaeota
- 30. GOS 1 Phage? Phage? GOS 4 Thaumarchaeota
- 31. ???? Phage? Phage? ???? Thaumarchaeota
- 35. Uses of Phylogeny in Metagenomics Example 2: Binning
- 36. Binning challenge No reference genome? What do you do?
- 37. Binning challenge No reference genome? What do you do? Phylogeny
- 38. CFB Phyla
- 39. Sulcia makes amino acids Baumannia makes vitamins and cofactors Wu et al. 2006 PLoS Biology 4: e188.
- 40. AMPHORA Guide tree
- 41. Uses of Phylogeny in Metagenomics Example 3: Functional Diversity and Functional Predictions
- 42. Predicting Function • Identification of motifs – Short regions of sequence similarity that are indicative of general activity – e.g., ATP binding • Homology/similarity based methods – Gene sequence is searched against a databases of other sequences – If significant similar genes are found, their functional information is used • Problem – Genes frequently have similarity to hundreds of motifs and multiple genes, not all with the same function
- 43. 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 1A 1B 2A 2B 3A 3B 1 2 3 4 5 6 ACTUAL EVOLUTION (ASSUMED TO BE UNKNOWN) Based on Eisen, 1998 Genome Duplication Res 8: 163-167.
- 44. Massiuve Diversity of Proteorhodopsins Venter et al., 2004
- 45. Uses of Phylogeny in Metagenomics Example 4: Phylogenetic Ecology
- 46. Uses of Phylogeny in Metagenomics Example 5: Selecting Organisms for Study
- 47. rRNA Tree of Life Bacteria Archaea Eukaryotes Figure from Barton, Eisen et al. “Evolution”, CSHL Press. 2007. Based on tree from Pace 1997 Science 276:734-740
- 50. GEBA Phylogenomic Lesson 1 Phylogeny-driven genome selection helps discover new genetic diversity
- 51. Protein Family Rarefaction Curves • Take data set of multiple complete genomes • Identify all protein families using MCL • Plot # of genomes vs. # of protein families
- 52. Wu et al. 2009 Nature 462, 1056-1060
- 53. Wu et al. 2009 Nature 462, 1056-1060
- 54. Wu et al. 2009 Nature 462, 1056-1060
- 55. Wu et al. 2009 Nature 462, 1056-1060
- 56. Wu et al. 2009 Nature 462, 1056-1060
- 57. Synapomorphies exist Wu et al. 2009 Nature 462, 1056-1060
- 58. Families/PD not uniform +,%-./&#(%)"* !"#$%"&'(%)"* ! !
- 59. GEBA Phylogenomic Lesson 2 Improves analysis of genome data from uncultured organisms
- 60. 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
- 61. 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
- 62. 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
- 63. 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
- 64. 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 e improves Ac s tin ob ac te C ria hl o ro bi C GEBA Project 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 metagenomic analysis 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
- 65. 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 But not a lot 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
- 66. Phylogeny and Metagenomics Future 1 Need to adapt genomic and metagenomic methods to make better use of data
- 67. iSEEM Project
- 68. AMPHORA 2 Coming w/ More Markers Phylogene9c group Genome Gene Maker Number Number Candidates Archaea 62 145415 106 Ac-nobacteria 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 684 See posters by Dongying Wu and Guillaume Jospin
- 69. • Build AMPHORA ALL reference tree with concatenated alignment • Align reads that match any of the HMMs to concatenated alignment • Place reads into reference tree one at a time
- 70. Phylogeny and Metagenomics Future 2 We have still only scratched the surface of microbial diversity
- 71. rRNA Tree of Life Bacteria Archaea Eukaryotes Figure from Barton, Eisen et al. “Evolution”, CSHL Press. 2007. Based on tree from Pace 1997 Science 276:734-740
- 72. Phylogenetic Diversity: Genomes From Wu et al. 2009 Nature 462, 1056-1060
- 73. Phylogenetic Diversity with GEBA From Wu et al. 2009 Nature 462, 1056-1060
- 74. Phylogenetic Diversity: Isolates From Wu et al. 2009 Nature 462, 1056-1060
- 75. Phylogenetic Diversity: All From Wu et al. 2009 Nature 462, 1056-1060
- 76. 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 72
- 77. Earth Microbiome Project • Goal – to systema-cally approach the problem of characterizing microbial life on earth • Strategy: – Explore microbes in environmental parameter space – Design ‘ideal’ strategy to interrogate these biomes – Acquire samples and sequence broad and deep both DNA, mRNA and rRNA – Define microbial community structure and the protein universe • Gilbert et al., 2010a,b SIGS •
- 78. Phylogenomics Future 3 Need Experiments from Across the Tree of Life too
- 79. A Happy Tree of Life
Notes de l'éditeur
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- Phylogenetic analysis of rRNAs led to the discovery of archaea\n
- Phylogenetic analysis of rRNAs led to the discovery of archaea\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
- \n
- \n
- \n
- \n
- \n
- 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
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- \n
- Sites of imaginative potential – iconic locations – Iconic Sampling – \n\nLife is strange – microbes are stranger – how do we capitalize on this?\n
- \n
- \n