BIOINFORMATICSDR. VÍCTOR TREVIÑOVTREVINO@ITESM.MX

Multiple Sequence AlignmentsandPhylogeny

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SEQUENCE SIMILARITY Within a protein sequence, some regions

will be more conserved than others. As more conserved, more important. for function for 3D structure for localization for modification for interaction for regulation/control for transcriptional regulation

(in DNA)

REASONS TOPERFORM

SEQUENCESIMILARITYANALYSIS

ANDSEARCHES

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SEQUENCE ALIGNMENT Procedure for comparing two (pair-wise

alignment) or more (multiple sequence alignment) sequences by searching for similar patterns that are in the same order in the sequences Identical residues (nt or aa) are placed in the same

column Non-identical residues can be placed in the same

column or indicated as gaps

Wikipedia, http://www-personal.umich.edu/~lpt/fgf/fgfrcomp.htmBioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

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MULTIPLE SEQUENCE ANALYSIS – ADDITIONAL USES

Interesting regions Promoter regions Consensus sequence for probe

design

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Multiple Sequence Alignment - MSA

Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

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MULTIPLE SEQUENCE ALIGNMENT - MSADynamical programming is designed for two

sequences It would take quite a long time for three or

more (see MSA program)

Sequence A

Seq

uenc

e B

Sequence C

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RELATION MSA & EVOLUIONARY TREE RECONSTRUCTION

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MULTIPLE SEQUENCE ALIGNMENT – METHODS

Extenstions of sequence pair alignment MSA

Progressive Methods CLUSTALW

Iterative Methods Hidden Markov Models (HMM)

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MULTIPLE SEQUENCE ALIGNMENT - MSAAlgorithm

1. Calculate all pair-wise alignment scores (alignment costs).

2. Use the scores (costs) to predict a tree.3. Calculate pair weights based on the tree.4. Produce a heuristic msa based on the tree.5. Calculate the maximum for each sequence

pair.6. Determine the spatial positions that must be

calculated to obtain the optimal alignment.7. Perform the optimal alignment.8. Report the epsilon found compared to the

maximum epsilon.epsilon for a given sequence pair is the difference between the score of the alignment of that pair in the msa and the score of the optimal pair-wise alignment. The bigger the value of , the more divergent the msa from the pair-wise alignment and the smaller the contribution of tht alignment to the msa. For example, if an extra copy of one of the sequences is added to the alignment project, then for sequence pairs that do not include that sequence will increase, indicating a lesser role because the contributions of that pair have been out-voted by the alike sequences.

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PROGRESSIVE MULTIPLE SEQUENCE ALIGNMENTDynamical programming is designed for two

sequences It would take quite a long time for three or

more (see MSA program)Therefore… 1. Pair-wise all sequences2. Determine "distances between each one"3. Align the two most similar then get the alignment4. Get the next more similar and perform the same

steps until all sequences has been included5. E.G.

1. (S3+S4)=c1,2. (S1+S2)=c23. (c1+c2)=c34. (c3+S5)=final

S1S2S3S4S5

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PROGRESSIVE MULTIPLE SEQUENCE ALIGNMENT - CLUSTALW

Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

(then normalized tolargest = 1)

Alignment Scorefor column

CLUSTALWMETHOD

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PROGRESSIVE MULTIPLE SEQUENCE ALIGNMENT - CLUSTALW

Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

1

2

3

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PROGRESSIVE MULTIPLE SEQUENCE ALIGNMENT - PROBLEMS

Dependency on the most similar sequences Nested problems when most similar

sequences are actually different So, for closely related sequence, CLUSTALW is

the best Choice of suitable scoring matrices

Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

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ITERATIVE MULTIPLE SEQUENCE ALIGNMENT

Try to correct for the dependency on the most similar sequences in progressive methods

Repeatedly realigning subgroups, then aligning these on the global alignment Based in tree ordering, separation of

sequences, or random grupo selection

Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

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ITERATIVE MULTIPLE SEQUENCE ALIGNMENT

Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

vtrevino@itesm.mxHMM MULTIPLE SEQUENCE ALIGNMENT

Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

D1

vtrevino@itesm.mxMULTIPLE SEQUENCE ALIGNMENT - PROGRAMS

Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

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MULTIPLE SEQUENCE ALIGNMENT - OVERVIEW

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PHYLOGENY ANALYSIS AND PREDICTION FROM DNA/PROTEIN SEQUENCES

Determination of how the family might have been derived during evolution

Sequences is depicted as branches on a tree

Very similar sequences are located as neighbours in a branch

The goal is to discover all the branching relationships and the branch lengths

Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

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PHYLOGENY ANALYSIS AND PREDICTION FROM DNA/PROTEIN SEQUENCES

Phylogenetic relationships among the genes can help to predict which ones might have an equivalent function.

Phylogenetic analysis may also be used to follow the changes occurring in a rapidly changing species, such as a virus

Important for discovering function, 3D structure, localization, modification,

interaction, regulation/control, transcriptional regulation

Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

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PHYLOGENY ANALYSIS AND PREDICTION FROM DNA/PROTEIN SEQUENCES

Related to SEQUENCE ALIGNMENT

Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

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SEQUENCE SIMILARITY – EVOLUTIONARY RELATIONSHIP

Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

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GENOME COMPLEXITY

Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

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GENOME COMPLEXITY

Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

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EVOLUTIONARY TREE An evolutionary tree is a two-dimensional

graph showing evolutionary relationships among organisms

The separate sequences are referred to as taxa (singular taxon), defined as phylogenetically distinct units on the tree

The tree is composed of outer branches (or leaves) representing the taxa and nodes and branches representing relationships among the taxa

Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

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EVOLUTIONARY TREE A and B are derived

from a common ancestor

each node in the tree represents a splitting of the evolutionary path of the gene into two different species that are isolated reproductively

Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

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EVOLUTIONARY TREE Beyond spliting, any

further evolutionary changes in each new branch are independent of those in the other new branch

The length of each branch to the next node represents the number of sequence changes that occurred prior to the next level of separation

Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

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EVOLUTIONARY TREE Uniform mutation

rate Molecular Clock Hypothesis, suitable for closely related species

Special cases could use non-uniform rates

The root is defined by including a taxon that we are reasonably sure branched off earlier than the other

Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

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EVOLUTIONARY TREE The sum of all the branch

lengths in a tree is referred to as the tree length.

The tree is also a bifurcating or binary tree, in that only two branches emanate from each node.

Trees can have more than one branch emanating from a node if the events separating taxa are so close that they cannot be resolved, or to simplify the tree.

The unrooted tree also shows the evolutionary relationships among sequences A–D, but it does not reveal the location of the oldest ancestry.

Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

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EVOLUTIONARY TREE The number of possible rooted trees

increases very rapidly with the number of sequences or taxa

Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

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METHODS TO BUILD EVOLUTIONARY TREES

To find the evolutionary tree or trees that best account for the observed variation in a group of sequences

Maximum Parsimony Distance Maximum Likelihood

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METHOD SELECTION

Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

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CONSIDERATIONS Not Large number of gaps

Phylogenetic methods analyze conserved regions that are represented in all the sequences (Local Alignments)

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MAXIMUM PARSIMONY (OR MINIMUM EVOLUTION)

Predicts the evolutionary tree by minimizing the number of steps required to generate the observed sequence changes

Requires a multiple sequence alignment Method revise each informative position

and each possible tree same residue in at least two sequences but not

all Used for sequences that are quite similar

and for small number of sequences

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MAXIMUM PARSIMONY (OR MINIMUM EVOLUTION)

Noninformative

Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

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DISTANCE METHODS Employs the number of changes between

each pair Sequence pairs that have the smallest

number of sequence changes are "neighbours" sharing a node in the tree

Very related to Multiple sequence alignment method (CLUSTALW) which produced DISTANCE MATRICES then analysed by distance methods

Remember Distance vs Similarity (and gaps)

Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

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DISTANCE METHODS

Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press

"Idealized"

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DISTANCE ALGORITHMS Fitch and Margoliash Method Neighbor-joining Method Unweighted Pair Group Method

with Arithmetic Mean (UPGMA)

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DISTANCE ALGORITHM Choosing a outgroup (Grupo Fuera)

improves prediction because methods are informed about the "order" of the outgroup

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MAXIMUM LIKELIHOOD Uses probability of the number of

sequence changes Analysis is performed for each

informative residue (like in maximum parsimony)

All possible trees are considered (so, for small number of sequences)

Consider variations in mutation rates, so it can be used for most distant sequences

Main disadvantage: Computation Time

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MAXIMUM LIKELIHOOD Needs a model that provides estimates

of substitution rates for each residue pair

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RELIABILITY OF PHYLOGENETIC PREDICTIONS

Bootstrap method randomly resampling residues within columns (robustness test) Good evidence if more than 70%

predictions are conserved then Collapse branches and confirm tree

length Compare distinct methods and

parameters

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"CLASSIC" PROGRAMS PHYLIPhttp://evolution.genetics.washington.edu/phylip.html

PAUPhttp://paup.csit.fsu.edu/downl.html

Phylemonhttp://phylemon.bioinfo.cipf.es/cgi-bin/tools.cgi

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PHYLEMON WEB SERVICE

vtrevino@itesm.mxPROGRAMS – WEB SERVICES

http://bioinformatics.ca/links_directory/index.php?search=phylogeny&submit=Search+Directory

vtrevino@itesm.mxPROGRAMS – WEB SERVICES

http://bioinformatics.ca/links_directory/index.php?search=phylogeny&submit=Search+Directory

vtrevino@itesm.mxBOOK

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EXERCISE/HOMEWORK Select a gene Get the sequence in at least 7 species Select a site (Phylemon) Perform the multiple sequence alignment

(ClustalW) Perform Phylogeny to obtain a tree

At least 2 tree methods At least 3 parameter(s) changes Take DNA/Protein

Report results and discussion

12 MSA+Trees

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PAPERS TO REVISE Phylogeny-aware gap placement

prevents errors in sequence alignment and evolutionary analysis – Loytynoja, Goldman, Science 2008

Insertions and deletions treated as different events

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PAPERS PENDING FOR THIS SESSION