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

chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKDwheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSEtrybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGPmouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP ***. ::: .: .. . : . . * . *: *

chite AATAKQNYIRALQEYERNGG-wheat ANKLKGEYNKAIAAYNKGESAtrybr AEKDKERYKREM---------mouse AKDDRIRYDNEMKSWEEQMAE * : .* . :

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

chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKDwheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSEtrybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGPmouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP ***. ::: .: .. . : . . * . *: *

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

SAM HMMer

SAGAGA

Clustal

Dialign

T-Coffee

ProgressiveSimultaneous

POA OMA

PralineMAFFT

Combalign

Non tree based

Cédric Notredame (21/04/23)Iterative

Iteralign

SAM HMMer

Clustal

Dialign

T-Coffee

ProgressiveSimultaneous

POA OMA

PralineMAFFT

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

Tree: Cost=1C

PROBLEM: We do not know the true tree

STAR Tree

Model: ALL the sequences have the same ancestor

A CStar Tree: Cost=2

PROBLEM: the tree star is phylogenetically wrong

Sums of Pairs

Model=Every sequence is the ancestor of every sequence

A CSums of Pairs: Cost=6

CAAACC

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

kii mmsmS ,

[s(a,b): matrix]

[i: column i]

[k, l: seq index]

Sums of Pairs: Some of itslimitations (Durbin,

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,

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

5*N*(N-1)=

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

PROBLEM: -requires a simultaneous alignment-assumes independant sequences

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

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

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

Existsmmmm li

ki ,1,

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

Concistency Based

Iteralign

Dialign

T-Coffee

Praline

Combalign

ClustalPOA

MAFFTOMA

WeightedSums

of Pairs

EnthropySAM HMMer

A few Multiple Sequence Alignment Algorithms

A Few Algorithms

MSA and DCA

ClustalW

Dialign IIPrrp

GIBBS Sampler

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

chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKDwheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSEtrybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGPmouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP

chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKDwheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE

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

…[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)

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)

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 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

-Greedy Heuristic (No Guarranty).

Progressive Alignment : ClustalW

-Scales Well: N, N L3 2 2

Progressive Alignment With a Heuristic DP:

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

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)

Nk: Number of Edges meeting on Node k.

]][[*),( yB

xA RRMatWRRScore

PROBLEM: Weight Depends only on the Tree topology

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 =

]][[**),( 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 (MAFFT evaluation)

Conclusion

What Is BaliBaseWhich Method ?

PROBLEM

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

Strategy

ClustalW, T-coffee,MSA, DCA

PrrP,T-Coffee

Dialign

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/

Recent Progress in Multiple Sequence Alignments: A Survey

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