Why phylogenetic reconstruction of molecular evolution?
A) Systematic classification of organisms
e.g.:
Who were the first angiosperms? (i.e. where are the first angiosperms located
relative
to present day angiosperms?)
Where in the tree of life is the last common ancestor located?
B) Evolution of molecules, tracing disease outbreaks (ebola, HIV)
e.g.: domain shuffling, reassignment of function, gene duplications, horizontal gene transfer, drug targets, detection of genes that drive evolution of a species/population (e.g. influenca virus)
C) Identification of organisms
e.g., phylotyping in microbiom samples,
origin of genes and viruses
How:
1) Obtain sequences
Sequencing
Databank Searches -> ncbi a) entrez, b) BLAST, c) blast of pre-release data
Friends
2) Determine homology (see notes for earlier classes for practical implementation)
Reminder on Definitions:
Homology: Two sequences are homologous, if there
existed an ancestral molecule in the past that is ancestral to both of the sequences
3) Align sequences
(most algorithms used for phylogenetic reconstruction require a global alignment. An exception is statalign
from Thorne JL, and Kishino H, 1992, Freeing phylogenies from artifacts of alignment. Mol Bio Evol 9:1148-1162)
Some evolutionary biologists recommend to select only the part of the alignment that is reliable. (Discuss!) Modify alignment, if necessary.
4) Reconstruct evolutionary history
- calculate
pairwise distances
(different distance measures, correction for multiple hits, correction for codon bias) - make distance matrix (table of pairwise corrected distances)
- calculate tree from distance matrix
A) Distance analyses
(e.g.: smallest error between distance matrix
and distances in tree), or use
ii) algorithmic approaches (UPGMA or neighbor joining)
B) Parsimony analyses
find that tree that explains sequence data with minimum number of substitutions
(tree includes hypothesis of sequence at each of the nodes)
C) Maximum Likelihood analyses
given a model for sequence evolution, find the tree that has the highest probability under this model.
This approach can also be used to successively refine the model.
Bayesian statistics use ML analyses to calculate posterior probabilities for trees, clades and evolutionary parameters. Especially MCMC approaches have become very popular in the last year, because they allow to estimate evolutionary parameters (e.g., which site in a virus protein is under positive selection), without assuming that one actually knows the "true" phylogeny.
D
- ...) Else:
spectral analyses, evolutionary parsimony, i.e., look only
at patterns of substitutions, supertrees from many gene trees.
Another way to categorize methods of phylogenetic reconstruction is to ask if they are using
- an optimality criterion (e.g.: smallest error between distance matrix and distances in tree, least number of steps), or
- algorithmic approaches (UPGMA or neighbor joining)
5) Interpret the result.
It is especially important to consider artifacts that might originate in phylogenetic reconstruction, and to asses the reliability of your results.