slide1 n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Sequence alignment: PowerPoint Presentation
Download Presentation
Sequence alignment:

Loading in 2 Seconds...

play fullscreen
1 / 45

Sequence alignment: - PowerPoint PPT Presentation


  • 213 Views
  • Uploaded on

CLUSTALW. MUSCLE. Sequence alignment:. Removing ambiguous positions:. T-COFFEE. FORBACK. SEQBOOT. Generation of pseudosamples:. PROTDIST. TREE-PUZZLE. PROTPARS. PHYML. Calculating and evaluating phylogenies: . NEIGHBOR. FITCH. SH-TEST in TREE-PUZZLE. CONSENSE.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Sequence alignment:' - feng


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1

CLUSTALW

MUSCLE

Sequence alignment:

Removing ambiguous positions:

T-COFFEE

FORBACK

SEQBOOT

Generation of pseudosamples:

PROTDIST

TREE-PUZZLE

PROTPARS

PHYML

Calculating and evaluating phylogenies:

NEIGHBOR

FITCH

SH-TEST in TREE-PUZZLE

CONSENSE

Comparing phylogenies:

Comparing models:

Maximum Likelihood Ratio Test

Visualizing trees:

ATV, njplot, treeview …

Some possible pathways from sequence to tree, model and support values.

phyml
phyml

PHYML - A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood

An online interface is here ; there is a command line version that is described here (not as straight forward as in clustalw);

a phylip like interface is automatically invoked, if you type

“phyml” – the manual is here.

The paper describing phyml is here,

a brief interview with the authors is here

treepuzzle ne puzzle
TreePuzzle ne PUZZLE

TREE-PUZZLE is a very versatile maximum likelihood program that is particularly useful to analyze protein sequences. The program was developed by Korbian Strimmer and Arnd von Haseler (then at the Univ. of Munich) and is maintained by von Haseler, Heiko A. Schmidt, and Martin Vingron

(contacts see http://www.tree-puzzle.de/).

tree puzzle
TREE-PUZZLE
  • allows fast and accurate estimation of ASRV (through estimating the shape parameter alpha) for both nucleotide and amino acid sequences (see here for figures).
  • It has a “fast” algorithm to calculate trees through quartet puzzling (calculating ml trees for quartets of species and building the multispecies tree from the quartets).
  • The program provides confidence numbers (puzzle support values), which tend to be smaller than bootstrap values (i.e. provide a more conservative estimate),
  • the program calculates branch lengths and likelihood for user defined trees, which is great if you want to compare different tree topologies, or different models using the maximum likelihood ratio test.
  • Branches which are not significantly supported are collapsed.
  • TREE-PUZZLE runs on "all" platforms
  • TREE-PUZZLE reads PHYLIP format, and communicates with the user in a way similar to the PHYLIP programs.
maximum likelihood ratio test
Maximum likelihood ratio test

If you want to compare two models of evolution (this includes the tree) given a data set, you can utilize the so-called maximum likelihood ratio test. 

If L1 and L2 are the likelihoods of the two models, d =2(logL1-logL2) approximately follows a Chi square distribution with n degrees of freedom. Usually n is the difference in model parameters. I.e., how many parameters are used to describe the substitution process and the tree. In particular n can be the difference in branches between two trees (one tree is more resolved than the other).

In principle, this test can only be applied if on model is a more refined version of the other. In the particular case, when you compare two trees, one calculated without assuming a clock, the other assuming a clock, the degrees of freedom are the number of OTUs – 2 (as all sequences end up in the present at the same level, their branches cannot be freely chosen) .

To calculate the probability you can use the CHISQUARE calculator for windows available from Paul Lewis.

slide6

TREE-PUZZLE allows (cont)

  • TREEPUZZLE calculates distance matrices using the ml specified model. These can be used in FITCH or Neighbor.
  • PUZZLEBOOT automates this approach to do bootstrap analyses – WARNING: this is a distance matrix analyses!
  • The official script for PUZZLEBOOT is here – you need to create a command file (puzzle.cmds), and puzzle needs to be envocable through the command puzzle.
  • Your input file needs to be the renamed outfile from seqboot
  • A slightly modified working version of puzzleboot_mod.sh is here, and here is an example for puzzle.cmds . Read the instructions before you run this!
  • Maximum likelihood mapping is an excellent way to
  • assess the phylogenetic information contained in a dataset.
  • ML mapping can be used to calculate the support around one branch.
  • @@@ Puzzle is cool, don't leave home without it! @@@
slide7

ml mapping

From: Olga Zhaxybayeva and J Peter Gogarten BMC Genomics 2002, 3:4 

slide8

ml mapping

Figure 5. Likelihood-mapping analysis for two biological data sets. (Upper) The distribution patterns. (Lower) The occupancies (in percent)for the seven areas of attraction. (A) Cytochrome-b data fromref. 14. (B) Ribosomal DNA of major arthropod groups (15).

From: Korbinian Strimmer and Arndt von HaeselerProc. Natl. Acad. Sci. USAVol. 94, pp. 6815-6819, June 1997

slide9

ml mapping can asses the topology surrounding an individual branch :

E.g.: If we want to know if Giardia lamblia forms the deepest branch within the known eukaryotes, we can use ML mapping to address this problem. 

To apply ml mapping we choose the "higher" eukaryotes as cluster a, another deep branching eukaryote (the one that competes against Giardia) as cluster b, Giardia as cluster c, and the outgroup as cluster d.  For an example output see this sample ml-map. 

An analysis of the carbamoyl phosphate synthetase domains with respect to the root of the tree of life is here. 

ml mapping can asses the not necessarily treelike histories of genome
ml mapping can asses the not necessarily treelike histories of genome

Application of ML mapping to comparative Genome analyses

see here for a comparison of different probability measures. Fig. 3: outline of approach Fig. 4: Example and comparison of different measures

see here for an approach that solves the problem of poor taxon sampling that is usually considered inherent with quartet analyses.Fig. 2: The principle of “analyzing extended datasets to obtain embedded quartets” Example next slides:

slide11

(a,b)-(c,d) /\ / \ / \ / 1 \ / \ / \ / \ / \ / \/ \ / 3 : 2 \ / : \ /__________________\ (a,d)-(b,c) (a,c)-(b,d)Number of quartets in region 1: 68 (= 24.3%)Number of quartets in region 2: 21 (= 7.5%)Number of quartets in region 3: 191 (= 68.2%)Occupancies of the seven areas 1, 2, 3, 4, 5, 6, 7: (a,b)-(c,d) /\ / \ / 1 \ / \ / \ / /\ \ / 6 / \ 4 \ / / 7 \ \ / \ /______\ / \ / 3 : 5 : 2 \ /__________________\ (a,d)-(b,c) (a,c)-(b,d)Number of quartets in region 1: 53 (= 18.9%)

Number of quartets in region 2: 15 (= 5.4%) Number of quartets in region 3: 173 (= 61.8%) Number of quartets in region 4: 3 (= 1.1%)

Number of quartets in region 5: 0 (= 0.0%)

Number of quartets in region 6: 26 (= 9.3%)

Number of quartets in region 7: 10 (= 3.6%)

Cluster a: 14 sequencesoutgroup (prokaryotes)

Cluster b: 20 sequencesother Eukaryotes

Cluster c: 1 sequencesPlasmodium

Cluster d: 1 sequences Giardia

tree puzzle problems drawbacks
TREE-PUZZLE – PROBLEMS/DRAWBACKS
  • The more species you add the lower the support for individual branches. While this is true for all algorithms, in TREE-PUZZLE this can lead to completely unresolved trees with only a few handful of sequences.
  • Trees calculated via quartet puzzling are usually not completely resolved, and they do not correspond to the ML-tree:The determined multi-species tree is not the tree with the highest likelihood, rather it is the tree whose topology is supported through ml-quartets, and the lengths of the resolved branches is determined through maximum likelihood.
puzzle example
puzzle example

The “best” tree might not be the true tree.

When can one conclude that a tree is a significantly worse explanation for the data compared to the best tree? Estimate the probability that a dataset might have resulted from a given tree.

Example:

Kira’s kangaroo data

Usertrees - SH test - go through outfile

(PARS, PROTPARS and DNAPARS perform a similar test when confronted with multiple usertrees)

slide14

COMPARISON OF DIFFERENT SUPPORT MEASURES

A: mapping of posterior probabilities according to Strimmer and von Haeseler

B: mapping of bootstrap support values

C: mapping of bootstrap support values from extended datasets

Zhaxybayeva and Gogarten, BMC Genomics 2003 4: 37

slide15

bootstrap values from extended datasets

More gene families group species according to environment than according to 16SrRNA phylogeny

In contrast, a themophilic archaeon has more genes grouping with the thermophilic bacteria

ml-mapping

versus

bayes theorem

P(data|model, I)

P(model|data, I) = P(model, I)

P(data,I)

Likelihood

describes how well the model predicts the data

Bayes’ Theorem

Posterior

Probability

represents the degree

to which we believe a given model accurately describes the situation

given the available data and all of our prior information I

Prior

Probability

describes the degree to which we believe the model accurately describes reality

based on all of our prior information.

Normalizing

constant

Reverend Thomas Bayes (1702-1761)

slide17

Elliot Sober’s Gremlins

Observation: Loud noise in the attic

?

Hypothesis: gremlins in the attic playing bowling

Likelihood = P(noise|gremlins in the attic)

P(gremlins in the attic|noise)

?

?

slide18

Li

pi=

L1+L2+L3

Ni

pi

Ntotal

Alternative Approaches to Estimate

Posterior Probabilities

Bayesian Posterior Probability Mapping with MrBayes(Huelsenbeck and Ronquist, 2001)

Problem:

Strimmer’s formula

only considers 3 trees

(those that maximize the likelihood for

the three topologies)

Solution:

Exploration of the tree space by sampling trees using a biased random walk

(Implemented in MrBayes program)

Trees with higher likelihoods will be sampled more often

,where Ni - number of sampled trees of topology i, i=1,2,3

Ntotal – total number of sampled trees (has to be large)

slide19

Illustration of a biased random walk

Figure generated using MCRobot program (Paul Lewis, 2001)

slide20

Spectral Decomposition of Phylogenetic Data

Phylogenetic information present in genomes

Break information

into small quanta of information (bipartitions or embedded quartets)

Analyze spectra to detect transferred genes and plurality consensus.

slide21

BIPARTITION OF A PHYLOGENETIC TREE

Bipartition (or split) – a division of a phylogenetic tree into two parts that are connected by a single branch.

It divides a dataset into two groups, but it does not consider the relationships within each of the two groups.

Yellowvs Rest * ** .. .**

95

compatible to illustratedbipartition

Orange vs Rest. .* . . ..*

* ** . . .. .

incompatible to illustratedbipartition

slide22

“Lento”-plot of 34 supported bipartitions (out of 4082 possible)

  • 13 gamma-
  • proteobacterial
  • genomes (258 putative orthologs):
  • E.coli
  • Buchnera
  • Haemophilus
  • Pasteurella
  • Salmonella
  • Yersinia pestis (2 strains)
  • Vibrio
  • Xanthomonas (2 sp.)
  • Pseudomonas
  • Wigglesworthia

There are 13,749,310,575 possible unrooted tree topologies for 13 genomes

slide23

Consensus clusters of eight significantly supported bipartitions

Phylogeny of putatively transferred gene(virulence factor homologs (mviN))

only 258 genes analyzed

slide24

“Lento”-plot of supported bipartitions (out of 501 possible)

  • 10 cyanobacteria:
  • Anabaena
  • Trichodesmium
  • Synechocystis sp.
  • Prochlorococcus marinus (3 strains)
  • Marine Synechococcus
  • Thermo- synechococcus elongatus
  • Gloeobacter
  • Nostoc punctioforme

Number of datasets

Based on 678 sets of orthologous genes

Zhaxybayeva, Lapierre and Gogarten, Trends in Genetics, 2004, 20(5): 254-260.

slide25

The phylogeny of ribulose bisphosphate carboxylase large subunit

Consensus clusters corresponding to three significantly supported bipartitions

Zhaxybayeva, Lapierre and Gogarten, Trends in Genetics, 2004, 20(5): 254-260.

slide26

Example of bipartition analysis for five genomes of photosynthetic bacteria(188 gene families)

Bipartitions supported by genes from chlorophyll biosynthesis pathway

total 10 bipartitions

R: Rhodobacter capsulatus, H: Heliobacillus mobilis, S: Synechocystis sp., Ct: Chlorobium tepidum, Ca: Chloroflexus aurantiacus

H

Ca

R

Zhaxybayeva, Hamel, Raymond, and Gogarten, Genome Biology 2004, 5: R20

Plurality

Ct

S

R

Ca

H

Chl. Biosynth.

Ct

S

slide27

Phylogenetic Analyses of Genes from chlorophyll biosynthesis pathway

(extended datasets)

Xiong et al. Science, 2000 289:1724-30

Zhaxybayeva, Hamel, Raymond, and Gogarten, Genome Biology 2004, 5: R20

R: Rhodobacter capsulatus, H: Heliobacillus mobilis, S: Synechocystis sp., Ct: Chlorobium tepidum, Ca: Chloroflexus aurantiacus

slide28

PROBLEMS WITH BIPARTITIONS

  • No easy way to incorporate gene families that are not represented in all genomes.
  • The more sequences are added, the shorter the internal branches become, and the lower is the bootstrap support for the individual bipartitions.
  • A single misplaced sequence can destroy all bipartitions.
slide29

:embedded quartet for genomes 1, 4, 9, and 10 .

This bootstrap sample supports the topology ((1,4),9,10).

Bootstrap support values for embedded quartets

: tree calculated from one pseudo-sample generated by bootstraping from an alignmentof one gene family present in 11 genomes

+

9

9

10

1

1

1

10

4

10

9

4

4

Zhaxybayeva et al. 2006, Genome Research, in press

Quartet spectral analyses of genomes iterates over three loops:

  • Repeat for all bootstrap samples.
  • Repeat for all possible embedded quartets.
  • Repeat for all gene families.
slide30

Iterating over Bootstrap Samples

This gene family for the quartet of species A, B, C, D

Supports the Topology ((A, D), B, C) with 70% bootstrap support

slide31

:embedded quartet for genomes 1, 4, 9, and 10 .

This bootstrap sample supports the topology ((1,4),9,10).

Bootstrap support values for embedded quartets

: tree calculated from one pseudo-sample generated by bootstraping from an alignmentof one gene family present in 11 genomes

+

9

9

10

1

1

1

10

4

10

9

4

4

Quartet spectral analyses of genomes iterates over three loops:

  • Repeat for all bootstrap samples.
  • Repeat for all possible embedded quartets.
  • Repeat for all gene families.
slide32

Illustration of one component of a quartet spectral analysesSummary of phylogenetic information for one genome quartet for all gene families

Total number of gene families containing the species quartet

Number of gene families supporting the same topology as the plurality (colored according to bootstrap support level)

Number of gene families supporting one of the two alternative quartet topologies

slide33

Quartet Spectrum

of 11 cyanobacterialgenomes

330 possible quartets

1128 datasets from relaxed core (core datasets + datasets with one or two taxa missing)

Number of datasets

685 datasets show conflicts with plurality

quartets

slide35

Conflicts with plurality signal are observed in sets of orthologs across all functional categories, including genes involved in translation and transcription

624/1128 ≈ 55%

slide36

Genes with orthologs outside the cyanobacterial phylum:

Distribution among Functional Categories

(using COG db, release of March 2003)

Cyanobacteria do form a coherent group, but conflict with plurality (294)

700 phylogenetically useful extended datasets

Cyanobacteria do not form a coherent group (160)

slide38

Species evolution versus plurality consensus

  • In case of the marine Synecchococcus and Prochlorococcus spp. the plurality consensus is unlikely to reflect organismal history.
  • This is probably due to frequent gene transfer mediated by phages e.g.:
  • These conflicting observations are not limited to pro-karyotes. In incipient species of Darwin’s finches frequentintrogression can make some individuals characterized by morphology and mating behavior as belonging to the same species genetically more similar to a sister species (Grant et al. 2004 “Convergent evolution of Darwin's finches caused by introgressive hybridization and selection” Evolution Int J Org Evolution 58, 1588-1599).
slide40

Coalescence – the process of tracing lineages backwards in time to their common ancestors. Every two extant lineages coalesce to their most recent common ancestor. Eventually, all lineages coalesce to the cenancestor.

t/2

(Kingman,

1982)

Illustration is from J. Felsenstein, “Inferring Phylogenies”, Sinauer, 2003

slide41

Coalescence ofORGANISMALandMOLECULAR Lineages

Time

  • 20 lineages
  • One extinction and one speciation event per generation
  • One horizontal transfer event once in 5 generations (I.e., speciation events)
  • RED: organismal lineages (no HGT)
  • BLUE: molecular lineages (with HGT)
  • GRAY: extinct lineages
  • RESULTS:
  • Most recent common ancestors are different for organismal and molecular phylogenies
  • Different coalescence times
  • Long coalescence time for the last two lineages
slide42

Y chromosome

Adam

Mitochondrial

Eve

Lived

approximately

50,000 years ago

Lived

166,000-249,000

years ago

Thomson, R. et al. (2000) Proc Natl Acad Sci U S A 97, 7360-5

Underhill, P.A. et al. (2000) Nat Genet 26, 358-61

Cann, R.L. et al. (1987) Nature 325, 31-6

Vigilant, L. et al. (1991) Science 253, 1503-7

Albrecht Dürer, The Fall of Man, 1504

Adam and Eve never met 

The same is true for ancestral rRNAs, EF, ATPases!

slide44

Lineages Through Time Plot

10 simulations of organismal evolution assuming a constant number of species (200) throughout the simulation; 1 speciation and 1 extinction per time step. (green O)

25 gene histories simulated for each organismal history assuming 1 HGT per 10 speciation events (red x)

log (number of surviving lineages)

green:organismal lineages ; red:molecular lineages (with gene transfer)

slide45

The deviation from the “long branches at the base” pattern could be due to

  • under sampling
  • an actual radiation
    • due to an invention that was not transferred
    • following a mass extinction

Bacterial 16SrRNA based phylogeny(from P. D. Schloss and J. Handelsman, Microbiology and Molecular Biology Reviews, December 2004.)