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"Phylogeny-driven studies in genomics and metagenomics" talk by Jonathan Eisen at #CSMUBC2012
- 1. Phylogeny-Driven Approaches to Genomics and Metagenomics June 23, 2012 Canadian Society for Microbiology Jonathan A. Eisen University of California, Davis @phylogenomics
- 2. 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 Klenk
- 3. Phylogeny: What is it?
- 4. Phylogeny: What is it? • 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)
- 5. Whatever the History: Trying to Incorporate it is Critical from Lake et al. doi: 10.1098/rstb.2009.0035
- 6. Phylogeny • Applies to • Species • Genes • Genomes
- 7. Phylogeny: What is it good for?
- 8. Phylogeny: What is it good for? Uses of Phylogeny in Genomics and Metagenomics
- 9. Uses of Phylogeny in Genomics and Metagenomics Example 1: Phylotyping
- 10. rRNA Phylotyping DNA extraction PCR Makes lots of Sequence PCR copies of the rRNA genes rRNA genes in sample rRNA1 5’...ACACACATAGGTGGAGCTA GCGATCGATCGA... 3’ Phylogenetic tree Sequence alignment = Data matrix rRNA2 rRNA1 rRNA2 rRNA1 A C A C A C 5’..TACAGTATAGGTGGAGCTAG CGACGATCGA... 3’ rRNA4 rRNA3 rRNA2 T A C A G T rRNA3 rRNA3 C A C T G T 5’...ACGGCAAAATAGGTGGATT E. coli Humans rRNA4 C A C A G T CTAGCGATATAGA... 3’ Yeast 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 T
- 11. rRNA Phylotyping • Collect DNA from environment • PCR amplify rRNA genes using broad (so-called universal) primers • Sequence • Align to others • Infer evolutionary tree • Unknowns “identified” by placement on tree
- 12. Era IV: Genomes in Environment shotgun sequence Metagenomics
- 13. rRNA Phylotyping in Sargasso Venter et al., Science 304: 66. 2004
- 14. RecA Phylotyping in Sargasso Data Venter et al., Science 304: 66. 2004
- 15. Weighted % of Clones 0 0.125 0.250 0.375 0.500 Al ph ap ro t eo Be ba ta ct er pr ia 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 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 oc cu s- rRNA 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
- 17. Metagenomics
- 19. Binning challenge Best binning method: reference genomes
- 20. Binning challenge Best binning method: reference genomes
- 21. Binning challenge No reference genome? What do you do?
- 22. Binning challenge No reference genome? What do you do? Composition, Assembly, others
- 23. Binning challenge No reference genome? What do you do? Phylogeny
- 24. Sulcia makes amino acids Baumannia makes vitamins and cofactors Wu et al. 2006 PLoS Biology 4: e188.
- 25. CFB Phyla
- 26. Side benefit II: PG Ecology
- 27. rRNA survey • Sequence rRNAs • Cluster
- 28. rRNA survey OTU1 • Sequence OTU2 rRNAs OTU3 • Cluster OTU4 • Identify OTU5 OTU6 “OTUs” OTU7 OTU8 OTU9 OTU10
- 29. OTUs on Tree OTU1 OTU5 OTU4 OTU6 OTU2 OTU3 OTU7 OTU9 OTU8 OTU10
- 30. OTUs on Tree OTU1 • Clades OTU5 • Rates of OTU4 change OTU6 • LGT OTU2 OTU3 • Convergence OTU7 • Character OTU9 OTU8 history OTU10
- 31. Unifrac nuscript typically used as a qualitative measure because duplicate se- Weighted UniFrac. Weighted UniFrac is a new variant of the original un- quences are usually removed from the tree. However, the P weighted UniFrac measure that weights the branches of a phylogenetic tree test may be used in a semiquantitative manner if all clones, based on the abundance of information (Fig. 1B). Weighted UniFrac is thus a quantitative measure of  diversity that can detect changes in how many se- even those with identical or near-identical sequences, are in- quences from each lineage are present, as well as detect changes in which taxa cluded in the tree (13). are present. This ability is important because the relative abundance of different Here we describe a quantitative version of UniFrac that we kinds of bacteria can be critical for describing community changes. In contrast, call “weighted UniFrac.” We show that weighted UniFrac be- the original, unweighted UniFrac (Fig. 1A) is a qualitative  diversity measure haves similarly to the FST test in situations where both are because duplicate sequences contribute no additional branch length to the tree (by definition, the branch length that separates a pair of duplicate sequences is zero, because no substitutions separate them). The first step in applying weighted UniFrac is to calculate the raw weighted UniFrac value (u), according to the first equation: NIH-PA Author Manuscript n uϭ bi ϫ ͯA Ϫ B ͯ Ai T B T i i Here, n is the total number of branches in the tree, bi is the length of branch i, Ai and Bi are the numbers of sequences that descend from branch i in commu- nities A and B, respectively, and AT and BT are the total numbers of sequences in communities A and B, respectively. In order to control for unequal sampling effort, Ai and Bi are divided by AT and BT. If the phylogenetic tree is not ultrametric (i.e., if different sequences in the sample have evolved at different rates), clustering with weighted UniFrac will place more emphasis on communities that contain quickly evolving taxa. Since these taxa are assigned more branch length, a comparison of the communities FIG. 1. Calculation of the unweighted and the weighted UniFrac that contain them will tend to produce higher values of u. In some situations, it measures. Squares and circles represent sequences from two different may be desirable to normalize u so that it has a value of 0 for identical commu- environments. (a) In unweighted UniFrac, the distance between the nities and 1 for nonoverlapping communities. This is accomplished by dividing u circle and square communities is calculated as the fraction of the by a scaling factor (D), which is the average distance of each sequence from the branch length that has descendants from either the square or the circle root, as shown in the equation as follows: environment (black) but not both (gray). (b) In weighted UniFrac, ͩ branch lengths are weighted by the relative abundance of sequences in ͪ n the square and circle communities; square sequences are weighted Aj Bj Dϭ dj ϫ ϩ twice as much as circle sequences because there are twice as many total AT BT circle sequences in the data set. The width of branches is proportional Figure 1. j NIH-PA Author Manuscript to the degree to which each branch is weighted in the calculations, and Here, dj is the distance of sequence j from the root, (PD) and PD Gain (G) for the grey community. The Estimates of Phylogenetic Diversity Aj and Bj are the numbers gray branches have no weight. Branches 1 and 2 have heavy weights of times the sequences were observed in communitieswhite, and grey communities. (A) PD is the sum of the boxes represent taxa from the black, A and B, respectively, and since the descendants are biased toward the square and circles, respec- AT and BT are the total numbers of sequences from communities A and B, tively. Branch 3 contributes no value since it has an equal contribution branches leading to the grey taxa. (B) G is the sum of the branches leading only to the grey respectively. from circle and square sequences after normalization. Clustering with normalized u values treatsshowing the increase inof taxa. (C) PD rarefaction curves each sample equally instead branch length with sampling effort for the intestinal and stool bacteria from three healthy individuals. Aligned16S rRNA sequences from the three individuals were available with the Supplementary Materials in (Eckburg, et al., 2005). The Arb parsimony insertion tool was used to add the sequences to a tree containing over 9,000 sequences (Hugenholtz, 2002) that is available for download at the rRNA Database Project II website (Maidak, et al., 2001). The curves represent the average values for 50 replicate trials. FEMS Microbiol Rev. Author manuscript; available in PMC 2009 July 1.
- 32. Caveat: Not Everything in Groups
- 33. RecA, RpoB in GOS GOS 1 GOS 2 GOS 3 GOS 4 GOS 5 Wu et al PLoS One 2011
- 34. Uses of Phylogeny in Genomics and Metagenomics Example 2: Functional Diversity and Functional Predictions
- 35. 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
- 36. 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
- 37. From Eisen et al. 1997 Nature Medicine 3: 1076-1078.
- 38. Blast Search of H. pylori “MutS” • Blast search pulls up Syn. sp MutS#2 with much higher p value than other MutS homologs • Based on this TIGR predicted this species had mismatch repair • Assumes functional constancy Based on Eisen et al. 1997 Nature Medicine 3: 1076-1078.
- 39. MutL?? Based on Eisen et al. 1997 Nature Medicine 3: 1076-1078.
- 40. Overlaying Functions onto Tree MutS2 Aquae MSH5 StrpyBacsuSynsp Deira Helpy Yeast Human Borbu Celeg Metth MSH6 mSaco Yeast Human Mouse Arath Yeast MSH4 Celeg Human Arath Human MSH3 Mouse Fly Spombe Yeast Xenla Rat Mouse Yeast Human MSH1 Spombe Yeast MSH2 Neucr Arath Aquae Trepa Chltr Deira Theaq Bacsu Borbu Thema Synsp Strpy Ecoli Based on Eisen, Neigo 1998 Nucl Acids Res MutS1 26: 4291-4300.
- 42. 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.
- 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 1 2 3 4 5 6 2A 2B 3A 3B ACTUAL EVOLUTION (ASSUMED TO BE UNKNOWN) Based on Duplication Eisen, 1998 Genome Res 8:
- 44. Diversity of Proteorhodopsins Venter et al., 2004
- 45. Carboxydothermus sporulates Wu et al. 2005 PLoS Genetics 1: e65.
- 46. Wu et al. 2005 PLoS Genetics 1: e65.
- 48. Uses of Phylogeny in Genomics and Metagenomics Example 3: Selecting Organisms for Study
- 49. As of 2002 Proteobacteria TM6 OS-K • At least 40 Acidobacteria Termite Group phyla of bacteria OP8 Nitrospira Bacteroides 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 Hugenholtz, OP11 2002
- 50. As of 2002 Proteobacteria TM6 OS-K • At least 40 Acidobacteria Termite Group OP8 phyla of Nitrospira Bacteroides bacteria Chlorobi Fibrobacteres Marine GroupA • Most genomes WS3 Gemmimonas from three Firmicutes Fusobacteria phyla 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 Hugenholtz, OP11 2002
- 51. As of 2002 Proteobacteria TM6 OS-K • At least 40 Acidobacteria Termite Group OP8 phyla of Nitrospira Bacteroides bacteria Chlorobi Fibrobacteres Marine GroupA • Most genomes WS3 Gemmimonas from three Firmicutes Fusobacteria phyla Actinobacteria OP9 Cyanobacteria Synergistes • Some studies Deferribacteres Chrysiogenetes in other phyla NKB19 Verrucomicrobia Chlamydia OP3 Planctomycetes Spriochaetes Coprothmermobacter OP10 Thermomicrobia Chloroflexi TM7 Deinococcus-Thermus Dictyoglomus Aquificae Thermudesulfobacteria Thermotogae OP1 Based on Hugenholtz, OP11 2002
- 52. As of 2002 Proteobacteria TM6 OS-K • At least 40 Acidobacteria Termite Group OP8 phyla of Nitrospira Bacteroides bacteria Chlorobi Fibrobacteres Marine GroupA • Most genomes WS3 Gemmimonas from three Firmicutes Fusobacteria phyla Actinobacteria OP9 Cyanobacteria Synergistes • Some other Deferribacteres Chrysiogenetes phyla are only NKB19 Verrucomicrobia Chlamydia sparsely OP3 Planctomycetes Spriochaetes sampled Coprothmermobacter OP10 • Same trend in Thermomicrobia Chloroflexi TM7 Eukaryotes Deinococcus-Thermus Dictyoglomus Aquificae Thermudesulfobacteria Thermotogae OP1 Based on Hugenholtz, OP11 2002
- 53. As of 2002 Proteobacteria TM6 OS-K • At least 40 Acidobacteria Termite Group OP8 phyla of Nitrospira Bacteroides bacteria Chlorobi Fibrobacteres Marine GroupA • Most genomes WS3 Gemmimonas from three Firmicutes Fusobacteria phyla Actinobacteria OP9 Cyanobacteria Synergistes • Some other Deferribacteres Chrysiogenetes phyla are only NKB19 Verrucomicrobia Chlamydia sparsely OP3 Planctomycetes Spriochaetes sampled Coprothmermobacter OP10 • Same trend in Thermomicrobia Chloroflexi TM7 Viruses Deinococcus-Thermus Dictyoglomus Aquificae Thermudesulfobacteria Thermotogae OP1 Based on Hugenholtz, OP11 2002
- 56. 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)
- 57. GEBA Now • 300+ genomes • Rich sampling of major groups of cultured organisms
- 58. GEBA Lesson 1: The rRNA Tree of Life is a Useful Tool From Wu et al. 2009 Nature 462, 1056-1060
- 59. GEBA Lesson 2: The rRNA Tree of Life is not perfect ... 16s WGT, 23S Badger et al. 2005 Int J System Evol Microbiol 55: 1021-1026.
- 60. GEBA Lesson 3: Phylogeny 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 prediction
- 61. GEBA Lesson 4 : Metadata Important
- 62. GEBA Lesson 5: Improves discovering new genetic diversity
- 63. Phylogenetic Distribution Novelty: Bacterial Actin Related Protein C. boidinii gi57157304 S. cerevisiae gi14318479 L. starkeyi gi166080363 S. japonicus gi213407080 ACTIN A. cliftonii gi14269497 99 U. pertusa gi50355609 H. sapiens gi4501889 M. cerebralis gi46326807 67 C. cinerea gi169844021 N. crassa gi85101929 ARP1 100 I. scapularis gi215507378 51 100 H. sapiens gi5031569 65 S. japonicus gi213404844 100 S. cerevisiae gi6320175 ARP2 D. melanogaster gi24642545 100 G. gallus gi45382569 75 C. neoformans gi58266690 S. cerevisiae gi6322525 ARP3 100 D. melanogaster gi17737543 100 H. sapiens gi5031573 H. ochraceum gi227395998 BARP S. cerevisiae gi1008244 73 P. patens gi168051992 ARP4 99 A. thaliana gi18394608 94 S. cerevisiae gi1301932 100 S. japonicus gi213408393 ARP5 87 D. discoideum gi66802418 74 D. melanogaster gi17737347 97 S. cerevisiae gi6323114 100 D. hansenii gi21851 1921 ARP6 100 O. sativa gi182657420 A. thaliana gi1841 1737 ARP7 D. melanogater gi19920358 100 M. musculus gi226246593 ARP10 0.5 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.
- 64. Protein Family Rarefaction • Take data set of multiple complete genomes • Identify all protein families using MCL • Plot # of genomes vs. # of protein families
- 65. Wu et al. 2009 Nature 462, 1056-1060
- 66. Wu et al. 2009 Nature 462, 1056-1060
- 67. Wu et al. 2009 Nature 462, 1056-1060
- 68. Wu et al. 2009 Nature 462, 1056-1060
- 69. Wu et al. 2009 Nature 462, 1056-1060
- 70. Synapomorphies exist Wu et al. 2009 Nature 462, 1056-1060
- 71. GEBA Lesson 6: Improves Analysis of Uncultured
- 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 ia 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 Ac tin es analysis improves ob ac te ria C hl or ob i C Major Phylogenetic Group FB Sargasso Phylotypes metagenomic GEBA Project 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 EFG EFTu rRNA RecA RpoB HSP70 Venter et al., Science 304: 66-74. 200
- 73. Weighted % of Clones 0 0.125 0.250 0.375 0.500 Al ph ap ro t eo Be ba ta ct er pr ia 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 EFG EFTu rRNA RecA RpoB HSP70 Venter et al., Science 304: 66-74. 200
- 74. • AND THEN ALL OF THEM WERE DECEIVED
- 75. • For each of these areas - need to do a MUCH better job ...
- 77. Major Issues in Phylotpying Beyond Moore’s Law Metagenomics Short reads
- 78. Major Issues in Phylotpying Beyond Moore’s Law Metagenomics Short reads WE NEED NEW METHODS
- 79. Method 1: Each is an island • Each new sequences is an island • Take reference data • Build alignment, models, trees • Add new sequence to reference alignment and build tree
- 80. 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 One
- 81. AMPHORA Wu and Eisen Genome Biology 2008 9:R151 doi:10.1186/ gb-2008-9-10-r151 Guide tree
- 82. Phylotyping w/ Proteins Wu and Eisen Genome Biology 2008 9:R151 doi:10.1186/gb-2008-9-10-r151
- 83. Whole Genome Tree Wu and Eisen Genome Biology 2008 9:R151 doi: 10.1186/ gb-2008-9-10-r151
- 84. Method 2: Most in the Family
- 85. Phylogenetic Challenge xxxxxxxxxxxxxxxxxxxxxxx xxxxxx xxxxxxxxxxxxx xxxxxxxxxxxxxx xxxxxxxxxxxxxx A single tree with everything?
- 86. Phylogenetic Challenge xxxxxxxxxxxxxxxxxxxxxxx xxxxxx xxxxxxxxxxxxx xxxxxxxxxxxxxx xxxxxxxxxxxxxx A single tree with everything (as long as there is a lot of overlap)
- 87. Phylogenetic Challenge xxxxxxxxxxxxxxxxxxxxxxx xxxxxx xxxxxxxxxxxxx xxxxxxxxxxxxxx xxxxxxxxxxxxxx A single tree with everything (as long as there is a lot of overlap)
- 88. Phylogenetic Challenge A single tree with everything?
- 89. rRNA in Sargasso Metagenome Venter et al., Science 304: 66. 2004
- 90. STAP All ss-rRNA Taxonomy Pip Combine all into one alignment Figure 1. A flow chart of the STAP pipeline.
- 91. RecA in Sargasso Venter et al., Science 304: 66. 2004
- 92. Weighted % of Clones 0 0.125 0.250 0.375 0.500 Al ph ap ro t eo Be ba ta ct er pr ia 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 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 RpoB Fu s so ba De ct in er ia oc oc cu s- rRNA Th Eu er ry m ar u ch s ae C ot a re na Protein vs. rRNA Sargasso Data rc ha eo ta Venter et al., Science 304: 66-74. 200
- 94. Method 3: All in the family • Combine new sequences into one tree • Take reference data • Build alignment, models, trees • Add all sequences to reference alignment and build tree
- 95. Phylogenetic Challenge A single tree with everything?
- 96. Phylogenetic Challenge A single tree with everything?
- 97. PhylOTU Finding Metagenomic OT Figure 1. PhylOTU Workflow. Computational processes are represented as squares and databases are represented as cylinders in this general workflow of PhylOTU. See Results section for details. Bio 2011 PhylOTU - Sharpton et al. PLoS Comp. doi:10.1371/journal.pcbi.1001061.g001
- 100. Method 4: All in the genome • Combine new sequences from different gene families into one tree • Take reference data • Build alignment, models • Concatenate • Add all sequences to reference alignment and build tree
- 101. Challenge • Each gene poorly sampled in metagenomes • Can we combine all into a single tree?
- 102. Kembel Combiner Kembel et al. The phylogenetic diversity of metagenomes. PLoS One 2011
- 103. Kembel Combiner VOL. 73, 2007 PHYL TABLE 1. Measure Only presence/absence of taxa considered Qua Additionally accounts for the no. of times that Qua each taxon was observed cally defined by a sequence similarity threshold) in the sam as equally related. Newer  diversity measures that incorpo phylogenetic information are more powerful because they count for the degree of divergence between sequences (13 29, 30). Phylogenetic  diversity measures can also be ei quantitative or qualitative depending on whether abundanc taken into account. The original, unweighted UniFrac mea (13) is a qualitative measure. Unweighted UniFrac meas the distance between two communities by calculating the f tion of the branch length in a phylogenetic tree that lead descendants in either, but not both, of the two commun (Fig. 1A). The fixation index (FST), which measures distance between two communities by comparing the gen diversity within each community to the total genetic diversit the communities combined (18), is a quantitative measure accounts for different levels of divergence between sequen The phylogenetic test (P test), which measures the significa of the association between environment and phylogeny (18 typically used as a qualitative measure because duplicate quences are usually removed from the tree. However, th test may be used in a semiquantitative manner if all clo even those with identical or near-identical sequences, are cluded in the tree (13). Here we describe a quantitative version of UniFrac tha call “weighted UniFrac.” We show that weighted UniFrac haves similarly to the FST test in situations where both FIG. 1. Calculation of the unweighted and the weighted Uni measures. Squares and circles represent sequences from two diffe environments. (a) In unweighted UniFrac, the distance between
- 104. Improving Phylotyping II • We need to analyze more gene families
- 105. Families/PD not uniform 31 6
- 106. 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 Gammaproteobacter 126 483632 118 ia Deltaproteobacteria 25 102115 206 Epislonproteobacter 18 33416 455 ia 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
- 108. Improving Functional Predictions • We need to analyze even more gene families
- 109. Sifting Families Representative Genomes Extract New Protein Genomes Annotation Extract All v. All Protein BLAST Annotation Homology Screen for Clustering Homologs (MCL) SFams HMMs Align Build Sharpton et al. submitted Figure 1 HMMs
- 110. B A C Sharpton et al. submitted