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# Practical multiple sequence algorithms - PowerPoint PPT Presentation

Practical multiple sequence algorithms. Sushmita Roy BMI/CS 576 www.biostat.wisc.edu/bmi576/ Sushmita Roy [email protected] Sep 24th, 2013. Goals for today. Review Guide-tree based m ultiple sequence alignment

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### Practical multiple sequence algorithms

Sushmita Roy

BMI/CS 576

www.biostat.wisc.edu/bmi576/

Sushmita Roy

[email protected]

Sep 24th, 2013

• Review Guide-tree based multiple sequence alignment

• Two practical implementations of algorithms for multiple sequence alignment

• CLUSTALW

• MUSCLE

• Greedy

• The tree might not be correct, that is, reflect an incorrect ordering of how sequences should be joined

• Errors in alignment

• Even if the tree is correct, there might be some positions that are misaligned.

• Choice of alignment parameters

• Especially when the sequences are diverged and there are more mismatches than identities

• For closely related sequences, identities dominate over mismatches

• Different weight matrices might be optimal for different evolutionary distances.

• Gaps do not occur randomly

• Gaps more likely to occur between “secondary structures” rather than within them.

• A progressive alignment algorithm with several heuristics

• Based on a guide tree approach

• Dynamically varies the gap penalties in a position and residue specific manner

• Weight different sequences differently

Thompson et al, 1994

• Build up a multiple sequence alignment by progressively adding new sequences by following the order of a phylogenetic tree.

• Needs sequences to have different extents of divergence

• Gaps inserted in the earlier alignments should be preserved as these gaps are most reliable.

Steps in ClustalW

• Align all pairs of sequences separately to create a pairwise distance matrix.

• Calculate a guide tree from the matrix

• Align sequences progressively according to guide tree starting from the leaves

• For two sequences with the following alignment

• AATAATAATAA_TA

• Similarity S

• No. of identical bases/size of alignment

• 4/7 for the above example

• Distance=1-S

• Consider four sequences

• AAAC

• AGC

• ACC

• GAC

• Generate pairwise alignments for all pairs of sequences

Sequence pair

Alignment

% similarity

Distance

1. and 2.

AAAC_AGC

2/4

0.5

1.

2.

3.

4.

1. and 3.

AAAC_ACC

2/4

0.5

1.

2.

1. and 4.

AAAC_GAC

2/4

0.5

3.

4.

2. and 3.

AGCACC

2/3

0.33

2. and 4.

AGCGAC

1/3

0.67

3. and 4.

ACCGAC

1/3

0.67

• UPGMA: Unweighted pair group method using arithmetic averages

• Represent all sequences as the leaf nodes of a tree

• Merge two closest nodes at a time to create a new node in the tree

• Set new node at height determined by nodes being merged

• Let i and j be two existing nodes that are merged to create a new node

• Distance between a new node kcreated from two existing nodes iand j and other nodes l

Distance between node k and l

Number of elements in cluster

associated with node j

1

2

3

4

1

Place new node at height d23/2

2

3

5

d23/2=1/6

4

1

2

3

4

1

4

5

1

4

5

1

4

5

1

d14/2=0.25

4

6

5

1/6

5

2

1

4

3

6

5

d56/2=0.29

7

d14/2=0.25

6

5

1/6

2

1

4

3

• Assume we have two alignments corresponding to intermediate nodes of the guide tree

• At each step we maximize over score from

• aligning column i in A1 to a column j in A2

• aligning column i in A1 to gaps in A2

• aligning column j in A2 to gaps in A1

• ClustalW uses an average of all pairwise comparisons between two alignments

Alignment A1

Alignment A2

AAAC_GAC

AGCACC

ClustalW scores for aligning columns from two alignments

Assume a score of 1 for mismatch, 2 for match and 0 for gap

Score of aligning column 3 from Alignment 1 and column 2 from alignment 2

AAAC_GAC

Alignment 1

Alignment 2

AGCACC

A A A C_ G A C

A G CA C C

Max of three options

A

_

A

_

_

_

Alignment 1

A A

_ _

A A

Alignment 2

Assigning sequence weights in ClustalW

• ClustalW also considers different weights for different sequences

• Closely related sequences need to be down-weighted

• Divergent sequences are up-weighted

• Uses the branch length of the tree to calculate weights

ClustalW weights of sequences

Weight of a sequence: sum of branch lengths from root to leaf, but sequences sharing a branch share the weight

For example, weight for Hbb_Human=0.081+(0.226/2)+(0.061/4)+(0.015/5)+(0.062/6)

ClustalW score computation

ClustalW gap handling rules

• Gap penalties are dynamically adjusted

• For each position in the alignment compute a possible gap penalty value

• If there is a gap in any of the sequences being aligned reduce its penalty

• If there is no gap, and this position is <8 positions from another gap, increase the gap open penalty

• Reduce gap penalty for positions inside a hydrophilic stretch of 5 residues

• Otherwise use the gap penalty associated with residue-specific gap penalties estimated based on the known alignments

• different amino acid substitution matrices depending upon the estimated divergence of sequences being aligned at a particular stage may be selected.

High gap penalty within 8 positions of existing gaps

Hydrophilic stretches

Existing gap

Higgins et al, methods in Enzymology, 1996

• Dynamically switch between matrices depending upon the average similarity between sequences being aligned

• PAM

• 80-100%: PAM20

• 60-80%: PAM60

• 40-60%: PAM120

• 0-40%: PAM350

• BLOSUM

• 80-100%: BLOSUM80

• 60-80%: BLOSUM62

• 30-60%: BLOSUM45

• 0-30%: BLOSUM30

• Applying ClustalW to SH3 domain proteins

Proteins share <12% sequence identity

Alignment blocks correspond to beta strand secondary structures

Summary of ClustalW

• Guide tree method

• Complex gap penalty rules

• Sequences are weighted to reduce the importance of very similar sequences

• Progressive + iterative

• Has three main stages

• Stage1: Draft Progressive

• Stage 2: Improved Progressive

• Stage 3: Refinement:

• Select pairs of subtrees and re-align the alignment for the subtrees.

• Keep if it improves alignment

Stage 1: Draft progressive

Stage 2: Improved progressive

Stage 3: Refinement

1.1 Compute k-mer distance matrix

1.2 Use UPGMA to make tree (TREE1)

1.3. Use guide tree to make first MSA

K-mer distance

Let k=2

• K-mer distance is defined from common fractional k-mer count (F)

• D=1-F

# of instances in sequence 1

# of instances in sequence 1

A k-mer

Length of sequences

K-mer distance example

2.1 Recomputesimilarity of sequences of pairs using mutual alignment in MSA

2.2 Construct a phylogenetic tree (TREE2) using an alignment-based distance

2.3 Build a new progressive alignment only for subtrees where branching order has changed between TREE1 and TREE2

2.4 Repeat 2.3 until number of “reordered nodes” does not decrease.

Stage 2.1. Recomputing pairwise sequence similarity from a multiple alignment

Derived pairwise alignment

Fraction identity

TGTTAAC

TGT-AAC

6/7

An MSA

Exclude gaps in both sequences

-TGTTAAC

-TGT-AAC

-TGT--AC

ATGT---C

ATGT-GGC

TGTTAAC

TGT--AC

5/7

-TGTTAAC

ATGT---C

4/8

-TGTTAAC

ATGT-GGC

4/8

Stage 2.2: Phylogenetic tree creation

Construct a phylogenetic tree using a Kimura distance

D: fractional identity of sequences

Stage 2.3 Re-align only when branching order is changed

Recompute alignment for these nodes

Branching order same

Branching order different:

x branches before v

Stage 3: Iterative Refinement

3.1 Select a branch

3.2 Extract profiles

3.3 Re-align profiles

3.4 Update MSA if its score is better than current MSA

• Select a branch in order of decreasing distance from the root

1

MQTIF

MQTIF

LH-IW

5

2

LHIW

MQTIF

LH-IW

LQS-W

LQSW

6

3

LQSW

L-SW

4

LSF

Branch selection order: 1,2,3,4,5,6

1

MQTIF

Re-align profiles for subtrees

MQTIF

LH-IW

MQTIF

5

2

LHI-W

MQTIF

LQS-WL-S-W

LHIW

MQTIF

LH-IW

LQS-WL-S-W

Delete branch 1

LH-IW

LQS-WL-S-W

LQSW

6

3

LQSW

L-SW

Is score better?

4

LSF

yes

Keep new alignment

1

LHIW

Re-align profiles for subtrees

MQTIF

MQTIF

LH-IW

5

2

LHI-W

MQTIF

LQS-WL-S-W

LHIW

MQTIF

LH-IW

LQS-WL-S-W

MQTIF

LQS-WL-S-W

Delete branch 2

LQSW

6

3

Is score better?

LQSW

L-SW

4

yes

LSF

Keep new alignment

• Three stage algorithm

• Stage 1: Draft progressive

• k-mer distance

• UPGMA tree (TREE1)

• Guide tree based alignment (MSA1)

• Stage 2: Improved progressive

• Distance derived from MSA1

• UPGMA tree (TREE2)

• Redo alignment for nodes with changed orderings

• Repeat until number of re-ordered nodes does not change

• Stage 3: Iterative refinement

• Generate subtree profiles

• Realign profiles

• Keep realignment if of higher score

• Repeat until no more improvement or fixed number of steps.

• MUSCLE-fast: Stage 1

• MUSCLE-p: Stage1 and 2

Accuracy measures the fraction of residues correctly aligned with the reference alignment

Edgar, 2004, BMC Bioinformatics

Summary of algorithms datasets

• ClustalW

• Lots of heuristics for gaps

• One guide tree and then alignment

• Weights sequences

• Dynamically selects scoring matrix depending upon sequence identity

• MUSCLE

• Three-stage algorithm: Draft, Improved, Iterative refinement

• Two guide trees

• Uses k-mer distance for first tree

• Selectively re-aligns using second tree

• Refines iteratively by working on subtree-associated alignments

• Fast and has as good or better quality alignments

• Consider coding sequences of 15 yeast species

• Consider promoter sequences of 15 yeast species

• Align with MUSCLE and CLUSTALW

Protein sequence alignment datasets

MUSCLE

CLUSTALW

Promoter sequence alignment datasets

MUSCLE

CLUSTALW

Original sequences

Shuffled sequences

Comparing alignment of promoters to shuffled sequences in CLUSTALW MUSCLE

Original sequences

Shuffled sequences

Conclusion CLUSTALW

• Algorithms seemed similar for protein/coding sequences

• Algorithms gave different alignments for DNA sequence

• Possibly DNA sequence is harder to align

• DNA sequence in non-coding regions are even harder to align

• Pairwise alignment

• Global: (Needleman-Wunsch)

• Local: (Smith-Waterman)

• Database searching

• BLAST

• Multiple sequence alignment

• Star alignment

• Progressive alignment with guide tree: CLUSTALW

• Progressive + Iterative alignment with guide tree: MUSCLE