biological sequence analysis chapter 3 claus lundegaard

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Biological Sequence Analysis Chapter 3 Claus Lundegaard

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Page 1: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Biological Sequence Analysis

Chapter 3

ClausLundegaard

Page 2: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Objectives

• Review sequence alignment• Scoring matrices• Insertion/deletions• Dynamics programming

• Multiple alignments• How it is done?

Page 3: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Protein Families

Organism 1

Organism 2

Enzyme 1

Enzyme 2

Closely related Same Function

MSEKKQPVDLGLLEEDDEFEEFPAEDWAGLDEDEDAHVWEDNWDDDNVEDDFSNQLR::::::.::::::::::::::::::::.:::::::::::::::::::::::::::::MSEKKQTVDLGLLEEDDEFEEFPAEDWTGLDEDEDAHVWEDNWDDDNVEDDFSNQLR

Related Sequences

Protein Family

Page 4: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Homology modeling and the human genome

Page 5: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Alignments

ACDEFGHIKLMNACEDFGHIPLMN

75%ID

ACDEFGHIKLMNACACFGKIKLMN

75%ID

Page 6: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Substitutions

Glutamic acid

Aspartic acidD

E

Page 7: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Substitutions

T Threonine

S Serine

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Substitutions

ThreonineT

W Tryptophane

Page 9: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Deriving Substitution ScoresBLOSUM, Henikoff & Henikoff, 1992

Protein Family

Block A Block B

Page 10: Biological Sequence Analysis Chapter 3 Claus Lundegaard

BLOSUM MatricesHenikoff & Henikoff, 1992

...A...

...A...

...A...

...S...

...A...

...A...

...A...

...A...

...A...

...A...

A 8 AA 1 AS

7 AA

6 AA

5 AA

4 AA

3 AA

2 AA

0 AA

1 AA

1 AS

1 AS

1 AS

1 AS

1 AS

2 SA

0 AS

1 AS

36 9

45

s

w

ws(s-1)/2 = 1x10x9/2 =

f

Page 11: Biological Sequence Analysis Chapter 3 Claus Lundegaard

BLOSUM MatricesHenikoff & Henikoff, 1992

The probability of occurrence of the i’th amino acid in an i, j pair is:

45 pairs = 90 participants in pairs

A’s in pairs: 36x2 + 9x1 = 81AA AS

S’s in pairs: 0x2 + 9x1 = 9

Probability pA for encountering an A: 81/90 = 0.9

Probability pS for encountering an S: 9/90 = 0.1

qAA = 36/45qAA = 9/45pA = 0.8 + 0.2/2 = 0.9

OR

Page 12: Biological Sequence Analysis Chapter 3 Claus Lundegaard

BLOSUM MatricesHenikoff & Henikoff, 1992

Expected probability, e, of occurrence of pairs:

eAA = pApA = 0.9x0.9 = 0.81

eAS = pApS + pSpA = 0.9x0.1 + 0.1x0.9 = 2x(0.9x0.1) = 0.18

eSS = pSpS = 0.1x0.1 = 0.01

Page 13: Biological Sequence Analysis Chapter 3 Claus Lundegaard

BLOSUM MatricesHenikoff & Henikoff, 1992

Odds and logodds:Odd ratio:

logodd, s:

means that the observed frequencies are as expected

means that the observed frequencies are lower than expected

means that the observed frequencies are higher than expected

In the final BLOSUM matrices values are presented in half-bits, i.e., logodds are multiplied with 2 and rounded to nearest integer.

Page 14: Biological Sequence Analysis Chapter 3 Claus Lundegaard

BLOSUM MatricesHenikoff & Henikoff, 1992

• Segment clustering•Sequences with more than X% ID are represented as one average sequence (cluster)

•Sequences are added to the cluster if it has more than X% ID to any of the sequences already in the cluster

•If the clustering level is more than 50% ID, the final Matrix is a BLOSUM50, more than 62% leads to the BLOSUM62 matrix, etc.

•The higher the %ID the more conserved

Page 15: Biological Sequence Analysis Chapter 3 Claus Lundegaard

BLOSUM MatricesHenikoff & Henikoff, 1992

A R N D C Q E G H I L K M F P S T W Y V B Z X *A 4 -1 -2 -2 0 -1 -1 0 -2 -1 -1 -1 -1 -2 -1 1 0 -3 -2 0 -2 -1 0 -4 R -1 5 0 -2 -3 1 0 -2 0 -3 -2 2 -1 -3 -2 -1 -1 -3 -2 -3 -1 0 -1 -4 N -2 0 6 1 -3 0 0 0 1 -3 -3 0 -2 -3 -2 1 0 -4 -2 -3 3 0 -1 -4 D -2 -2 1 6 -3 0 2 -1 -1 -3 -4 -1 -3 -3 -1 0 -1 -4 -3 -3 4 1 -1 -4 C 0 -3 -3 -3 9 -3 -4 -3 -3 -1 -1 -3 -1 -2 -3 -1 -1 -2 -2 -1 -3 -3 -2 -4 Q -1 1 0 0 -3 5 2 -2 0 -3 -2 1 0 -3 -1 0 -1 -2 -1 -2 0 3 -1 -4 E -1 0 0 2 -4 2 5 -2 0 -3 -3 1 -2 -3 -1 0 -1 -3 -2 -2 1 4 -1 -4 G 0 -2 0 -1 -3 -2 -2 6 -2 -4 -4 -2 -3 -3 -2 0 -2 -2 -3 -3 -1 -2 -1 -4 H -2 0 1 -1 -3 0 0 -2 8 -3 -3 -1 -2 -1 -2 -1 -2 -2 2 -3 0 0 -1 -4 I -1 -3 -3 -3 -1 -3 -3 -4 -3 4 2 -3 1 0 -3 -2 -1 -3 -1 3 -3 -3 -1 -4 L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4 -2 2 0 -3 -2 -1 -2 -1 1 -4 -3 -1 -4 K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5 -1 -3 -1 0 -1 -3 -2 -2 0 1 -1 -4 M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5 0 -2 -1 -1 -1 -1 1 -3 -1 -1 -4 F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6 -4 -2 -2 1 3 -1 -3 -3 -1 -4 P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7 -1 -1 -4 -3 -2 -2 -1 -2 -4 S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4 1 -3 -2 -2 0 0 0 -4 T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 -2 -2 0 -1 -1 0 -4 W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11 2 -3 -4 -3 -2 -4 Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7 -1 -3 -2 -1 -4 V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4 -3 -2 -1 -4 B -2 -1 3 4 -3 0 1 -1 0 -3 -4 0 -3 -3 -2 0 -1 -4 -3 -3 4 1 -1 -4 Z -1 0 0 1 -3 3 4 -2 0 -3 -3 1 -1 -3 -1 0 -1 -3 -2 -2 1 4 -1 -4 X 0 -1 -1 -1 -2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -2 0 0 -2 -1 -1 -1 -1 -1 -4 * -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 1

ACDEFGHIKLMNACEDFGHIPLMN

ACDEFGHIKLMNACACFGKIKLMN

4+9-2-4+6+6-1+4+5+4+5+6 = 42

4+9+2+2+6+6+8+4-1+4+5+6 = 55

Page 16: Biological Sequence Analysis Chapter 3 Claus Lundegaard

A

10 20 30 40 50 60 70humanD -----MSEKKQPVDLGLLEEDDEFEEFPAEDWAGLDEDEDAHVWEDNWDDDNVEDDFSNQLRAELEKHGYKMETS ..: . : :.:. : . .. ...:. :::::::::::::::..::::.::::Anophe MSDKENKDKPKLDLGLLEEDDEFEEFPAEDWAGNKEDEEELSVWEDNWDDDNVEDDFNQQLRAQLEKHK------ 10 20 30 40 50 60 B

10 20 30 40 50 60 70humanD ----MSEKKQPVDLGLLEEDDEFEEFPAEDWAGLDEDEDAHVWEDNWDDDNVEDDFSNQLRAELEKHGYKMETS ....:...:::::::::::::::::::::..:::..........::....:..::..........Anophe MSDKENKDKPKLDLGLLEEDDEFEEFPAEDWAGNKEDEEELSVWEDNWDDDNVEDDFNQQLRAQLEKHK----- 10 20 30 40 50 60

Figure 3.3: (A) The human proteasomal subunit aligned to the mosquito homolog using the BLOSUM50 matrix. (B) The human proteasomal subunit aligned to the mosquito homolog using identity scores.

Gaps

10 20 30 40 50 60 70humanD ----MSEKKQPVDLGLLEEDDEFEEFPAEDWAGLDEDEDAH-VWEDNWDDDNVEDDFSNQLRAELEKHGYKMETS ....:...:::::::::::::::::::::..:::... :::::::::::::::..::::.::::Anophe MSDKENKDKPKLDLGLLEEDDEFEEFPAEDWAGNKEDEEELSVWEDNWDDDNVEDDFNQQLRAQLEKHK------ 10 20 30 40 50 60

Page 17: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Gap Penalties

•A gap is a kind like a mismatch but...•Often the gap score (gap penalty) has an even lower value than the lowest mismatch score

•Having only one type of gap penalties is called a linear gap cost

•Biologically gaps are often inserted/deleted as a one or more event

•In most alignment algorithms is two gap penalties.

•One for making the first gap•Another (higher score) for making an additional gap

•Affine gap penalty

Page 18: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Dynamic Programming

The rest of the slides are stolen from Anders Gorm Petersen

Anders G.Pedersen

Page 19: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Alignment depicted as path in matrix

T C G C A

T

C

C

A

T C G C A

T

C

C

A

TCGCATC-CA

TCGCAT-CCA

Page 20: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Position labeled “x”: TC aligned with TC

--TC -TC TCTC-- T-C TC

Alignment depicted as path in matrix

T C G C A

T

C

C

A

x

Meaning of point in matrix: all residues up to this point have been aligned (but there are many different possible paths).

Page 21: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Dynamic programming: computation of scores

T C G C A

T

C

C

A

x

Any given point in matrix can only be reached from three possible positions (you cannot “align backwards”).

=> Best scoring alignment ending in any given point in the matrix can be found by choosing the highest scoring of the three possibilities.

Page 22: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Dynamic programming: computation of scores

T C G C A

T

C

C

A

x

Any given point in matrix can only be reached from three possible positions (you cannot “align backwards”).

=> Best scoring alignment ending in any given point in the matrix can be found by choosing the highest scoring of the three possibilities.

score(x,y) = max

score(x,y-1) - gap-penalty

Page 23: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Dynamic programming: computation of scores

T C G C A

T

C

C

A

x

Any given point in matrix can only be reached from three possible positions (you cannot “align backwards”).

=> Best scoring alignment ending in any given point in the matrix can be found by choosing the highest scoring of the three possibilities.

score(x,y) = max

score(x,y-1) - gap-penalty

score(x-1,y-1) + substitution-score(x,y)

Page 24: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Dynamic programming: computation of scores

T C G C A

T

C

C

A

x

Any given point in matrix can only be reached from three possible positions (you cannot “align backwards”).

=> Best scoring alignment ending in any given point in the matrix can be found by choosing the highest scoring of the three possibilities.

score(x,y) = max

score(x,y-1) - gap-penalty

score(x-1,y-1) + substitution-score(x,y)

score(x-1,y) - gap-penalty

Page 25: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Dynamic programming: computation of scores

T C G C A

T

C

C

A

x

Any given point in matrix can only be reached from three possible positions (you cannot “align backwards”).

=> Best scoring alignment ending in any given point in the matrix can be found by choosing the highest scoring of the three possibilities.

Each new score is found by choosing the maximum of three possibilities. For each square in matrix: keep track of where best score came from.

Fill in scores one row at a time, starting in upper left corner of matrix, ending in lower right corner.

score(x,y) = max

score(x,y-1) - gap-penalty

score(x-1,y-1) + substitution-score(x,y)

score(x-1,y) - gap-penalty

Page 26: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Dynamic programming: example

A C G T

A 1 -1 -1 -1

C -1 1 -1 -1

G -1 -1 1 -1

T -1 -1 -1 1

Gaps: -2

Page 27: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Dynamic programming: example

Page 28: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Dynamic programming: example

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Dynamic programming: example

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Dynamic programming: example

T C G C A: : : :T C - C A1+1-2+1+1 = 2

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Global versus local alignments

Global alignment: align full length of both sequences. (The “Needleman-Wunsch” algorithm).

Local alignment: find best partial alignment of two sequences (the “Smith-Waterman” algorithm).

Global alignment

Seq 1

Seq 2

Local alignment

Page 32: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Local alignment overview

• The recursive formula is changed by adding a fourth possibility: zero. This means local alignment scores are never negative.

• Trace-back is started at the highest value rather than in lower right corner

• Trace-back is stopped as soon as a zero is encountered

score(x,y) = max

score(x,y-1) - gap-penalty

score(x-1,y-1) + substitution-score(x,y)

score(x-1,y) - gap-penalty

0

Page 33: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Local alignment: example

Page 34: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Substitution matrices and sequence similarity

• Substitution matrices come as series of matrices calculated for different degrees of sequence similarity (different evolutionary distances).

• ”Hard” matrices are designed for similar sequences

– Hard matrices a designated by high numbers in the BLOSUM series (e.g., BLOSUM80)

– Hard matrices yield short, highly conserved alignments

• ”Soft” matrices are designed for less similar sequences

– Soft matrices have low BLOSUM values (45)

– Soft matrices yield longer, less well conserved alignments

Page 35: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Alignments: things to keep in mind

“Optimal alignment” means “having the highest possible score, given substitution matrix and set of gap penalties”.

This is NOT necessarily the biologically most meaningful alignment.

Specifically, the underlying assumptions are often wrong: substitutions are not equally frequent at all positions, affine gap penalties do not model insertion/deletion well, etc.

Pairwise alignment programs always produce an alignment - even when it does not make sense to align sequences.

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Database searching

Using pairwise alignments to search

databases for similar sequences

Database

Query sequence

Page 37: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Database searching

Most common use of pairwise sequence alignments is to search databases for related sequences. For instance: find probable function of newly isolated protein by identifying similar proteins with known function.

Most often, local alignment ( “Smith-Waterman”) is used for database searching: you are interested in finding out if ANY domain in your protein looks like something that is known.

Often, full Smith-Waterman is too time-consuming for searching large databases, so heuristic methods are used (fasta, BLAST).

Page 38: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Database searching: heuristic search algorithms

FASTA (Pearson 1995)

Uses heuristics to avoid calculating the full dynamic programming matrix

Speed up searches by an order of magnitude compared to full Smith-Waterman

The statistical side of FASTA is still stronger than BLAST

BLAST (Altschul 1990, 1997)

Uses rapid word lookup methods to completely skip most of the database entries

Extremely fast

One order of magnitude faster than FASTA

Two orders of magnitude faster than Smith-Waterman

Almost as sensitive as FASTA

Page 39: Biological Sequence Analysis Chapter 3 Claus Lundegaard

BLAST flavors

BLASTN

Nucleotide query sequence

Nucleotide database

BLASTP

Protein query sequence

Protein database

BLASTX

Nucleotide query sequence

Protein database

Compares all six reading frames with the database

TBLASTN

Protein query sequence

Nucleotide database

”On the fly” six frame translation of database

TBLASTX

Nucleotide query sequence

Nucleotide database

Compares all reading frames of query with all reading frames of the database

Page 40: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Searching on the web: BLAST at NCBI

Very fast computer dedicated to running BLAST searches

Many databases that are always up to date

Nice simple web interface

But you still need knowledge about BLAST to use it properly

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When is a database hit significant?

• Problem:

– Even unrelated sequences can be aligned (yielding a low score)

– How do we know if a database hit is meaningful?

– When is an alignment score sufficiently high?

• Solution:

– Determine the range of alignment scores you would expect to get for random reasons (i.e., when aligning unrelated sequences).

– Compare actual scores to the distribution of random scores.

– Is the real score much higher than you’d expect by chance?

Page 42: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Random alignment scores follow extreme value distributions

The exact shape and location of the distribution depends on the exact nature of the database and the query sequence

Searching a database of unrelated sequences result in scores following an extreme value distribution

Page 43: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Significance of a hit: one possible solution

(1) Align query sequence to all sequences in database, note scores

(2) Fit actual scores to a mixture of two sub-distributions: (a) an extreme value distribution and (b) a normal distribution

(3) Use fitted extreme-value distribution to predict how many random hits to expect for any given score (the “E-value”)

Page 44: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Significance of a hit: example

Search against a database of 10,000 sequences.

An extreme-value distribution (blue) is fitted to the distribution of all scores.

It is found that 99.9% of the blue distribution has a score below 112.

This means that when searching a database of 10,000 sequences you’d expect to get 0.1% * 10,000 = 10 hits with a score of 112 or better for random reasons

10 is the E-value of a hit with score 112. You want E-values well below 1!

Page 45: Biological Sequence Analysis Chapter 3 Claus Lundegaard

Database searching: E-values in BLAST

BLAST uses precomputed extreme value distributions to calculate E-values from alignment scores

For this reason BLAST only allows certain combinations of substitution matrices and gap penalties

This also means that the fit is based on a different data set than the one you are working on

A word of caution: BLAST tends to overestimate the significance of its matches

E-values from BLAST are fine for identifying sure hitsOne should be careful using BLAST’s E-values to judge if a marginal hit can be trusted (e.g., you may want to use E-values of 10-4 to 10-5).

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Refresher: pairwise alignments

• Most used substitution matrices are themselves Most used substitution matrices are themselves derived empirically from simple multiple alignmentsderived empirically from simple multiple alignments

Multiple alignment

A/A 2.15%A/C 0.03%A/D 0.07%...

Calculatesubstitutionfrequencies

Score(A/C) = log

Freq(A/C),observedFreq(A/C),expected

Convertto scores

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Multiple alignment

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Multiple alignments: what use are they?

• Starting point for studies of Starting point for studies of molecular evolutionmolecular evolution

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Multiple alignments: what use are they?

• Characterization of protein families (sequence profiles):Characterization of protein families (sequence profiles):– Identification of conserved (functionally important) sequence Identification of conserved (functionally important) sequence

regionsregions– Prediction of structural features (disulfide bonds, amphipathic Prediction of structural features (disulfide bonds, amphipathic

alpha-helices, surface loops, etc.)alpha-helices, surface loops, etc.)

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Scoring a multiple alignment:the “sum of pairs” score

...A...

...A...

...S...

...T...

One column from alignment

AA: 4, AS: 1, AT:0AS: 1, AT: 0ST: 1

Score: 4+1+0+1+0+1 = 7

⇒In theory, it is possible to define an alignment score for multiple alignments (there are many alternative scoring systems)

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Multiple alignment: dynamic programming is only feasible for very

small data sets • In theory, optimal multiple In theory, optimal multiple

alignment can be found by dynamic alignment can be found by dynamic programming using a matrix with programming using a matrix with more dimensions (one dimension more dimensions (one dimension per sequence)per sequence)

• BUT even with dynamic BUT even with dynamic programming finding the optimal programming finding the optimal alignment very quickly becomes alignment very quickly becomes impossible due to the astronomical impossible due to the astronomical number of computationsnumber of computations

• Full dynamic programming only Full dynamic programming only possible for up to about 4-5 protein possible for up to about 4-5 protein sequences of average length sequences of average length

• Even with heuristics, not feasible for Even with heuristics, not feasible for more than 7-8 protein sequencesmore than 7-8 protein sequences

• Never used in practiceNever used in practice

Dynamic programming matrix for 3 sequences

For 3 sequences, optimal path must comefrom one of 7 previous points

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Multiple alignment: an approximate solution

• Progressive alignment (ClustalX and other Progressive alignment (ClustalX and other programs):programs):

3.3. Perform all Perform all pairwisepairwise alignments; keep track of sequence alignments; keep track of sequence similarities between all pairs of sequences (construct similarities between all pairs of sequences (construct “distance matrix”)“distance matrix”)

5.5. Align the most similar pair of sequencesAlign the most similar pair of sequences

7.7. Progressively add sequences to the (constantly growing) Progressively add sequences to the (constantly growing) multiple alignment in order of decreasing similaritymultiple alignment in order of decreasing similarity..

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Progressive alignment: details

1) Perform all pairwise alignments, note pairwise distances (construct “distance matrix”)

2) Construct pseudo-phylogenetic tree from pairwise distances

S1S2S3S4

6 pairwisealignments

S1 S2 S3 S4S1S2 3S3 1 3S4 3 2 3

S1 S3 S4 S2

S1 S2 S3 S4S1S2 3S3 1 3S4 3 2 3

“Guide tree”

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Progressive alignment: details

3) Use tree as guide for multiple alignment:a) Align most similar pair of sequences using dynamic

programming

b) Align next most similar pair

c) Align alignments using dynamic programming - preserve gaps

S1 S3 S4 S2

S1

S3

S2

S4

S1

S3

S2

S4New gap to optimize alignmentof (S2,S4) with (S1,S3)

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Aligning profiles

S1

S3

S2

S4

+

S1

S3

S2

S4New gap to optimize alignmentof (S2,S4) with (S1,S3)

Aligning alignments: each alignment treated as a single sequence (a profile)

Full dynamic programmingon two profiles

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Scoring profile alignments

...A...

...S...

...S...

...T...

+

One column from alignment

AS: 1, AT:0

SS: 4, ST:1

Score: 1+0+4+1 = 1.54

Compare each residue in one profile to all residues in second profile. Score is average of all comparisons.

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Additional ClustalX heuristics

• Sequence weighting:Sequence weighting:– scores from similar groups of sequences are down-weightedscores from similar groups of sequences are down-weighted

• Variable substitution matrices:Variable substitution matrices:– during alignment ClustalX uses different substitution matrices during alignment ClustalX uses different substitution matrices

depending on how similar the sequences/profiles aredepending on how similar the sequences/profiles are

• Variable gap penalties:Variable gap penalties: gap penalties depend on substitution matrixgap penalties depend on substitution matrix gap penalties depend on similarity of sequencesgap penalties depend on similarity of sequences reduced gap penalties at existing gapsreduced gap penalties at existing gaps increased gap penalties CLOSE to existing gapsincreased gap penalties CLOSE to existing gaps reduced gap penalties in hydrophilic stretches (presumed reduced gap penalties in hydrophilic stretches (presumed

surface loop)surface loop) residue-specific gap penaltiesresidue-specific gap penalties and more...and more...

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Other multiple alignment programs

ClustalW / ClustalX

pileup

multalign

multal

saga

hmmt

DIALIGN

SBpima

MLpima

T-Coffee

...

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Other multiple alignment programs

ClustalW / ClustalX

pileup

multalign

multal

saga

hmmt

DIALIGN

SBpima

MLpima

T-Coffee

...

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Global methods (e.g., ClustalX) get into trouble when data is not globally

related!!!

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Global methods (e.g., ClustalX) get into trouble when data is not globally

related!!!

Clustalx

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Global methods (e.g., ClustalX) get into trouble when data is not globally

related!!!

Clustalx

Possible solutions:(1) Cut out conserved regions of interest and THEN align

them (2) Use method that deals with local similarity (e.g. DIALIGN)

Page 63: Biological Sequence Analysis Chapter 3 Claus Lundegaard

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•Real life example with ClustalWReal life example with ClustalW

Page 64: Biological Sequence Analysis Chapter 3 Claus Lundegaard

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