MAXIMUM LIKELIHOOD

Question 1

2 Major components of phylogenetic analysis

Answer 1

1. Phylogeny inference or “tree building”
2. Character and rate analysis

Question 2

the inference of the branching orders, and ultimately the evolutionary relationships, between “taxa” (entities such as genes, populations, species, etc.)

Answer 2

phylogeny inference or “tree building”

Question 3

using phylogenies as analytical frameworks for rigorous understanding of the evolution of various traits or conditions of interest

Answer 3

character and rate analysis

Question 4

review common phylogenetic tree terminology and types of trees at d module given

Answer 4

ok

Question 5

are mathematical and/or statistical methods for inferring the divergence order of taxa, as well as the lengths of the branches that connect them

Answer 5

molecular phylogenetics: tree building methods

Question 6

use the aligned characters, such as DNA or protein sequences, directly during tree inference

Answer 6

character-based methods

Question 7

transform the sequence data into pairwise distances and then use the matrix during tree building

Answer 7

distance-based methods

Question 8

which tree building method uses character-based and optimality criterion?

Answer 8

parsimony
maximum likelihood

Question 9

which tree building method uses distance-based and optimality criterion?

Answer 9

minimum evolution
least squares

Question 10

which tree building method uses distance-based and clustering algorithm?

Answer 10

UPGMA & NJ

Question 11

having likeliness or resemblance (an observation)

Answer 11

similar

Question 12

genetically connected (a historical fact)

Answer 12

related

Question 13

types of computational methods

Answer 13

clustering algorithm
optimality approaches

Question 14

use pairwise distances

Answer 14

clustering algorithm

Question 15

are purely algorithmic methods, in which the algorithm itself defines the tree selection criterion

Answer 15

clustering algorithm

Question 16

tend to be very fast programs that produce singular tree rooted by distance

Answer 16

clustering algorithm

Question 17

no objective function to compare to other trees, even if numerous other trees could explain the data equally well

Answer 17

clustering algorithm

Question 18

REMEMBER: finding a singular tree is not necessarily same as finding the “true” evolutionary tree

Answer 18

ok

Question 19

uses either character or distance data

Answer 19

optimality approaches

Question 20

first define an optimality criterions (minimum branch lengths, fewest no. of events, highest likelihood), and then use a specific algorithm for finding trees with the best value for the objective function

Answer 20

optimality approaches

Question 21

can identify many equally optimal trees, if such exist

Answer 21

optimality approaches

Question 22

REMEMBER: Finding an optimal tree is not necessarily the same as finding the “true” tree

Answer 22

ok

Question 23

this method was first proposed by the English statistician R.A. Fisher in 1922

Answer 23

maximum likelihood

Question 24

the probability of observing data under the assumed model will change depending on the parameter values of the model

Answer 24

maximum likelihood

Question 25

the aim is to choose the value of the parameter that maximizes the probability of finding the data

Answer 25

maximum likelihood

Question 26

3 main components of maximum likelihood

Answer 26

- data - a model describing the probability of observing the data - a criterion that allows us to move from the data and model to an estimate of the parameters of the model

Question 27

review the simple coin tossing experiment as an example

Answer 27

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Question 28

study of different life forms (process of evolution)

Answer 28

phylogenetics

Question 29

reconstruct the evolutionary relationship between species and to estimate the time of divergence between two organisms since they shared a last common ancestor

Answer 29

phylogenetics

Question 30

____ analysis of DNA or protein sequences has become an important tool for studying the evolutionary history of organisms from bacteria to humans

Answer 30

phylogenetics

Question 31

Optimality criterion: NONE. The algorithm itself builds "the" tree.

Answer 31

Clustering Methods (UPGMA & NJ)

Question 32

Advantages: * Can be used on indirectly-measured distances (immunological, hybridization). * Distances can be 'corrected for unsean events. * The fastest of the methods avallable (N-J is screamingly fasti). * Can therefore analyze very large datasets quickly (needed for HIV, etc.). * Can be used for some types of rate and date analysis.

Answer 32

Clustering Methods (UPGMA & NJ)

Question 33

Disadvantages: * Similarity and relationship are not necessarily the same thing, so clustering by similarity does nat necessarily give an evolutionary tree. Cannot be used for character analysis! * Have no explicit optimization criteria, so one cannot even know if the program worked property to find the correct tree for the method.

Answer 33

Clustering Methods (UPGMA & NJ)

Question 34

Optimality criterion: The 'most-parsimonious' tree is the one that requires the fewest number of evolutionary events (e.g., nucleotide substitutions, amino acid replacements) to explain the sequences.

Answer 34

Parsimony Method

Question 35

Advantagos: * Are simple, intuitive, and logical (many possible by 'pencil-and-paper). * Can be used on molecular and non-molecular (e.g., morphological) data. * Can tease apart types of similarity (shared-derived, shared-ancestral, homoplasy) * Can be used for character (can infer the exact substitutions) and rate analysis. * Can be used to infer the sequences of the extinct (hypothetical) ancestors.

Answer 35

Parsimony Method

Question 36

Disadvantages: - simple, intuitive, and logical (derived from "Medieval logic", not statistics!) - can be fooled by high levels of homoplasy ("same" events) - can become positively misleading in the "Felsenstein Zone"

Answer 36

parsimony method

Question 37

Optimality criterion: ML methods evaluate phylogenetic hypotheses in terms of the probability that a proposed model of the evolutionary process and the proposed unrooted tree would give rise to the observed data. The tree found to have the highest ML value is considered to be the preferred tree.

Answer 37

Maximum Likelihood (ML) Methods

Question 38

Advantages: * Are inherently statistical and evolutionary model-based. * Usually the most 'consistent of the methods avaitable. * Can be used for character (can infer the exact substitutions) and rate analysis. * Can be used to infer the sequences of the extinct (hypothelical) ancestors. * Can help account for branch-length eifects in unbalanced trees. * Can be applied to nucleotide or amino acid sequences, and other types of data.

Answer 38

Maximum Likelihood (ML) Methods

Question 39

Disadvantages: * Are not as simple and intuitive as many other methods. * Are computationally very intense (limits number of taxa and length of sequence). * Like parsimony, can be fooled by high levels of homoplasy. * Violations of the assumed model can lead to incorrect trees.

Answer 39

Maximum Likelihood (ML) Methods