W2 L2 Phylogenetic p2 W Flashcards

1
Q

Searching tree space

A

-When the number of species increase, the amount of three that connect species increase
-The selection of tree is based on heuristic search

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2
Q

BAsic principle of heuristics

A

-Getting a start tree
-modifying the start tree and evaluating the choices
- many algorithm can be used

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3
Q

Getting a start tree

A
  • stepwise: adding one new member at a time
    -star decomposition: using likelihood to create polytomy from an uprooted tree
  • distance method
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4
Q

Branch swapping

A

NNI: nearest neighbor interchange, evaluate the change and compare it to the original. The better one is then kept SPR: sub tree pruning and regrafiting, a branch is moved into a new position
TBR: tree bisection & reconnection. Separate and reconnect in a different configuration

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5
Q

why must we carryout Heuristic search many time

A

Hill climbing algorithms will not always climb the tallest hill (when only used once)
Software will differ in how likely they are to find the optimal tree
* Use multiple start trees to start hill climbing process

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6
Q

Why use different model

A
  • different result based on different model
    -Maximum likelihood is Favor over MP when two set of data are contradicting
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7
Q

Advantages of likelihood-based methods

A
  • Deals better than MP with differences in rates between lineages (be cautious of long branches clustering together)
  • ML handles this better because it takes the possibility of homoplasies into consideration (parallel, convergent and back substitutions)
  • Major advantage that different types of substitutions (and different sites) carry a more realistic weight thanks to use of models
  • Reasonably robust against different types of model violations as long as model is a decent approximation of actual process of molecular evolution
  • Computationally more expensive
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8
Q

Bayesian phylogenetic inference

A
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9
Q

Enriching interpritation

A

-taking the data, letting the branch length to associate with a factor (time/ amount of evolution)

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10
Q

Dating trees for richer interpretaion

A
  • Interpret branching times with earth history
  • Localise key events in time (e.g. genome duplication, radiation of gene family)
  • Interpret biogeographic scenarios
  • When did characters originate?
  • Calculate rates of speciation
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11
Q

Molecular clock and phylogeny

A
  • The “molecular clock” ticks at a constant rate
  • Seldom true à relax that assumption by modelling rate variation
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12
Q

Relaxing the molecular clock

A

-take into account autocorrelation: rates are hereditary due to these hereditary element:
* Generation times
* DNA proofreading mechanisms
* Habitat conservatism
* Small rate changes are more likely than large changes
OR sample branch lengths from a distribution

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13
Q

Using fossils to constrain node ages

A

-look at fossil that is linked to our organism group based on morphology
-Chronogram with node ages and error bars this inform us the age (length of the branch)

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14
Q

question in regard to Trait evolution

A
  • When did a particular character appear in the history of a group and how does this match up with earth history?
  • How frequently do changes in that character happen over evolutionary time?
  • Are different evolutionary changes equally likely to happen?
  • Do some organisms evolve faster than others?
  • What’s the role of directional selection in shaping organisms’ traits?
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15
Q

Macroevolutionary inference

A

-From fossil record: observe when characters appear and disappear
-relationship unclear
From phylogenies
l relationships clear
l infer when characters appear and disappear

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16
Q

Inferring trait evolution

A

Similar approach to phylogeny inference
Data > phylogeny + trait values for terminal nodes (species)
Model > depends on situation
Model estimation à parameter inference
Usually with ML
Model comparison à information criteria (often AICc)

17
Q

Ancestral state estimation

A
  • ML optimization
  • Stochastic mapping
  • Relative probabilities of different state