TIGRTIGRTIGRTIGR
“Nothing in biology makes sense
except in the light of evolution.”
T. H. Dobzhansky (1973)
TIGRTIGR
Talk Outline
• Complete Genome Projects - history and current
status
• What have we learned about evolutionary hi...
TIGRTIGR
The Institute for Genomic
Research
• A not for profit institution, staff ~230
• Departments:
– Eukaryotic Genomic...
TIGRTIGR
General Steps in Analysis of
Complete Genomes
• Identification/prediction of genes
• Characterization of gene fea...
TIGRTIGR
Complete Genome/Chromosome Progress
0
10
20
30
40
50
Complete Genomes
1995 1996 1997 1998 1999 2000
Year
Eukaryot...
TIGRTIGR
Limitations of Genome Analysis
• Functional predictions are PREDICTIONS
• Need to follow up all predictions with
...
TIGRTIGR
Evolutionary Genomics I:
Selection of Species
• Phylogenetic diversity
• Relatedness to model organism
• Understa...
TIGRTIGR
rRNA Tree - Complete/In Progress
EuryarchaeotaCrenarchaeotaAlpha
Proteobacteria
Epsilon
Proteobacteria
Delta
Prot...
TIGRTIGR
Bacteria Archaea
Evolutionary Diversity Still Poorly
Represented in Complete Genomes
TIGRTIGR
Close Relatives vs Year
0510152025303540199519961997199819992000Solo generaMultiple species
TIGRTIGR
TIGRTIGR
Genome sequences and evolution
• Origin of new gene function
• Gene loss
• Genome degradation
• Gene and genome d...
TIGRTIGR
Evolutionary Genome Analysis I:
Functional Prediction
TIGRTIGR
Evolutionary Genome Analysis II:
Gene Loss
TIGRTIGR
EuksArchBacteriaLossEvolutionary Origin of GeneMTMJSCHSAADRTABSMGMPBBTPHPHIECSSMTPresence ( ) or Absence of GeneS...
TIGRTIGR
Why Identify Gene Loss
• Indicates that gene is not absolutely required for
survival
• Parallel loss of same gene...
TIGRTIGR
Duplication and Loss of Mismatch
Repair Genes
51234*
E. coliH. influenzaeN. gonorrhoaeaH. pyloriSyn. spB. subtili...
TIGRTIGR
Evolution and Complete Genomes II:
Gene and Genome Duplication
TIGRTIGR
Why Duplications Are Useful to Identify
• Allows division into orthologs and paralogs
• Improves functional predi...
TIGRTIGR
Expansion of MCP Family in V. choleraeE.coli gi1787690B.subtilis gi2633766Synechocystis sp. gi1001299Synechocysti...
TIGRTIGR
C. pneumoniae Paralogs by Position
0
250000
500000
750000
1000000
1250000
Subject Orf Position
0 250000 500000 75...
TIGRTIGR
C. pneumoniae Paralogs -
Lineage Specific
0
250000
500000
750000
1000000
1250000
Subject Orf Position
0 250000 50...
TIGRTIGR
Evolution and Complete Genomes III:
Genome Rearrangements
TIGRTIGR
X-files
Eisen et al. 2000. Genome Biology 1(6): 11.1-11.9
Also see Tillier and Collins. 2000. Nature Genetics
26(...
TIGRTIGR
V. cholerae vs. E. coli
Best Matching Proteins by Location
0
1000000
2000000
3000000
4000000
5000000
E. coli
ORF ...
TIGRTIGR
M. leprae vs. M. tuberculosis Whole
Genome Alignment
0
1000000
2000000
3000000
4000000
Mycobacterium tuberculosis...
TIGRTIGR
Duplication and Gene Loss Model
A
B
CD
E
F
A
B
CD
E
F
A
B
C
D
E
F
A
B
C
D
E
F
A’
B’
C’
D’
E’
F’
A
B
C
D
E
F
A’
B’...
TIGRTIGR C. trachomatis MoPn
C.pneumoniaeAR39
Origin
Terminus
C. trachomatis vs C. pneumoniae Dot Plot
TIGRTIGR
B1A1B2A2B3A3B3B22423222120191817161514131211109672582627282930123453132
B1313267891011121314151617181920212223242...
TIGRTIGR
Why are Inversions Symmetrical
Around Origin
• Genetic studies in Salmonella and E. coli
suggest that there may b...
TIGRTIGR
Evolution and Complete Genomes IV:
Gene Transfer
TIGRTIGR
Why Gene Transfers Are Useful to Identify
• Laterally transferred genes frequently involved in
environmental adap...
TIGRTIGR
Tree of Life or Web of Life?
TIGRTIGR
Most ‘Evidence’ for Gene Transfer
has Alternative Explanations
TIGRTIGR
How to Infer Gene Transfers
• Unusual distribution patterns
• Unusual nucleotide composition
• High sequence simi...
TIGRTIGR
100s of DNA Islands in O157:H7 vs.
K12: Gene Loss or Transfer?
TIGRTIGR
Lateral Transfer Inference Based
on Complete Genome Analysis I:
Organellar to Nuclear Transfers
in A. thaliana
TIGRTIGR
A. thaliana Nuclear Proteins:
Best Matches to Complete Genomes
0
1000
2000
3000
4000
BestMatches
CHLTE
PORGI
BACS...
TIGRTIGR
SYNSP0100200300400500600700800900
Number of Best Matches to This Species050010001500200025003000350040004500
Numb...
TIGRTIGR
Organellar HSP60s
DROMECG12101DROMECG7235DROMECG2830DROMECG16954ARATH At2g33210ARATH F14O13.19ARATH MCP4.7YEAST S...
TIGRTIGR
Lateral Transfer Inference Based
on Complete Genome Analysis II:
Bacterial to Vertebrate Transfers
Based on Analy...
TIGRTIGR
Lander et al. ‘Evidence’
• Genes match bacteria not non-vertebrate
eukaryotes
• Or, genes have stronger match to ...
TIGRTIGR
Alternative explanations
• Gene loss from non-vertebrate eukaryotes
• Rapid divergence in non-vertebrate
eukaryot...
TIGRTIGR
Evolutionary Rate Variation
231456
TIGRTIGR
Trees Don’t Support Transfer
Paramecium bursaria Chlorella virus 1Homo sapiens HAS1Mus musculus HAS1Xenopus laevi...
TIGRTIGR
Number of pBVTs is Dependent
on # of Genomes Analyzed
TIGRTIGR
Birney et al, same issue of Nature
as complete genome
“The unfinished human genomic DNA may contain
contamination...
TIGRTIGR
Evolution and Complete Genomes V:
Species Evolution
TIGRTIGR
Whole Genome “Phylogeny”
TIGRTIGR
Whole Genome vs. rRNA
hanobacterium thermoautotrophicumhaeoglobus fulgidusococcus horikoshiihanococcus jannaschii...
TIGRTIGR
Deinococcus radiodurans
2a) RecA2b) SS-rRNAErwinia carotovaraEscherichia coliShigella flexneriEnterobacter agglom...
TIGRTIGR
Coming Attractions I:
Phylogenetic Profiles
TIGRTIGR
Phylogenetic Profile - E.coli
Flagellar GenesfhiAfliMfliPfliGflgGfliFflgIflhAflhBgcpE
TIGRTIGR
PG Profile. C. tepidum
Chlorophyll Synthesis
CbiGCbiPDsrNCbiACbiJHCobNBchH1BchH2CobN2BchH3ChlIChlI2ChlI3
TIGRTIGR
Coming Attractions II:
Uncultured Environmental Species
TIGRTIGR
TIGRTIGR
Genomics does not require initial
culturing step.
• Isolate, by filtration, all bacteria in a water sample
• Extr...
TIGRTIGR
Bacterial Rhodopsin:
a new photosynthesis system in the oceans
SAR86, an
uncultured
bacteria
BAC
Sequenced and
An...
TIGRTIGR
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
0.05
0 m
80 m
750 m
γ
α
β
ε
Proteobacteria
Archaea
Best Match...
TIGRTIGR
RecA-Bacteroides/Cytophaga in
Monterey Bay BACs
Chlorobium
tepidum
Cytophaga hutchinsonii
Prevotella
ruminocola
B...
TIGRTIGR
TIGRTIGR
TIGRTIGR
Wither Genomics? Not yet.
• Despite limitations, a great deal can still be
learned from genome sequence analysis.
TIGRTIGR
Evolutionary Diversity Still Poorly
Represented in Complete Genomes
Tmf-pendenR-rubrum3Azs-brasi2Rm-vannielRhb-le...
TIGRTIGR
Limited Ecological and Physiological
Diversity
• All genomes from cultured species or
pathogens/symbionts
• Limit...
TIGRTIGR
TIGRTIGR
Why Completeness is Important
• Improves characterization of genome features
– Gene order, replication origins
• ...
TIGRTIGR
Acknowledgements
• Genome inversions: S. Salzberg, J. Heidelberg, O. White, A.
Stoltzfus, J. Peterson, H. Ochman
...
TIGRTIGR
Evolutionary Studies Improve
Most Aspects of Genome Analysis
• Phylogeny of species places comparative data in pe...
TIGRTIGR
Genome Information and Analysis
Improves Studies of Evolution
• Complete genome information particularly useful
•...
TIGRTIGR
TIGRTIGR
TIGRTIGR
Tracing Gene Loss
• Need presence and absence information of orthologous
genes from different species
• Determini...
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Talk by Jonathan Eisen on "Phylogenomics" at Gordon Conference in 2001

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  • Talk by Jonathan Eisen on "Phylogenomics" at Gordon Conference in 2001

    1. 1. TIGRTIGRTIGRTIGR “Nothing in biology makes sense except in the light of evolution.” T. H. Dobzhansky (1973)
    2. 2. TIGRTIGR Talk Outline • Complete Genome Projects - history and current status • What have we learned about evolutionary history and processes from recent genome projects • Two main themes - completeness and closeness • Coming attractions • Why we need more genomes
    3. 3. TIGRTIGR The Institute for Genomic Research • A not for profit institution, staff ~230 • Departments: – Eukaryotic Genomics – Microbial Genomics – Functional Genomics – Bioinformatics – Sequencing Core
    4. 4. TIGRTIGR General Steps in Analysis of Complete Genomes • Identification/prediction of genes • Characterization of gene features • Characterization of genome features • Prediction of gene function • Prediction of pathways • Integration with known biological data • Comparative genomics
    5. 5. TIGRTIGR Complete Genome/Chromosome Progress 0 10 20 30 40 50 Complete Genomes 1995 1996 1997 1998 1999 2000 Year Eukaryote Archaea Bacteria
    6. 6. TIGRTIGR Limitations of Genome Analysis • Functional predictions are PREDICTIONS • Need to follow up all predictions with experimental work • Each genome sequence is a snapshots of one clone • Genome analysis is not able to identify novel processes • Annotation needs to be updated • Assembly can be wrong • Some parts of genome may be missed (e.g., low copy plasmids)
    7. 7. TIGRTIGR Evolutionary Genomics I: Selection of Species • Phylogenetic diversity • Relatedness to model organism • Understanding major evolutionary transitions • Determining right depth • Short branch lengths
    8. 8. TIGRTIGR rRNA Tree - Complete/In Progress EuryarchaeotaCrenarchaeotaAlpha Proteobacteria Epsilon Proteobacteria Delta Proteobacteria SpirochetesGreen Sulfur bacteria ChlamydiaCyanobacteriaThermotogalesThermophilic O2 reducers Deinococcus/ Thermus Beta Proteobacteria Gamma Proteobacteria Low GC Gram-positive bacteriaHigh GCGram-positive bacteriaGreen Non- Sulfur bacteria
    9. 9. TIGRTIGR Bacteria Archaea Evolutionary Diversity Still Poorly Represented in Complete Genomes
    10. 10. TIGRTIGR Close Relatives vs Year 0510152025303540199519961997199819992000Solo generaMultiple species
    11. 11. TIGRTIGR
    12. 12. TIGRTIGR Genome sequences and evolution • Origin of new gene function • Gene loss • Genome degradation • Gene and genome duplication • Rates and patterns of mutation, recombination • Gene transfer • Species evolution
    13. 13. TIGRTIGR Evolutionary Genome Analysis I: Functional Prediction
    14. 14. TIGRTIGR Evolutionary Genome Analysis II: Gene Loss
    15. 15. TIGRTIGR EuksArchBacteriaLossEvolutionary Origin of GeneMTMJSCHSAADRTABSMGMPBBTPHPHIECSSMTPresence ( ) or Absence of GeneSpecies AbbreviationKingdom Example of Tracing Gene Loss TIGRTIGR
    16. 16. TIGRTIGR Why Identify Gene Loss • Indicates that gene is not absolutely required for survival • Parallel loss of same gene in different species may indicate selective advantage of loss of that gene • Correlated loss of genes in a pathway indicates a conserved association among those genes (important for phylogenetic profiles) • Loss in organellar genomes frequently accompanied by gain in nuclear genome
    17. 17. TIGRTIGR Duplication and Loss of Mismatch Repair Genes 51234* E. coliH. influenzaeN. gonorrhoaeaH. pyloriSyn. spB. subtilisS. pyogenesM. pneumoniaeM. genitaliumA. aeolicusD. radioduransT.pallidumB.burgdorferiSyn. spB. subtilisS. pyogenesA. aeolicusD. radioduransB. burgdorferiMutS1MutS-IlineageMutS-II lineageSpecies TreeGene loss*Gene Duplications1-5Gene LossA.B.A. aeolicusS pyogenesB. subtilisSyn. spD. radioduransMutS2B.burgdorferi
    18. 18. TIGRTIGR Evolution and Complete Genomes II: Gene and Genome Duplication
    19. 19. TIGRTIGR Why Duplications Are Useful to Identify • Allows division into orthologs and paralogs • Improves functional predictions • Helps identify mechanisms of duplication • Can be used to study mutation processes in different parts of a genome • Lineage specific duplications may be indicative of species’ specific adaptations
    20. 20. TIGRTIGR Expansion of MCP Family in V. choleraeE.coli gi1787690B.subtilis gi2633766Synechocystis sp. gi1001299Synechocystis sp. gi1001300Synechocystis sp. gi1652276Synechocystis sp. gi1652103H.pylori gi2313716H.pylori99 gi4155097C.jejuni Cj1190cC.jejuni Cj1110cA.fulgidus gi2649560A.fulgidus gi2649548B.subtilis gi2634254B.subtilis gi2632630B.subtilis gi2635607B.subtilis gi2635608B.subtilis gi2635609B.subtilis gi2635610B.subtilis gi2635882E.coli gi1788195E.coli gi2367378E.coli gi1788194E.coli gi1789453C.jejuni Cj0144C.jejuni Cj0262cH.pylori gi2313186H.pylori99 gi4154603C.jejuni Cj1564C.jejuni Cj1506cH.pylori gi2313163H.pylori99 gi4154575H.pylori gi2313179H.pylori99 gi4154599C.jejuni Cj0019cC.jejuni Cj0951cC.jejuni Cj0246cB.subtilis gi2633374T.maritima TM0014T.pallidum gi3322777T.pallidum gi3322939T.pallidum gi3322938B.burgdorferi gi2688522T.pallidum gi3322296B.burgdorferi gi2688521T.maritima TM0429T.maritima TM0918T.maritima TM0023T.maritima TM1428T.maritima TM1143T.maritima TM1146P.abyssi PAB1308P.horikoshii gi3256846P.abyssi PAB1336P.horikoshii gi3256896P.abyssi PAB2066P.horikoshii gi3258290P.abyssi PAB1026P.horikoshii gi3256884D.radiodurans DRA00354D.radiodurans DRA0353D.radiodurans DRA0352P.abyssi PAB1189P.horikoshii gi3258414B.burgdorferi gi2688621M.tuberculosis gi1666149V.cholerae VC0512V.cholerae VCA1034V.cholerae VCA0974V.cholerae VCA0068V.cholerae VC0825V.cholerae VC0282V.cholerae VCA0906V.cholerae VCA0979V.cholerae VCA1056V.cholerae VC1643V.cholerae VC2161V.cholerae VCA0923V.cholerae VC0514V.cholerae VC1868V.cholerae VCA0773V.cholerae VC1313V.cholerae VC1859V.cholerae VC1413V.cholerae VCA0268V.cholerae VCA0658V.cholerae VC1405V.cholerae VC1298V.cholerae VC1248V.cholerae VCA0864V.cholerae VCA0176V.cholerae VCA0220V.cholerae VC1289V.cholerae VCA1069V.cholerae VC2439V.cholerae VC1967V.cholerae VCA0031V.cholerae VC1898V.cholerae VCA0663V.cholerae VCA0988V.cholerae VC0216V.cholerae VC0449V.cholerae VCA0008V.cholerae VC1406V.cholerae VC1535V.cholerae VC0840V.cholerae VC0098V.cholerae VCA1092V.cholerae VC1403V.cholerae VCA1088V.cholerae VC1394V.cholerae VC0622NJ*******************************************************************************
    21. 21. TIGRTIGR C. pneumoniae Paralogs by Position 0 250000 500000 750000 1000000 1250000 Subject Orf Position 0 250000 500000 750000 1000000 1250000 Query Orf Position
    22. 22. TIGRTIGR C. pneumoniae Paralogs - Lineage Specific 0 250000 500000 750000 1000000 1250000 Subject Orf Position 0 250000 500000 750000 1000000 1250000 Query Orf Position
    23. 23. TIGRTIGR Evolution and Complete Genomes III: Genome Rearrangements
    24. 24. TIGRTIGR X-files Eisen et al. 2000. Genome Biology 1(6): 11.1-11.9 Also see Tillier and Collins. 2000. Nature Genetics 26(2):195-7.
    25. 25. TIGRTIGR V. cholerae vs. E. coli Best Matching Proteins by Location 0 1000000 2000000 3000000 4000000 5000000 E. coli ORF Coordinates 0 500000 1000000 1500000 2000000 2500000 3000000 V. cholerae ORF Coordinates
    26. 26. TIGRTIGR M. leprae vs. M. tuberculosis Whole Genome Alignment 0 1000000 2000000 3000000 4000000 Mycobacterium tuberculosis 0 1000000 2000000 3000000 Mycobacterium leprae
    27. 27. TIGRTIGR Duplication and Gene Loss Model A B CD E F A B CD E F A B C D E F A B C D E F A’ B’ C’ D’ E’ F’ A B C D E F A’ B’ C’ D’ E’ F’ A C D F A’ B’ E’ E. coli E. coli B C D F A’ B’ D’ E’ V. cholerae A B C D E F A’ B’ C’ D’ E’ F’
    28. 28. TIGRTIGR C. trachomatis MoPn C.pneumoniaeAR39 Origin Terminus C. trachomatis vs C. pneumoniae Dot Plot
    29. 29. TIGRTIGR B1A1B2A2B3A3B3B22423222120191817161514131211109672582627282930123453132 B131326789101112131415161718192021222324252627282930123453132 B32423222120191817161514131211109672582627282933231304521 A131326789101112131415161718192021222324252627282930123453132 A231326789101112131918171615142021222324252627282930123453132 A32678910111213191817161514202122232425262754331302928132 B2Inversion Around Terminus (*) Inversion Around Terminus (*) Inversion Around Origin (*) Inversion Around Origin (*) ******** Common Ancestor of A and B 31326789101112131415161718192021222324252627282930123453132 A2A1A2A3B2B1 Symmetric Inversion Model
    30. 30. TIGRTIGR Why are Inversions Symmetrical Around Origin • Genetic studies in Salmonella and E. coli suggest that there may be strong selection against other inversions – Mahan, Segall, Schmid and Roth – Liu and Sanderson – Rebollo, Francois, and, Louarn
    31. 31. TIGRTIGR Evolution and Complete Genomes IV: Gene Transfer
    32. 32. TIGRTIGR Why Gene Transfers Are Useful to Identify • Laterally transferred genes frequently involved in environmental adaptations and/or pathogenicity • Helps identify transposons, integrons, and other vectors of gene transfer • Helps identify species associations in the environment
    33. 33. TIGRTIGR Tree of Life or Web of Life?
    34. 34. TIGRTIGR Most ‘Evidence’ for Gene Transfer has Alternative Explanations
    35. 35. TIGRTIGR How to Infer Gene Transfers • Unusual distribution patterns • Unusual nucleotide composition • High sequence similarity to supposedly distantly related species • Unusual gene trees • Observe transfer events
    36. 36. TIGRTIGR 100s of DNA Islands in O157:H7 vs. K12: Gene Loss or Transfer?
    37. 37. TIGRTIGR Lateral Transfer Inference Based on Complete Genome Analysis I: Organellar to Nuclear Transfers in A. thaliana
    38. 38. TIGRTIGR A. thaliana Nuclear Proteins: Best Matches to Complete Genomes 0 1000 2000 3000 4000 BestMatches CHLTE PORGI BACSU MCYTU BBUR TREPA CHLPN ECOLI NEIME RICPR CAUCR HELPY SYNSP AQUAE DEIRA THEMA AERPE ARCFU METJA METTH PYRAB CELEG YEAST DROME B A E
    39. 39. TIGRTIGR SYNSP0100200300400500600700800900 Number of Best Matches to This Species050010001500200025003000350040004500 Number of ORFs in Complete Genome Best Matches vs. Prokaryotes
    40. 40. TIGRTIGR Organellar HSP60s DROMECG12101DROMECG7235DROMECG2830DROMECG16954ARATH At2g33210ARATH F14O13.19ARATH MCP4.7YEAST SWCAUCR ORF03639RICPR gi|3861167ECOLI gi|1790586NEIMEb gi|7227233.AQUAE gi|2984379CHLPN gi|4376399|DEIRA ORF02245BACSU gi|2632916SYNSP gi|1652489SYNSP gi|1001103ARATH At2g28000ARATH MRP15.11MCYTU gi|2909515MCYTU gi|1449370THEMA TM0506BBUR gi|2688576TREPA gi|3322286PORGI ORF00933CHLTE ORF00173HELPY gi|2313084 Mitochondrial Forms α−ΠροτεοΧψανοβαχτεριαΠλαστιδ Φορµσ
    41. 41. TIGRTIGR Lateral Transfer Inference Based on Complete Genome Analysis II: Bacterial to Vertebrate Transfers Based on Analysis of the Human Genome
    42. 42. TIGRTIGR Lander et al. ‘Evidence’ • Genes match bacteria not non-vertebrate eukaryotes • Or, genes have stronger match to bacteria than non-vertebrates • A set of ~120 of these genes found in many bacterial species
    43. 43. TIGRTIGR Alternative explanations • Gene loss from non-vertebrate eukaryotes • Rapid divergence in non-vertebrate eukaryotes • Incomplete genomes (e.g., D. melanogaster) • Bad annotation/gene finding • Contamination
    44. 44. TIGRTIGR Evolutionary Rate Variation 231456
    45. 45. TIGRTIGR Trees Don’t Support Transfer Paramecium bursaria Chlorella virus 1Homo sapiens HAS1Mus musculus HAS1Xenopus laevisXenopus laevisDanio rerioHomo sapiensMus musculusDanio rerioXenopus laevisGallus gallusBos taurusHomo sapiensMus musculusRattus norvegicusBradyrhizobium sp SNU001Rhizobium leguminosarumRhizobium spRhizobium lotiRhizobium tropiciRhizobium sp. NodCMesorhizobium sp 7653RSinorhizobium melilotiRhizobium melilotiRhizobium leguminosarumRhizobium galegaeAzorhizobium caulinodansStigmatella aurantiacaStreptomyces coelicolorStreptococcus uberisStreptococcus equisimilisStreptococcus pyogenes HASAStreptococcus pneumoniae0.2 BacteriaVertebratesVirusIIIIII
    46. 46. TIGRTIGR Number of pBVTs is Dependent on # of Genomes Analyzed
    47. 47. TIGRTIGR Birney et al, same issue of Nature as complete genome “The unfinished human genomic DNA may contain contamination, particularly from bacteria but also from other sources. Contaminating DNA is routinely removed from finished sequence, but some is still present in unfinished sequence. If the predicted gene matches a bacterial gene more closely than any vertebrate gene then it will almost always be a contaminant.”
    48. 48. TIGRTIGR Evolution and Complete Genomes V: Species Evolution
    49. 49. TIGRTIGR Whole Genome “Phylogeny”
    50. 50. TIGRTIGR Whole Genome vs. rRNA hanobacterium thermoautotrophicumhaeoglobus fulgidusococcus horikoshiihanococcus jannaschiieropyrum pernixchangeschaeaobacterium tuberculosislus subtilischocystis sp.Aquifex aeolicusermotoga maritimaeinococcus radioduranseponema pallidumorrelia burgdorferiobacter pyloripylobacter jejuniseria meningitidiserichia colio choleraemophilus influenzaeettsia prowazekiioplasma pneumoniaeoplasma genitaliummydia trachomatismydia pneumoniaecterianorhabditis elegansophila melanogastercharomyces cerevisiaekarya
    51. 51. TIGRTIGR Deinococcus radiodurans 2a) RecA2b) SS-rRNAErwinia carotovaraEscherichia coliShigella flexneriEnterobacter agglomeransYersinia pestisSerratia marcescensProteus vulgarisProteus mirabilisVibrio anguilarrumVibrio choleraeHaemophilus influenzaeArabidopsis thaliana CPSTAcetobacter polyoxogenesMethylobacillus flagellatumMethylomonas claraMethylophilus methylotrophusMagnetispirillum magnetotacticumRhizobium phaseoliRhizobium viciaeCorynebacterium glutamicumStreptomyces violaceusMycobacterium lepraeMycobacterium tuberculosisStreptomyces ambofaciensStreptomyces lividansBorrelia burgdorferiBacteroides fragilisChlamydia trachomatisThermus aquaticusThermus thermophilusAquifex pyrophilusThermotoga maritimaLactococcus lactisStreptococcus pneumoniaeBacillus subtilisStaphylococcus aureusAcholeplasma laidlawiiSynechococcus sp. PCC7002Synechococcus sp. PCC7942Anabaena variabilisCampylobacter jejuniHelicobacter pyloriAgrobacterium tumefaciensRhizobium melilotiRhodobacter sphaeroidesRhodobacter capsulatusRickettsia prowazekiiMyxococcus xanthus2Myxococcus xanthus1Xanthomonas oryzaeThiobacillus ferrooxidansAcidiphilium facilisBrucella abortusNeisseria gonorrhoeaePseudomonas fluorescencsPseudomonas aeruginosaAzotobacter vinelandiiPseudomonas putidaAcinetobacter calcoaceticusLegionella pneumophilaBurkholderia cepaciaBordetella pertussisMycoplasma mycoidesMycoplasma pulmonisErwinia carotovaraEscherichia coliEnterobacter agglomeransYersinia pestisSerratia marcescensProteus vulgarisArsenophonus nasoniaeVibrio anguilarrumVibrio choleraeHaemophilus influenzae"Flavobacterium" lutescensNicotiana tabacum CPSTAcetobacter pasterianusMethylobacillus flagellatumMethylomonas methylovoraMethylophilus methylotrophusMagnetispirillum magnetotacticumRhizobium phaseoliRhizobium viciaeCorynebacterium glutamicumStreptomyces coelicolorMycobacterium lepraeMycobacterium tuberculosisStreptomyces ambofaciensStreptomyces lividansBorrelia burgdorferiBacteroides fragilisChlamydia trachomatisThermus aquaticusThermus thermophilusDeinococcus radioduransAquifex pyrophilusThermotoga maritimaLactococcus lactisStreptococcus salivariusBacillus subtilisStaphylococcus aureusAcholeplasma laidlawiiSynechococcus sp. PCC6301Phormidium minutumAnabaena sp. PCC7120Campylobacter jejuniHelicobacter pyloriAgrobacterium tumefaciensRhizobium melilotiRhodobacter sphaeroidesRhodobacter capsulatusRickettsia prowazekiiMyxococcus xanthusXanthomonas oryzaeThiobacillus caldusAcidiphilium facilisBrucella abortusNeisseria gonorrhoeaePseudomonas flavescensPseudomonas aeruginosaPseudomonas putidaAcinetobacter calcoaceticusLegionella pneumophilaBurkholderia cepaciaBordetella pertussisMycoplasma mycoidesMycoplasma pulmonisγ1γ2βαΛοωΓΧΗιγηΓΧδεΧψανο∆/Τ
    52. 52. TIGRTIGR Coming Attractions I: Phylogenetic Profiles
    53. 53. TIGRTIGR Phylogenetic Profile - E.coli Flagellar GenesfhiAfliMfliPfliGflgGfliFflgIflhAflhBgcpE
    54. 54. TIGRTIGR PG Profile. C. tepidum Chlorophyll Synthesis CbiGCbiPDsrNCbiACbiJHCobNBchH1BchH2CobN2BchH3ChlIChlI2ChlI3
    55. 55. TIGRTIGR Coming Attractions II: Uncultured Environmental Species
    56. 56. TIGRTIGR
    57. 57. TIGRTIGR Genomics does not require initial culturing step. • Isolate, by filtration, all bacteria in a water sample • Extract total DNA in very large pieces • Clone those pieces as BACs into E.coli to get enough. • Sequence the BACs like a bacterial genome. Natural Water Filter concentrate Extract DNA Clone Into BACs Sequence Gene List
    58. 58. TIGRTIGR Bacterial Rhodopsin: a new photosynthesis system in the oceans SAR86, an uncultured bacteria BAC Sequenced and Analyzed Beja O, et.al., Science 2000 289:1902-6 Bacterial rhodopsin: evidence for a new type of phototrophy in the sea. Rhodopsin found H+ light H+ ADP ATP Cloned into E. coli E. coli pumps protons in the light
    59. 59. TIGRTIGR 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05 0 m 80 m 750 m γ α β ε Proteobacteria Archaea Best Matches of Bac Ends
    60. 60. TIGRTIGR RecA-Bacteroides/Cytophaga in Monterey Bay BACs Chlorobium tepidum Cytophaga hutchinsonii Prevotella ruminocola Bacteroides fragilis Porphyromonas gingivalis MBBAD68TR MBBAD65TR
    61. 61. TIGRTIGR
    62. 62. TIGRTIGR
    63. 63. TIGRTIGR Wither Genomics? Not yet. • Despite limitations, a great deal can still be learned from genome sequence analysis.
    64. 64. TIGRTIGR Evolutionary Diversity Still Poorly Represented in Complete Genomes Tmf-pendenR-rubrum3Azs-brasi2Rm-vannielRhb-legum8Bdr-japoniSpg-capsulRic-prowazSte-maltopSpr-volutaRub-gelat2Rcy-purpurNis-gonor1Hrh-halch2Alm-vinosmPs-aerugi3E-coliMyx-xanthuBde-stolpiDsv-desulfDsb-postgaC-leptumC-butyric4C-pasteuriEub-barkerC-quercicoHel-chlor2Acp-laidlaM-capricolC-ramosumB-stearothEco-faecalLis-monoc3B-cereus4B-subtilisStc-therm3L-delbruckL-caseiFus-nucleaGlb-violacOlst-lut_CZea mays CNost-muscrSyn-6301Tnm-lapsumFlx-litoraCy-lyticaEmb-brevi2Bac-fragilPrv-rumcolPrb-diffluCy-hutchinFlx-canadaSap-grandiChl-limicoWln-succi2Hlb-pylor6Cam-jejun5Stm-ambofaArb-globifCor-xerosiBif-bifiduCfx-aurantTmc-roseumAqu-pyrophenv-SBAR12env-SBAR16Msr-barkerTpl-acidopMsp-hungatHf-volcaniMb-formiciMt-fervid1Tc-celerArg-fulgidMpy-kandl1Mc-vannielMc-jannascenv-pJP27Sul-acaldaThp-tenaxenv-pJP89Tt-maritimFer-islandMei-ruber4D-radiodurChd-psittaAcbt-capslenv-MC18Pir-staleyLpn-illiniLps-interKSpi-stenosTrp-pallidBor-burgdoSpi-halophBrs-hyodysFib-sucS85Tmf-pendenR-rubrum3Azs-brasi2Rm-vannielRhb-legum8Bdr-japoniSpg-capsulRic-prowazSte-maltopSpr-volutaRub-gelat2Rcy-purpurNis-gonor1Hrh-halch2Alm-vinosmPs-aerugi3E-coliMyx-xanthuBde-stolpiDsv-desulfDsb-postgaC-leptumC-butyric4C-pasteuriEub-barkerC-quercicoHel-chlor2Acp-laidlaM-capricolC-ramosumB-stearothEco-faecalLis-monoc3B-cereus4B-subtilisStc-therm3L-delbruckL-caseiFus-nucleaGlb-violacOlst-lut_CZea mays CNost-muscrSyn-6301Tnm-lapsumFlx-litoraCy-lyticaEmb-brevi2Bac-fragilPrv-rumcolPrb-diffluCy-hutchinFlx-canadaSap-grandiChl-limicoWln-succi2Hlb-pylor6Cam-jejun5Stm-ambofaArb-globifCor-xerosiBif-bifiduCfx-aurantTmc-roseumAqu-pyrophenv-SBAR12env-SBAR16Msr-barkerTpl-acidopMsp-hungatHf-volcaniMb-formiciMt-fervid1Tc-celerArg-fulgidMpy-kandl1Mc-vannielMc-jannascenv-pJP27Sul-acaldaThp-tenaxenv-pJP89Tt-maritimFer-islandMei-ruber4D-radiodurChd-psittaAcbt-capslenv-MC18Pir-staleyLpn-illiniLps-interKSpi-stenosTrp-pallidBor-burgdoSpi-halophBrs-hyodysFib-sucS85 BacteriaArchaeaBacteriaArchaeaA. rRNA tree of Bacterial and Archaeal Major GroupsB. Groups with Completed Genomes Highlighted
    65. 65. TIGRTIGR Limited Ecological and Physiological Diversity • All genomes from cultured species or pathogens/symbionts • Limited ecological diversity – most are from pathogens or thermophiles • Limited physiological diversity – need whole range for particular physiologies, not just extremes
    66. 66. TIGRTIGR
    67. 67. TIGRTIGR Why Completeness is Important • Improves characterization of genome features – Gene order, replication origins • Better comparative genomics – Genome duplications, inversions • Presence and absence of particular genes can be very important (e.g., gene loss) • Missing sequence might be important (e.g., centromere) • Allows researchers to focus on biology not sequencing • Facilitates large scale correlation studies
    68. 68. TIGRTIGR Acknowledgements • Genome inversions: S. Salzberg, J. Heidelberg, O. White, A. Stoltzfus, J. Peterson, H. Ochman • Genome sequences and analysis: J. Heidelberg, T. Read, H. Tettelin, K. Nelson, J. Peterson, R. Fleischmann, D. Bryant • Horizontal transfers: K. Nelson, W. F. Doolittle • TIGR: C. Fraser, J. Venter, M-I. Benito, S. Kaul, Seqcore • $$$: NSF, NIH, ONR, DOE
    69. 69. TIGRTIGR Evolutionary Studies Improve Most Aspects of Genome Analysis • Phylogeny of species places comparative data in perspective • Evolution of genes and gene families – Functional predictions – Identification of orthologs and paralogs – Species specific mutation patterns • Evolution of pathways – Convergence – Prediction of function • Evolution of gene order/genome rearrangements • Phylogenetic distribution patterns • Identification of novel features
    70. 70. TIGRTIGR Genome Information and Analysis Improves Studies of Evolution • Complete genome information particularly useful • Unbiased sampling • More sequences of genes • Presence/absence information needed to infer certain events (e.g., gene loss, duplication) • Genome wide mutation and substitution patterns (e.g., strand bias) • Diversification and duplication
    71. 71. TIGRTIGR
    72. 72. TIGRTIGR
    73. 73. TIGRTIGR Tracing Gene Loss • Need presence and absence information of orthologous genes from different species • Determining absence requires a complete genome • May still miss some homologs (e.g., due to rapid divergence) • Helps to have closely related species • Use standard character state reconstruction methods to infer gene gain and loss

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