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Phylogenetic reconstruction Flashcards

1
Q

Cladistics vs Phenetics

A

Cladistics: regards combining characters (Apomorphies)

Phenetics: Regards numeric diferences (Distances)

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2
Q

Criterion and method cladistic methods

A
  • Parsimony:
    1. Maximum parsimony (MP)
  • Probabilistic: Likelihood-based:
  1. Maximum likelihood (ML)
  2. Bayesian inference (BI)
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3
Q

Parsimony

A
  • Less number of steps
  • We expect convergence and reversals
    to occur less than synapomorphies
  • less homoplasy
  • # sp = #trees
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4
Q

Advantages and disadvantages Parsimony

A

Advantages:

Is a simple method - easily understood operation

  • Does not depend on an explicit model of evolution
  • Gives both trees and associated hypotheses of character evolution
  • Should give reliable results if the data is well structured

Disadvantages:

May give misleading results if homoplasy is common or concentrated in particular parts of the tree

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5
Q

Character evolution

History vs model

Observed topology

A

History: Hypothesis about the evolution of a particular character or a phylogeny

Model: Specifies probability of change between the start and end of each branch

Observed topology: Different scenarios

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6
Q

Assumptions about character evolution

BUT

A
  1. Unordered (Fitch Parsimony)
  2. Ordered (Wagner Parsimony)
  3. Irreversible (Camin-Sokal Parsimony)
  4. Dollo (Dollo Parsimony): The loss of function is more posible

BUT: there are distances that violate inequality

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7
Q

Compound coding probelms

A
  • Create compound conditions
  • each of such condition might legitimately be consider its own character
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8
Q

Multistate coding problems

A

Phylogenetic information can be lost to the tree search process

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9
Q

Non-additive binary coding problem

A

Non-additive binary coding makes the absence token (usually 0) correspond to a ‘nonspecified other’ variable: The ‘0’ taken becomes a catch-all for anything that isn’t scored as ‘1’

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10
Q

Search for a Parsimony tree

A
  1. Exhaustive search (exact)
  2. Branch-and-bound search (exact)
  3. Heuristic search methods (hopefully exact)
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11
Q

Exhaustive search for the Parsimony tree

A
  • Adding 1 more taxon each time
  • All posible trees
  • Absourd t with more than 10 taxa
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12
Q

Branch and bound search for the Parsimony tree

A

Looking for short cuts (most likely trees)

  • Also t consuming
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13
Q

Heuristic search for the Parsimony tree

A
  1. Create a starting tree
  2. Branch swapping (Randomize the data ser for every search):
  • Multiple random search replicates
    3. new starting point: change points until: less steps
    4. re-start with different order.
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14
Q

Create a staring tree

A
  1. • A greedy method
  2. • Start with 3-taxon tree (Most parsimoniuos)
  3. • Add taxa one at a time.
  4. • Keep only the best tree found so far
  5. • No guarantee of optimality, but may provide good starting point for search
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15
Q

Branch swapping:

A
  • Nearest-Neighbor Interchange (NNI)
  • Subtree Pruning and Regrafting (SPR): cutting & pasting different parts of the tree
  • Tree Bisection and Reconnection (TBR)
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16
Q

Criterion phenetic

A

Distance methods

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17
Q

Distance methods

Criterion

Advantages & Disadvantages

A

Minimum Evolution (ME)

The tree with the shortest sum of the
branch lengths

Advantages:
• Distances can be ‘corrected’ for unseen events.
• Usually faster than character-based methods.
• Can be used for some rate analyses.

-used at the beginning for checking the alignments

Disadvantages:
• Information lost when characters transformed to distances.
• Cannot be used for character analysis.

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18
Q

Examples for distances (ME)

A
  • total number of differences
  • p (= uncorrected) distances
  • corrected distances following evolutionary models

p- distances = (total # differences)/total # characters

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19
Q

Distance methods tree reconstruction

A

Neighbor joining

20
Q

Why Maximum Likelihood

A
  • Multiple substitutions not detectable by parsimony or distance methods
  • Observes the likelihood of every character state
    in a phylogenetic tree
21
Q

Parameters of Maximum Likelihood

A
  1. Substitution probabilities
  2. Base composition
22
Q

Parameter substitution for ML

A

Transitions more frequent than transversion

23
Q

Parameter Base composition in ML

A
  • Amount of character states (ACGT)
  • varies significantly en very organism
24
Q

DNA substitutions models

A
  1. Jukes-Cantor (JC 1969)
  2. Kimura 2 parameters (K2P 1980)
  3. Felstein 1981 (F81)
  4. Hasegawa, Kishino, Yano (HKY 1985)
  5. General time reversible models ( GTR 1990)
  6. Nasty Model
25
Jukes-Cantor DNA substitutions models
* Most parsimonious * Assumes 25% of posibility to each base * Simple model (1 parameter)
26
Kimura DNA substitutions model
* @ Transitions * ß Transvertions * @ different from ß or else: JC * 2 parameter
27
Felstein DNA substitutions model
Unequal base frequencies Π Substitutions equally likely 2 paremeters
28
HKY DNA substitutions model
Transversions and transitions with different substitution rates 3 parameters
29
GTR DNA substitutions model
6 parameter Takes in to account that all transvertions don´t have the same posibilities @ is different from ß All 6 pairs of substitutions have different rates
30
More to improve a DNA substitutions model
* Proportion of invariant sites * Gamma distribution
31
proportion of invariant sites (I) (improve a model)
sequences that evolve fast may show less divergence than sequences than slower sequences - Not all nucleotides evolve freely GTR+I
32
Gamma distributions (G) Improve a model
Nucleotides vary differently, some vary more freely than others. Not equally distributed - Allow more than 2 categories (zero and non-cero rates) GTR+I+G
33
How to chose a model
Problem: The more complex a model, the more computationally expensive. but: If a model is too generalizing, the inferred phylogeny can be wrong. Therefore: Model, that is significant better than others but does not require more parameters than necessary. - Run a model test
34
Model test
hRLT for nested models - Likelihood of the different models - Until there are not significantly differences between the models
35
Likelihood methods
* Maximum likelihood * Bayesian inference * have an explicit probabilistic model * have statistical basis / support * search parameters for most likely answer
36
Bayesian inference (BI) Posterior probability
A priori assumptions The probability of the event of interest under certain conditions. (conditional probability) Likelihood and Prior probability
37
Sampling Procedure Markov Chain Monte Carlo
1: Robot is programmed to walk a pre-defined amount of steps (also called generations), e.g., 2,000,0000 2: Robot evaluates every step in varying length and direction: - if the step is uphill (higher likelihood): always takes step - if the step is downhill: 1. robot calculates a height ratio between the steps 2. generates a random number between 0 and 1 3. if number lower than ratio: take the step if number higher than ratio: it stays at same place 3: Robot evaluates following step... 4: Position (tree topology) of e.g. every 100th step is sampled.
38
Bootstrap values vs Posterior probabilities
Bootstrap: Index that best supports the data given, not a true stadistic. (Split) Posterior Probabilities: The tree that best supports the data.
39
When to stop the robot MCMC
- multiple runs (time intensive) - loooooooong runs (time intensive) - multiple Markov Chains (robots) simultaneously (Metropolis Coupled Markov Chain Monte Carlo = MCMCMC = MC3) -one chain as usual (cold chain) -other chains can make larger steps (heated chains) -chain with the highest probability at every step becomes automatically cold chain and is sampled.
40
maximum likelihood vs Bayesian inference
**ML** Stadistical knowledge no priors Unpredictible running time Branch support can take ages Heuristic search: get stuckin local optima **bayes** No stadistical knowledge Priors T=linear computational complex Branch support inmediatly convergence at burn in
41
Testing for Robustness of the phylogenetic tree
* Bootstrap * Jacknife * Bremer supports (Decay index)
42
Bootstrap
1) Characters are resampled with replacement \> many (100...1000...10,000)... bootstrap replicate data sets 2) Tree from each bootstrap replicate reconstructed 3) Majority-rule consensus of all trees \> Visualization of agreement in topologies 4) Majority rule consensus indices = measure of support for those groups = bootstrap proportions (BPs), - Tells support, but bot quality of the tree - Can be wrong if sampled the wrong kind of data
43
Jacknife
-Jackknifing is very similar to bootstrapping • differs only in resampling strategy • proportion of characters (e.g. 50%) is deleted • Results summarized with a majority-rule consensus tree • Majority rule indices = Jackknife Probabilities • Jackknifing and bootstrapping tend to produce: – broadly similar results – similar interpretations - cutting-off characters
44
Bremer support (Decay index)
* The number of extra steps it takes to collapse a group * Add aditional steps, to see if the topolofy remains * The higher the number, higher the support
45
How to measure Posterior probability?
(conditional probability \* Prior probability) / probability of the data given a specific model
46
How does the robot of MCMC works?
Every step is called a generation Cloud: Sampling a large amount of potential trees Program to always go up: down only under certain conditions
47
Paup index. * Ci * Hi * Ri
**Consistency index Ci** (Deals with apomorphies) Ci=( (minimum total SUM of character changes expected)/(actual amount of steps))\*100 Also useful to compare trees, to check the amount of homoplasies. The higher the Ci: the better (how good the data is, and how the characters can be included in the trees) With Binary characters (0-1): (each character expected to change only one. (parsimony) in the tree) CI=1 if there is no homoplasy negatively correlated with the number of species sampled **Homoplasy index Hi:** The amount of homoplasies= 1-Ci 0,85-1=-0,15 **Retention index** Ri: ((Max steps on the tree - number of state changes in tree)/( Max steps on the tree - number of state changes) Ri= (Max N. Of steps-Steps observed)/(Max N. Of steps-min. Steps) defined to be 0 for parsimony uninformative characters RI=1 if the character fits perfectly RI=0 if the tree fits the character as poorly as possible

Phylogenetics (4 decks)

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