"Phylogenetics" is the study or estimation of the evolutionary history that underlies that biological diversity.

The results of phylogenetic analysis are usually presented as a collection of nodes and branches. That is, a tree

In such tree, taxa that are closely related in an evolutionary sense appear close to each other, and taxa that are distantly related are in different (far) branches of the trees

Phylogenetic trees are also important for multiple sequence alignment

Trees may be rooted or unrooted.

Rooted trees reflect the most

basal ancestor of the tree in

question.

Unrooted trees do not imply a

known ancestral root.

There are competing techniques

for rooting a tree; one of the

most common methods is

through the use of an

"outgroup" .

An outgroup is a species that

have unambiguously separated

early from the other species

being considered.

B

Multiple sequence alignment can be viewed as an extension of pairwise sequence alignment, but the complexity of the computation grows exponentially with the number of sequences.

MSA applies both to nucleotide and amino acid sequences

One of the most essential tools in molecular biology that is used since 1987.

MSA can help us to reveal biological facts about proteins, like analysis of the secondary/tertiary structure.

MSA helps us to do a phylogenetic analysis of the sequences so as to construct evolutionary trees.

Exhaustive search: extension of DP to multiple dimensions.

Progressive alignment: compute tree of sequences, based on hierarchical clustering, and then merge closest first, greedily. E.g. ClustalW

Block-based global alignment find highly conserved regions and then grow alignment around these regions. E.g. BLAST

Iterative search: based on genetic algorithm search.

• Local alignments Profile analysis

Block analysis

Patterns searching and/or Statistical methods

VTISCTGSSSNIGAG-NHVKWYQQLPG

VTISCTGTSSNIGS--ITVNWYQQLPG

LRLSCSSSGFIFSS--YAMYWVRQAPG

LSLTCTVSGTSFDD--YYSTWVRQPPG

PEVTCVVVDVSHEDPQVKFNWYVDG--

ATLVCLISDFYPGA--VTVAWKADS--

AALGCLVKDYFPEP--VTVSWNSG---

VSLTCLVKGFYPSD--IAVEWWSNG--

Alignment of 2 sequences is represented as a

2-row matrix

In a similar way, we represent alignment of 3 sequences as a 3-row matrix

A T _ G C G _

A _ C G T _ A

A T C A C _ A

Score: more conserved columns, better alignment

Align 3 sequences: ATGC, AATC,ATGC

0 1 1 2 3 4

0 1 2 3 3 4

A A T -- C

A -- T G C

0 0 1 2 3 4

-- A T G C

• Resulting path in (x,y,z) space:

(0,0,0)(1,1,0)(1,2,1) (2,3,2) (3,3,3) (4,4,4)

x coordinate

y coordinate

z coordinate

In 3-D, 7 edges

in each unit cube

In 2-D, 3 edges

in each unit

square

C(i-1,j-1,k-1) C(i-1,j,k-1)

C(i,j-1,k)

C(i-1,j-1,k)C (i-1,j,k)

C(i,j,k)

C(i,j,k-1)C(i,j-1,k-1)

Enumerate all possibilities and choose the best one

C (i-1,j-1) C (i-1,j)

C (i,j-1)

For three sequences of length n, the run time is proportional to the number of edges in the 3-D grid. i. e 7n .

For a k-way alignment, build a k-dimensional Manhattan graph with n nodes

Most nodes have 2 -1 incoming edges

Runtime: 0(2 n )

Consider 2 protein sequences of 100 amino acids in length.

If it takes 1002 (103) seconds to exhaustively align these sequences, then it will take 104 seconds to align 3 sequences, 105 to align 4 sequences, etc.

It will take ~1021 seconds to align 20 sequences. One year is ~3x107 seconds. The age of the visible universe is ~.4x1018 seconds.

k

k k

k

Greedy method follows the problem solving heuristic of

making the locally optimal choice at each stage of ksequences with the hope of finding a global optimum to

an alignment of of k-1 sequences/profiles.

u1= ACGTACGTACGT…

u2 = TTAATTAATTAA…

u3 = ACTACTACTACT…

…

uk = CCGGCCGGCCGG

u1= ACg/tTACg/tTACg/cT…

u2 = TTAATTAATTAA…

…

uk = CCGGCCGGCCGG…

k

k-1

• Consider these 4 sequences

s1 GATTCA

s2 GTCTGA

s3 GATATT

s4 GTCAGC

• There are = 6 possible alignments2

4

s2 GTCTGA

s4 GTCAGC (score = 2)

s1 GAT-TCA

s2 G-TCTGA (score = 1)

s1 GAT-TCA

s3 GATAT-T (scores3 GAT-ATT

= 1) s4 G-TCAGC

s1 GATTCA--

s4 G—T-CAGC(score = 0)

s2 G-TCTGA

s3 GATAT-T (score = -1)

(score = -1)

Match= +1

Mismatch/gap= -1

s2 and s4 are closest; combine:

s2 GTCTGA

s4 GTCAGCs GTCt/aGa/c2,4(profile)

s1 s3s2,4

GATTCA GATATTGTCt/aGa/c

new set of 3 sequences:

s1

s3

s2,4

GATTCA GATATTGTCt/aGa/c

s1 GATTC- - A

s2,4 G -T -CTGA(score = 0)

s3 GATATT -

s2,4 G -TCTGA(score = -1)

s1 and s2,4 are closest; combine:

s1 GATTC- - A

S2,4 G -T -CTGAS1,2,4 Ga/-Tt/-ct/-g/-A

s3

S1,2,4

GATATTGa/-Tt/-ct/-g/-A

s3 GATAT –T- -

S1,2,4 GAT-TCTGA(score = 1)

S1,2,3,4 GATa/-Tc/-Tg/-a/-

Final Alignment:

Computationally complex

If msa includes matches, mismatches and gaps and alsoaccounts the degree of variation then msa can be appliedto only a few sequences

Difficult to score

Multiple comparison necessary in each column of the msa for a cumulative score

Placement of gaps and scoring of substitution is more difficult

Difficulty increases with diversity

Relatively easy for a set of closely related sequences

Identifying the correct ancestry relationships for a setof distantly related sequences is more challenging

Even difficult if some members are more alike compared to others

EMBL-EBI http://www.ebi.ac.uk/clustalw/

BCM Search Launcher: Multiple Alignment

http://dot.imgen.bcm.tmc.edu:9331/multi-align/multi-align.html

Multiple Sequence Alignment for Proteins (Wash. U. St. Louis) http://www.ibc.wustl.edu/service/msa/

web.warwick.ac.uk/telri/Bioinfo/

http://science.marshall.edu/murraye/

http://www.cs.iastate.edu/~cs544/Lectures/