Recent Progress in Multiple Sequence Alignments: A Survey

Cédric Notredame (21/04/23)

Recent Progress in Multiple Sequence

Alignments:A Survey

Cédric Notredame


Our Scope

What are The existing Methods?

How Do They Work: -Assemby Algorithms-Weighting Schemes.

When Do They Work ?

Which Future?


Outline

-Introduction

-A taxonomy of the existing Packages

-A few algorithms…

-Performance Comparison using BaliBase


Introduction


What Is A Multiple Sequence Alignment?

A MSA is a MODEL

It Indicates the RELATIONSHIP between residues of different sequences.

It REVEALS-Similarities-Inconsistencies

LIKE ANYMODEL


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How Can I Use A Multiple Sequence Alignment?

Extrapolation

Motifs/Patterns

Phylogeny

Profiles

Struc. Prediction

Multiple Alignments Are CENTRAL to MOST Bioinformatics Techniques.


How Can I Use A Multiple Sequence Alignment?

Multiple Alignments Is the most INTEGRATIVE Method Available Today.

We Need MSA to INCORPORATE existing DATA


Why Is It Difficult To Compute A multiple Sequence Alignment?

A CROSSROAD PROBLEM

BIOLOGY:What is A Good Alignment

COMPUTATIONWhat is THE Good Alignment

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Why Is It Difficult To Compute A multiple Sequence Alignment ?

BIOLOGY

CIRCULAR PROBLEM....

GoodSequences

GoodAlignment

COMPUTATION


A Taxonomy of Multiple Sequence Alignment Methods


Grouping According to the assembly Algorithm


SimultaneousAs opposed to Progressive

Exact As opposed to Heursistic

Stochastic As opposed to Determinist

Iterative As opposed to Non Iterative

[Simultaneous: they simultaneously use all the information]

[Heuristics: cut corners like Blast Vs SW]

[Heuristics: do not guarranty an optimal solution]

[Stochastic: contain an element of randomness]

[Stochastic: Example of a Monte Carlo Surface estimation ]

[Iterative: Most stochastic methods are iterative]

[Iterative: run the same algorithm many times]

Cédric Notredame (21/04/23)Iterative

Iteralign

Prrp

SAM HMMer

SAGAGA

Clustal

Dialign

T-Coffee

ProgressiveSimultaneous

MSA

POA OMA

PralineMAFFT

DCA

Combalign

Non tree based

GAs

HMMs

Cédric Notredame (21/04/23)Iterative

Iteralign

Prrp

SAM HMMer

GA

Clustal

Dialign

T-Coffee

ProgressiveSimultaneous

MSA

POA OMA

PralineMAFFT

DCA

Combalign

StochasticSAGA


NEARLY EVERY OPTIMISATIONALGORITHM

HAS BEEN APPLIED TO THEMSA PROBLEM!!!


Grouping According to the Objective Function


Scoring an Alignment: Evolutionary based

methods

BIOLOGYHow many events separate my sequences?

Such an evaluation relies on a biological model.

COMPUTATIONEvery position musd be independant


REAL Tree

Model: ALL the sequences evolved from the same ancestor

A

A

A C

Tree: Cost=1C

AAACC

A CA

PROBLEM: We do not know the true tree


STAR Tree

Model: ALL the sequences have the same ancestor

A

A

A CStar Tree: Cost=2

C

AAACC

A

PROBLEM: the tree star is phylogenetically wrong


Sums of Pairs

Model=Every sequence is the ancestor of every sequence

A

A

A CSums of Pairs: Cost=6

CAAACC

PROBLEM: -over-estimation of the mutation costs-Requires a weighting scheme

lk

li

kii mmsmS ,

[s(a,b): matrix]

[i: column i]

[k, l: seq index]


Sums of Pairs: Some of itslimitations (Durbin,

p140)

LLLLL

GCost=5*N*(N-1)/2-(5)*(N-1) - (-4)*(N-1)

[glycine effect]

Cost=5*N*(N-1)/2-(9)*(N-1)

Cost= 5*N*(N-1)/2[5: Leucine Vs Leucine with Blosum50]


Sums of Pairs: Some of its limitations (Durbin,

p140)

LLLLL

G

Delta=2*(9)*(N-1)

5*N*(N-1)=

(9)

5*N

N

Delta

Conclusion: The more Leucine, the less expensive it gets to add a Glycin to the column...


Enthropy based Functions

Model: Minimize the enthropy (variety) in each Column

AAACC

PROBLEM: -requires a simultaneous alignment-assumes independant sequences

j

jiia amc [number of Alanine (a) in column i]

a

iaiai PcmS log* [Score of column i][a: alphabet]

[P can incorporate pseudocounts]

S=0 if the column is conserved


Consistency based Functions

Model: Maximise the consistency (agreement) with a list of constraints (alignments)

PROBLEM: -requires a list of constraints

AAACC

lk

li

kii mmS , [kand l are sequences, i is a column]

Existsmmmm li

ki

li

ki ,1,

[the two residues are found aligned in the list of constraints]


Concistency Based

Iteralign

Dialign

T-Coffee

Praline

Combalign

Prrp

ClustalPOA

MSA

MAFFTOMA

DCA

SAGA

WeightedSums

of Pairs

EnthropySAM HMMer

GIBBS


A few Multiple Sequence Alignment Algorithms


A Few Algorithms

MSA and DCA

ClustalW

Dialign IIPrrp

SAGA

GIBBS Sampler

MAFFT

POA


Simultaneous: MSA and DCA


Simultaneous Alignments : MSA

1) Set Bounds on each pair of sequences (Carillo and Lipman)

2) Compute the Maln within the Hyperspace

-Few Small Closely Related Sequence.

-Do Well When They Can Run.

-Memory and CPU hungry


MSA: the carillo and Lipman bounds

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chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKDtrybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP

S( )=

S(S(

)

)+

…[Pairwise projection of sequences k and l]



a(k,l)=score of the projection k l in the optimal MSA

â(k,l)=score of the optimal alignment of k l

(a(x,y))=score of the complete multiple alignment

a(k,l) â(k,l) a(k,m) â(k,m)

?

Upper

Lower



LM: a lower bound for the complete MSA

a(k,l)>=LM +â(k,l)-(â(x,y))

LM<=(â(x,y)) - (â(k,l)-a(k,l))

a(k,l) â(k,l)

â(k,l)

LM+ â(k,l)-(â(x,y))

?



LM: can be measured on ANY heuristic alignment

a(k,l) â(k,l)

â(k,l)

LM+ â(k,l)-(â(x,y)) ä(k,l)

LM = (ä(x,y))

The better LM, the tighter the bounds…



backward Forward

Best( M-i, N-j) Best( 0-i, 0-j)

0

M

N 0

M

N

+


Simultaneous Alignments : MSA

1) Set Bounds on each pair of sequences (Carillo and Lipman)

2) Compute the Maln within the Hyperspace



-Memory and CPU hungry


Simultaneous Alignments : DCA

-Few Small Closely Related Sequence, but less limited than MSA

-Do Well When Can Run.

-Memory and CPU hungry, but less than MSA


Simultaneous With a New Sequence Representaion:

POA-Partial Ordered Graph




POA

POA makes it possible to represent complex relationships:

-domain deletion-domain inversions


Progressive: ClustalW


Progressive Alignment: ClustalW

Feng and Dolittle, 1988; Taylor 198ç

Clustering


Dynamic Programming Using A Substitution Matrix

Progressive Alignment: ClustalW


Tree based Alignment : Recursive Algorithm

Align ( Node N){

if ( N->left_child is a Node)A1=Align ( N->left_child)

else if ( N->left_child is a Sequence)A1=N->left_child

if (N->right_child is a node)A2=Align (N->right_child)

else if ( N->right_child is a Sequence)A2=N->right_child

Return dp_alignment (A1, A2)}

A D E F GCB


Progressive Alignment : ClustalW

-Depends on the ORDER of the sequences (Tree).

-Depends on the CHOICE of the sequences.

-Depends on the PARAMETERS:

•Substitution Matrix.

•Penalties (Gop, Gep).

•Sequence Weight.

•Tree making Algorithm.


Weighting Within ClustalWProgressive Alignment : ClustalW Weighting


Position Specific GOPProgressive Alignment : ClustalW GOP


ClustalW is the most Popular Method

-Fast

-Greedy Heuristic (No Guarranty).

Progressive Alignment : ClustalW

-Scales Well: N, N L3 2 2


Progressive Alignment With a Heuristic DP:

MAFFT



ProgressiveAnd

Concistency BasedDialign II


Dialign II

1) Identify best chain of segments on each pair of sequence. Assign a Pvalue to each Segment Pair.

3) Assemble the alignment according to the segment pairs.

2) Ré-évaluate each segment pair according to its consistency with the others


Dialign II

-May Align Too Few Residues

-No Gap Penalty-Does well with ESTs


ProgressiveAnd

Concistency BasedT-COFFEE


Mixing Local and Global Alignments

Local Alignment Global Alignment

Extension

Multiple Sequence Alignment


What is a library?

Extension+T-Coffee

2Seq1 MySeqSeq2 MyotherSeq#1 21 1 253 8 70….

3Seq1 anotherseqSeq2 atsecondoneSeq3 athirdone#1 21 1 25#1 33 8 70….


Iterative


7.16.1 ProgressiveIterative Methods

-HMMs, HMMER, SAM.

-Slow, Sometimes Inaccurate-Good Profile Generators


7.16.2 PrrpInitial Alignment

Tree and weights computation

Weights converged End

Realign two sub-groups

Alignment converged

YES

NO

YES NO

Inner Iteration

Outer Iteration

Iterative Methods : Prrp


Iterative Sochastic:SAGA, The Genetic

Algorithm





Automatic scheduling of the operators



Weighting Schemes


The Problem

The sequences Contain Correlated Information

Most scoring Schemes Ignore this Correlation


Weighting Sequence Pairs with a Tree:

Carillo and LipmanRationale I


A D E F GCB

E=EDGE

P=Evolutive Path from A to X

E must contribute the same weight to every path P that goes throught it.

QUESTION: Which Weight for a Pair of Sequences

All the weights using E must sum to 1: (WP,E)=1.

Wp=Nk-1)

1

Nk: Number of Edges meeting on Node k.


USAGE

]][[*),( yB

xAAB

yB

xA RRMatWRRScore


PROBLEM: Weight Depends only on the Tree topology

B

A C

AB: 0.5AC: 0.5BC: 0.5.

B

A C

AB: 0.5AC: 0.5BC: 0.5.


Weighting Sequences with a Tree

Clustal WWeights


GA D E FCB

QUESTION: Which Weight for Sequences ?

W=Length *1/4

W=Length *1/2

W=Length *1

GG W=W)

Number Sequences Sharing Edge

Edge LengthWseq =


USAGE

]][[**),( yB

xABA

yB

xA RRMatWWRRScore


PROBLEM: Overweight of distant sequences

D E F G

C-C Will dominate the Alignment

-C Will be very Difficult to align


Performance Comparison Using

Collections of Reference

Alignments: BaliBase and

Ribosomal RNA


What Is BaliBaseBaliBase

BaliBase is a collection of reference Multiple Alignments

The Structure of the Sequences are known and were used to assemble the MALN.

Evaluation is carried out by Comparing the Structure Based Reference Alignment With its Sequence Based Counterpart



DALI, Sap …

Method X

Comparison



DescriptionPROBLEM

Source: BaliBase, Thompson et al, NAR, 1999,

Even Phylogenic Spread.

One Outlayer Sequence

Two Distantly related Groups

Long Internal Indel

Long Terminal Indel


Choosing The Right Method


Choosing The Right Method (POA Evaluation)


Choosing The Right Method (POA Evaluation)


Choosing The Right Method (MAFFT evaluation)






Conclusion


What Is BaliBaseWhich Method ?

PROBLEM

Source: BaliBase, Thompson et al, NAR, 1999,

Strategy

Strategy

ClustalW, T-coffee,MSA, DCA

PrrP,T-Coffee

Dialign

T-Coffee

T-Coffee

Dialign

T-Coffee


Methods /Situtations

1-Carillo and Lipman:-MSA, DCA.


2-Segment Based:-DIALIGN, MACAW.

-May Align Too Few Residues-Good For Long Indels


3-Iterative:-HMMs, HMMER, SAM.

-Slow, Sometimes Inaccurate-Good Profile Generators

4-Progressive: -ClustalW, Pileup, Multalign…-Fast and Sensitive


Addresses

MAFFT Progressive www.biophys.kyoto-u.jp/katoh POA Progressive/Simulataneous www.bioinformatics.ucla.edu/poa MUSCLE Progressive/Iterative www.drive5.com/muscle/

Documents

Recent Progress in Multiple Sequence Alignments: A Survey