Trees - ML and Bayes

Question 1

What does maximum likelihood do

Answer 1

it tries to infer a phylogenetic tree by finding the tree that maximizes the probability of observing the data, given certain assumptions (models)

Question 2

How does the maximum likelihood process work

Answer 2

it requires explicitly models of rates of evolutionary change in the relevant characters and states, it begins with one tree (can be hypothesis or model) then goes on to compare the probability of the data for many different trees/models, searching across tree space for the tree that maximizes the likelihood of the data

Question 3

The maximum likelihood method model includes what 4 things

Answer 3

1. tree topology
2. branch lengths
3. nucleotide frequencies
4. a substitution rate matrix/instantaneous rate matrix

Question 4

What are the 2 models for substitution rate matrix’s

Answer 4

1. Jukes-Cantor (JC) model
2. Kimura two parameter (K2P) model

Question 5

Describe the Jukes Cantor model used for maximum likelihood and finding the substitution rate matrix

Answer 5

• it only has one parameter, the substitution rate, and assumes that all substitutions (changes of character state) are equally likely
• the substitution rate is expressed as α (alpha) per time unit.
• given enough time the probability of change to each of the other 3 states and the probability of no change all approach 0.25
• each column or row adds up to zero

Question 6

Describe the Kimura two-parameter model used for maximum likelihood and finding the substitution rate matrix

Answer 6

this has one rate for transitions (α) and one rate for transversions (β)
- each column or row adds up to zero

Question 7

What is a faster process: maximum likelihood or maximum parsimony

Answer 7

• maximum parsimony - heuristic methods(traditional searching) is almost always used)

Question 8

Describe bayesian inferene

Answer 8

• built on likelihood methods
• based on matrix of character states and requires an evaluation of all possible trees or a heuristic sample of them
• it involves beginning with prior probabilities and then sequentially estimating revised posterior probabilities based on the observations actually made
• it calculates the probability of the hypothesis given the data (reverse of ML)

Question 9

What kind of method does the bayesian inference search involve

Answer 9

markov-chain monte carlo method (MCMC) - this is a random walk simulation–> this leads to convergence on a set of trees for which the likelihoods are similar

Question 9

Difference between Bayesian inference and maximum likelihood

Answer 9

• ML = finds the tree that maximizes the probability of observing the data given certain assumptions/models
• Bayesian = calculates the probability of the hypothesis given the data

Question 10

What is the Bayesian inference model sensitive to

Answer 10

priors and model misspecification

Question 11

What is the order for the methods of tree construction

Answer 11

1. cluster analysis
2. maximum parsimony
3. likelihood methods (ML, BI)