An Introduction to Multiple Sequence Alignments Cédric Notredame

An Introduction toMultiple Sequence

AlignmentsCédric Notredame

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

Manguel M, Samaniego F.J., Abraham Wald’s Work on Aircraft Suvivability, J. American Statistical Association. 79, 259-270, (1984)

Our Scope

How Can I Use My Alignment?

How Does The Computer Align The Sequences?

How Can I Assemble a Mult. Aln?

What are the Difficulties?

Outline

-Why Do We Need Multiple Sequence Alignment ?

-The progressive Alignment Algorithm

-A possible Strategy…

-Potential Difficulties

Pre-requisite

-How Do Sequences Evolve?

-How can We COMPARE Sequences ?

-How can We ALIGN Sequences ?

Why Do We Need Multiple Sequence

Alignment ?

Sometimes Two Sequences Are Not Enough…

The man with TWO watches NEVER knows the time



What is A Multiple Sequence Alignment?

Structural Criteria:Residues are arranged so that those playing a similar role end up in the same column.

Evolution Criteria:Residues are arranged so that those having the same ancestor end up in the same column.

PhylogenicRelation

FunctionalRelation

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How Can I Use A Multiple Sequence Alignment?

Extrapolation Beyond The Twilight Zone

SwissProtUnkown Sequence

Homology?

Less Than 30 % idBUT

Conserved where it MATTERS




Extrapolation

Prosite Patterns




Extrapolation

Prosite PatternsP-K-R-[PA]-x(1)-[ST]…




Extrapolation

Prosite Patterns

SwissProtUncharacterised Signature

Match?

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Extrapolation

Prosite Patterns

Profiles And HMMs

-More Sensitive-More Specific

L?K>R

AFDEFGHQIVLW

A PROSITE PROFILE

A Substitution Cost For Every Amino Acid, At Every Position




Extrapolation

Motifs/Patterns

Phylogeny

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wheattrybr

mouse

-Evolution-Paralogy/Orthology

Profiles




Extrapolation

Motifs/Patterns

Phylogeny

Profiles

Struc. Prediction

Column Constraint

Evolution Constraint

Structure Constraint




Extrapolation

Motifs/Patterns

Phylogeny

Profiles

Struc. Prediction

PsiPred OR PhD For secondary Structure Prediction: 75% Accurate.

Threading: is improving but is not yet as good.




Automatic MultipleSequence Alignment methodsare not always perfect…

You know better…With your big BRAIN

Why Is It Difficult To Compute A multiple Sequence Alignment?

A CROSSROAD PROBLEM

BIOLOGY:What is A Good Alignment

COMPUTATIONWhat is THE Good Alignment


The Biological Problem.

Same as PairWise Alignment Problem

We do NOT know how Sequences Evolve.

We do NOT understand the Relation Between Structures and Sequences.

We would NOT recognize the Correct Alignment if we had it IN FRONT of our eyes…

The Biological Problem.The Charlie Chaplin Paradox

The Biological Problem.How to Evaluate an Alignment

-Substitution Matrix (Blosum)

-An Evaluation Function

AAACC

-Gap Penalties.

-A nice set of Sequences

A

A

A CSums of Pairs: Cost=6

C

Over-estimation of the Substitutions

Easy to compute

The COMPUTATIONAL Problem.Producing the Alignment

-Substitution Matrix (Blosum)

-An Evaluation Function

-Gap Penalties.

-A nice set of Sequences

-An Alignment Algorithm

GLOBAL Alignment

Will It Work?

HOW CAN I ALIGN MANY SEQUENCES

2 Globins =>1 Min

3 Globins =>2 hours


4 Globins => 10 days


5 Globins => 3 years


6 Globins =>300 years


!DHEA

Loaded

7 Globins =>30. 000 years


Solidified Fossil,Old stuff

8 Globins =>3 Million years


The Progressive Multiple Alignment

Algorithm(Clustal W)

Making An Alignment

Any Exact Method would be TOO SLOW

We will use a Heuristic Algorithm.

Progressive Alignment Algorithm is the most Popular

-Fast

-ClustalW

-Greedy Heuristic (No Guarranty).

Progressive Alignment

Feng and Dolittle, 1988; Taylor 1989

Clustering

Dynamic Programming Using A Substitution Matrix



-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.

Progressive AlignmentWhen Does It Work

Works Well When Phylogeny is Dense

No outlayer Sequence.

Image: River Crossing

SeqA GARFIELD THE LAST FA-T CATSeqB GARFIELD THE FAST CA-T ---SeqC GARFIELD THE VERY FAST CATSeqD -------- THE ---- FA-T CAT

CLUSTALW (Score=20, Gop=-1, Gep=0, M=1)

SeqA GARFIELD THE LAST FA-T CATSeqB GARFIELD THE FAST ---- CATSeqC GARFIELD THE VERY FAST CATSeqD -------- THE ---- FA-T CAT

CORRECT (Score=24)

Progressive AlignmentWhen Doesn’t It Work

GARFIELD THE LAST FAT CATGARFIELD THE FAST CAT ---

GARFIELD THE LAST FAT CAT

GARFIELD THE FAST CAT

GARFIELD THE VERY FAST CAT

THE FAT CAT

GARFIELD THE VERY FAST CAT-------- THE ---- FA-T CAT

GARFIELD THE LAST FA-T CATGARFIELD THE FAST CA-T ---GARFIELD THE VERY FAST CAT-------- THE ---- FA-T CAT

Building the Right Multiple Sequence

Alignment.

Recognizing The Right Sequences When you Meet Them…

Gathering Sequences: BLAST

Common Mistake:Sequences Too Closely Related

PRVA_MACFU SMTDLLNAEDIKKAVGAFSAIDSFDHKKFFQMVGLKKKSADDVKKVFHILDKDKSGFIEEPRVA_HUMAN SMTDLLNAEDIKKAVGAFSATDSFDHKKFFQMVGLKKKSADDVKKVFHMLDKDKSGFIEEPRVA_GERSP SMTDLLSAEDIKKAIGAFAAADSFDHKKFFQMVGLKKKTPDDVKKVFHILDKDKSGFIEEPRVA_MOUSE SMTDVLSAEDIKKAIGAFAAADSFDHKKFFQMVGLKKKNPDEVKKVFHILDKDKSGFIEEPRVA_RAT SMTDLLSAEDIKKAIGAFTAADSFDHKKFFQMVGLKKKSADDVKKVFHILDKDKSGFIEEPRVA_RABIT AMTELLNAEDIKKAIGAFAAAESFDHKKFFQMVGLKKKSTEDVKKVFHILDKDKSGFIEE :**::*.*******:***:* :****************..::******:***********

PRVA_MACFU DELGFILKGFSPDARDLSAKETKTLMAAGDKDGDGKIGVDEFSTLVAESPRVA_HUMAN DELGFILKGFSPDARDLSAKETKMLMAAGDKDGDGKIGVDEFSTLVAESPRVA_GERSP DELGFILKGFSSDARDLSAKETKTLLAAGDKDGDGKIGVEEFSTLVSESPRVA_MOUSE DELGSILKGFSSDARDLSAKETKTLLAAGDKDGDGKIGVEEFSTLVAESPRVA_RAT DELGSILKGFSSDARDLSAKETKTLMAAGDKDGDGKIGVEEFSTLVAESPRVA_RABIT EELGFILKGFSPDARDLSVKETKTLMAAGDKDGDGKIGADEFSTLVSES :*** ******.******.**** *:************.:******:**

-IDENTICAL SEQUENCES BRING NO INFORMATION FOR THE MULTIPLE SEQUENCE ALIGNMENT

-MULTIPLE SEQUENCE ALIGNMENTS THRIVE ON DIVERSITY…

Sequence Weighting Within ClustalW

Selecting Diverse Sequences (Opus II)

Respect Information!

This Alignment Is not Informative about the relation Betwwen TPCC MOUSE and the rest of the sequences.

PRVA_MACFU ------------------------------------------SMTDLLN----AEDIKKAPRVA_HUMAN ------------------------------------------SMTDLLN----AEDIKKAPRVA_GERSP ------------------------------------------SMTDLLS----AEDIKKAPRVA_MOUSE ------------------------------------------SMTDVLS----AEDIKKAPRVA_RAT ------------------------------------------SMTDLLS----AEDIKKAPRVA_RABIT ------------------------------------------AMTELLN----AEDIKKATPCC_MOUSE MDDIYKAAVEQLTEEQKNEFKAAFDIFVLGAEDGCISTKELGKVMRMLGQNPTPEELQEM : :*. .*::::

PRVA_MACFU VGAFSAIDS--FDHKKFFQMVG------LKKKSADDVKKVFHILDKDKSGFIEEDELGFIPRVA_HUMAN VGAFSATDS--FDHKKFFQMVG------LKKKSADDVKKVFHMLDKDKSGFIEEDELGFIPRVA_GERSP IGAFAAADS--FDHKKFFQMVG------LKKKTPDDVKKVFHILDKDKSGFIEEDELGFIPRVA_MOUSE IGAFAAADS--FDHKKFFQMVG------LKKKNPDEVKKVFHILDKDKSGFIEEDELGSIPRVA_RAT IGAFTAADS--FDHKKFFQMVG------LKKKSADDVKKVFHILDKDKSGFIEEDELGSIPRVA_RABIT IGAFAAAES--FDHKKFFQMVG------LKKKSTEDVKKVFHILDKDKSGFIEEEELGFITPCC_MOUSE IDEVDEDGSGTVDFDEFLVMMVRCMKDDSKGKSEEELSDLFRMFDKNADGYIDLDELKMM

-A better Spread of the Sequences is needed



PRVB_CYPCA -AFAGVLNDADIAAALEACKAADSFNHKAFFAKVGLTSKSADDVKKAFAIIDQDKSGFIEPRVB_BOACO -AFAGILSDADIAAGLQSCQAADSFSCKTFFAKSGLHSKSKDQLTKVFGVIDRDKSGYIEPRV1_SALSA MACAHLCKEADIKTALEACKAADTFSFKTFFHTIGFASKSADDVKKAFKVIDQDASGFIEPRVB_LATCH -AVAKLLAAADVTAALEGCKADDSFNHKVFFQKTGLAKKSNEELEAIFKILDQDKSGFIEPRVB_RANES -SITDIVSEKDIDAALESVKAAGSFNYKIFFQKVGLAGKSAADAKKVFEILDRDKSGFIEPRVA_MACFU -SMTDLLNAEDIKKAVGAFSAIDSFDHKKFFQMVGLKKKSADDVKKVFHILDKDKSGFIEPRVA_ESOLU --AKDLLKADDIKKALDAVKAEGSFNHKKFFALVGLKAMSANDVKKVFKAIDADASGFIE : *: .: . .* .:*. * ** *: * : * :* * **:**

PRVB_CYPCA EDELKLFLQNFKADARALTDGETKTFLKAGDSDGDGKIGVDEFTALVKA-PRVB_BOACO EDELKKFLQNFDGKARDLTDKETAEFLKEGDTDGDGKIGVEEFVVLVTKGPRV1_SALSA VEELKLFLQNFCPKARELTDAETKAFLKAGDADGDGMIGIDEFAVLVKQ-PRVB_LATCH DEELELFLQNFSAGARTLTKTETETFLKAGDSDGDGKIGVDEFQKLVKA-PRVB_RANES QDELGLFLQNFRASARVLSDAETSAFLKAGDSDGDGKIGVEEFQALVKA-PRVA_MACFU EDELGFILKGFSPDARDLSAKETKTLMAAGDKDGDGKIGVDEFSTLVAESPRVA_ESOLU EEELKFVLKSFAADGRDLTDAETKAFLKAADKDGDGKIGIDEFETLVHEA :** .*:.* .* *: ** :: .* **** **::** **

-A REASONABLE Model Now Exists.

-Going Further:Remote Homologues.

Aligning Remote Homologues

PRVA_MACFU ------------------------------------------SMTDLLNA----EDIKKAPRVA_ESOLU -------------------------------------------AKDLLKA----DDIKKAPRVB_CYPCA ------------------------------------------AFAGVLND----ADIAAAPRVB_BOACO ------------------------------------------AFAGILSD----ADIAAGPRV1_SALSA -----------------------------------------MACAHLCKE----ADIKTAPRVB_LATCH ------------------------------------------AVAKLLAA----ADVTAAPRVB_RANES ------------------------------------------SITDIVSE----KDIDAATPCS_RABIT -TDQQAEARSYLSEEMIAEFKAAFDMFDADGG-GDISVKELGTVMRMLGQTPTKEELDAITPCS_PIG -TDQQAEARSYLSEEMIAEFKAAFDMFDADGG-GDISVKELGTVMRMLGQTPTKEELDAITPCC_MOUSE MDDIYKAAVEQLTEEQKNEFKAAFDIFVLGAEDGCISTKELGKVMRMLGQNPTPEELQEM : ::

PRVA_MACFU VGAFSAIDS--FDHKKFFQMVG------LKKKSADDVKKVFHILDKDKSGFIEEDELGFIPRVA_ESOLU LDAVKAEGS--FNHKKFFALVG------LKAMSANDVKKVFKAIDADASGFIEEEELKFVPRVB_CYPCA LEACKAADS--FNHKAFFAKVG------LTSKSADDVKKAFAIIDQDKSGFIEEDELKLFPRVB_BOACO LQSCQAADS--FSCKTFFAKSG------LHSKSKDQLTKVFGVIDRDKSGYIEEDELKKFPRV1_SALSA LEACKAADT--FSFKTFFHTIG------FASKSADDVKKAFKVIDQDASGFIEVEELKLFPRVB_LATCH LEGCKADDS--FNHKVFFQKTG------LAKKSNEELEAIFKILDQDKSGFIEDEELELFPRVB_RANES LESVKAAGS--FNYKIFFQKVG------LAGKSAADAKKVFEILDRDKSGFIEQDELGLFTPCS_RABIT IEEVDEDGSGTIDFEEFLVMMVRQMKEDAKGKSEEELAECFRIFDRNADGYIDAEELAEITPCS_PIG IEEVDEDGSGTIDFEEFLVMMVRQMKEDAKGKSEEELAECFRIFDRNMDGYIDAEELAEITPCC_MOUSE IDEVDEDGSGTVDFDEFLVMMVRCMKDDSKGKSEEELSDLFRMFDKNADGYIDLDELKMM : . .: .. . *: * : * :* : .*:*: :** .

PRVA_MACFU LKGFSPDARDLSAKETKTLMAAGDKDGDGKIGVDEFSTLVAES-PRVA_ESOLU LKSFAADGRDLTDAETKAFLKAADKDGDGKIGIDEFETLVHEA-PRVB_CYPCA LQNFKADARALTDGETKTFLKAGDSDGDGKIGVDEFTALVKA--PRVB_BOACO LQNFDGKARDLTDKETAEFLKEGDTDGDGKIGVEEFVVLVTKG-PRV1_SALSA LQNFCPKARELTDAETKAFLKAGDADGDGMIGIDEFAVLVKQ--PRVB_LATCH LQNFSAGARTLTKTETETFLKAGDSDGDGKIGVDEFQKLVKA--PRVB_RANES LQNFRASARVLSDAETSAFLKAGDSDGDGKIGVEEFQALVKA--TPCS_RABIT FR---ASGEHVTDEEIESLMKDGDKNNDGRIDFDEFLKMMEGVQTPCS_PIG FR---ASGEHVTDEEIESIMKDGDKNNDGRIDFDEFLKMMEGVQTPCC_MOUSE LQ---ATGETITEDDIEELMKDGDKNNDGRIDYDEFLEFMKGVE :: .. :: : :: .* :.** *. :** ::

SomeGuideline

s…

Do Not Use Two Many Sequences…

Reading Your Alignment

Going Further…

PRVA_MACFU VGAFSAIDS--FDHKKFFQMVG------LKKKSADDVKKVFHILDKDKSGFIEEDELGFIPRVB_BOACO LQSCQAADS--FSCKTFFAKSG------LHSKSKDQLTKVFGVIDRDKSGYIEEDELKKFPRV1_SALSA LEACKAADT--FSFKTFFHTIG------FASKSADDVKKAFKVIDQDASGFIEVEELKLFTPCS_RABIT IEEVDEDGSGTIDFEEFLVMMVRQMKEDAKGKSEEELAECFRIFDRNADGYIDAEELAEITPCS_PIG IEEVDEDGSGTIDFEEFLVMMVRQMKEDAKGKSEEELAECFRIFDRNMDGYIDAEELAEITPCC_MOUSE IDEVDEDGSGTVDFDEFLVMMVRCMKDDSKGKSEEELSDLFRMFDKNADGYIDLDELKMMTPC_PATYE SDEMDEEATGRLNCDAWIQLFER---KLKEDLDERELKEAFRVLDKEKKGVIKVDVLRWI . : .. . :: . : * :* : .* *. : * .

PRVA_MACFU LKGFSPDARDLSAKETKTLMAAGDKDGDGKIGVDEFSTLVAES--PRVB_BOACO LQNFDGKARDLTDKETAEFLKEGDTDGDGKIGVEEFVVLVTKG--PRV1_SALSA LQNFCPKARELTDAETKAFLKAGDADGDGMIGIDEFAVLVKQ---TPCS_RABIT FR---ASGEHVTDEEIESLMKDGDKNNDGRIDFDEFLKMMEGVQ-TPCS_PIG FR---ASGEHVTDEEIESIMKDGDKNNDGRIDFDEFLKMMEGVQ-TPCC_MOUSE LQ---ATGETITEDDIEELMKDGDKNNDGRIDYDEFLEFMKGVE-TPC_PATYE LS---SLGDELTEEEIENMIAETDTDGSGTVDYEEFKCLMMSSDA : . :: : :: * :..* :. :** ::

WHAT MAKES A GOOD ALIGNMENT…

-THE MORE DIVERGEANT THE SEQUENCES, THE BETTER

-THE FEWER INDELS, THE BETTER

-NICE UNGAPPED BLOCKS SEPARATED WITH INDELS

-DIFFERENT CLASSES OF RESIDUES WITHIN A BLOCK:

•Completely Conserved•Conserved For Size and Hydropathy•Conserved For Size or Hydropathy

-THE ULTIMATE EVALUATION IS A MATTER OF PERSONNAL JUDGEMENT AND KNOWLEDGE.

Potential Difficulties

DO NOT OVERTUNE!!!



DO NOT PLAY WITH PARAMETERS IF YOU KNOW THE ALIGNMENT YOU WANT: MAKE IT YOURSELF!

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TUNING or NOT TUNING!!!

-MOST METHODS ARE TUNED FOR WORKING WELL ON AVERAGE

-PARAMETERS BEHAVIOUR DO NOT NECESSARILY FOLLOW THE THEORY (i.e. Substitution Matrices).

-A GOOD ALIGNMENT IS USUALLY ROBUST(i.e. Changes little).

-TUNE IF YOU WANT TO CONVINCE YOURSELF.

-PARAMETERS TO TUNE USUALLY INCLUDE:•GOP/ GEP•MATRIX•SENSITIVITY Vs SPEED

GOP

GEP

Substitution Matrices (Etzold and al. 1993)

Gonnet 61.7 %Blosum50 59.7 %

Pam250 59.2 %

KEEP A BIOLOGICAL PERSPECTIVE


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DIFFERENT PARAMETERS

WRONG ALIGNMENT !!!

REPEATS

THERE IS A PROBLEM WHEN TWO SEQUENCES DO NOT CONTAIN THE SAME NUMBER OF REPEATS

IT IS THEN BETTER TO MANUALLY EXTRACT THE REPEATS AND TO ALIGN THEM. INDIVIDUAL REPEATS CAN BE RECOGNIZED USING DOTTER

Naming Your Sequences The Right Way

What Are The AvailableMethods

???

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

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

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

Muscle

7.16.1 ProgressiveIterative Methods

-HMMs, HMMER, SAM, MUSCLE

-Slow, Sometimes Inaccurate-Good Profile Generators

7.16.1 ProgressiveMUSCLE

http://nar.oupjournals.org/content/vol32/issue5/images/large/gkh340f2.jpeg

7.16.1 Progressive

MUSCLE

phylogenomics.berkeley.edu/cgi-bin/muscle/input_muscle.py

MAFFTFast Fourrier Transformé

Prank

Stachmo

Mixing Heterogenous Data With

T-Coffee

Local Alignment Global Alignment

Multiple Sequence Alignment

Multiple Alignment

StructuralSpecialist

Struct Vs StructSeq Vs Struct

Thread

Evaluation on Homestrad

Superpose

Seq Vs SeqLocalGlobal

Mixing Sequences and Structures with T-Coffee

www.tcoffee.org

What is The Best Method

?

A better Question…

• What is the Best Alignment ?• What is the best bit of my

alignment ?

What is the Local Quality of my Alignment ?

II

I

Choosing the right method

Situation Solution

Priority Solution

Method

Priority

Trees Profile 2D –Pred 3D-Pred Func-Pred

Accuracy

Speed

Purpose Solution

Conclusion

-The BEST alignment Method: Your BrainThe Right Data

-Beware of repeated elements

Multiple Alignment

-The Best Evaluation Procedure:Experimental Data (SwissProt)

-Choosing The Sequences Well is Important

Know Your Problem: What do you want to do with your MSA

Multiple Alignment

Addresses

MAFFT Progressive/iterative www.biophys.kyoto-u.jp/katoh

POA Progressive/Simultaneous www.bioinformatics.ucla.edu/poa

MUSCLE Progressive/Iterative www.drive5.com/muscle

Documents

An Introduction to Multiple Sequence Alignments Cédric Notredame