An Introduction to
Multiple Sequence
Alignments
Cédric Notredame
chite
wheat
trybr
mouse
---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD
--DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE
KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP
-----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP
***. ::: .: .. .
: . .
* . *: *
chite
wheat
trybr
mouse
AATAKQNYIRALQEYERNGGANKLKGEYNKAIAAYNKGESA
AEKDKERYKREM--------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?
chite
wheat
trybr
mouse
---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD
--DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE
KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP
-----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP
***. ::: .: .. .
: . .
* . *: *
chite
wheat
trybr
mouse
AATAKQNYIRALQEYERNGGANKLKGEYNKAIAAYNKGESA
AEKDKERYKREM--------AKDDRIRYDNEMKSWEEQMAE
*
: .* . :
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.
Phylogenic
Relation
Functional
Relation
How Can I Use A Multiple Sequence Alignment?
chite
wheat
trybr
unknown
---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD
--DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE
KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP
-----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP
***. ::: .: .. .
: . .
* . *: *
chite
wheat
trybr
unknown
AATAKQNYIRALQEYERNGGANKLKGEYNKAIAAYNKGESA
AEKDKERYKREM--------AKDDRIRYDNEMKSWEEQMAE
*
: .* . :
Less Than 30 % id
BUT
Conserved where it MATTERS
Extrapolation Beyond The Twilight Zone
Homology?
SwissProt
Unkown Sequence
How Can I Use A Multiple Sequence Alignment?
chite
wheat
trybr
mouse
---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD
--DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE
KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP
-----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP
***. ::: .: .. .
: . .
* . *: *
chite
wheat
trybr
mouse
AATAKQNYIRALQEYERNGGANKLKGEYNKAIAAYNKGESA
AEKDKERYKREM--------AKDDRIRYDNEMKSWEEQMAE
*
: .* . :
Extrapolation
Prosite Patterns
How Can I Use A Multiple Sequence Alignment?
chite
wheat
trybr
mouse
---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD
--DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE
KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP
-----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP
***. ::: .: .. .
: . .
* . *: *
chite
wheat
trybr
mouse
AATAKQNYIRALQEYERNGGANKLKGEYNKAIAAYNKGESA
AEKDKERYKREM--------AKDDRIRYDNEMKSWEEQMAE
*
: .* . :
Extrapolation
Prosite Patterns
P-K-R-[PA]-x(1)-[ST]…
How Can I Use A Multiple Sequence Alignment?
chite
wheat
trybr
mouse
---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD
--DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE
KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP
-----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP
***. ::: .: .. .
: . .
* . *: *
chite
wheat
trybr
mouse
AATAKQNYIRALQEYERNGGANKLKGEYNKAIAAYNKGESA
AEKDKERYKREM--------AKDDRIRYDNEMKSWEEQMAE
*
: .* . :
Extrapolation
Prosite Patterns
SwissProt
Uncharacterised Signature
Match?
How Can I Use A Multiple Sequence Alignment?
chite
wheat
trybr
mouse
---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD
--DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE
KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP
-----KPKRPRSAYNIYVSESFQ----EAKDDS-IQGKLKLVNEAWKNLSP
***. ::: .: .. .
: . .
* . *: *
chite
wheat
trybr
mouse
AATAKQNYIRALQEYERNGGANKLKGEYNKAIAAYNKGESA
AEKDKERYKREM--------AKDDRIRYDNEMKSWEEQMAE
*
: .* . :
Extrapolation
Prosite Patterns
Profiles And HMMs
L?
K>R
A
F
D
E
F
G
H
Q
I
V
L
W
-More Sensitive
-More Specific
A PROSITE PROFILE
A Substitution Cost For Every Amino
Acid, At Every Position
How Can I Use A Multiple Sequence Alignment?
chite
wheat
trybr
mouse
---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD
--DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE
KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP
-----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP
***. ::: .: .. .
: . .
* . *: *
chite
wheat
trybr
mouse
AATAKQNYIRALQEYERNGGANKLKGEYNKAIAAYNKGESA
AEKDKERYKREM--------AKDDRIRYDNEMKSWEEQMAE
*
: .* . :
Extrapolation
Motifs/Patterns
Profiles
Phylogeny
chite
wheat
trybr
mouse
-Evolution
-Paralogy/Orthology
How Can I Use A Multiple Sequence Alignment?
chite
wheat
trybr
mouse
---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD
--DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE
KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP
-----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP
***. ::: .: .. .
: . .
* . *: *
chite
wheat
trybr
mouse
AATAKQNYIRALQEYERNGGANKLKGEYNKAIAAYNKGESA
AEKDKERYKREM--------AKDDRIRYDNEMKSWEEQMAE
*
: .* . :
Extrapolation
Motifs/Patterns
Profiles
Phylogeny
Struc. Prediction
Column Constraint

Evolution Constraint

Structure Constraint
How Can I Use A Multiple Sequence Alignment?
chite
wheat
trybr
mouse
---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD
--DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE
KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP
-----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP
***. ::: .: .. .
: . .
* . *: *
chite
wheat
trybr
mouse
AATAKQNYIRALQEYERNGGANKLKGEYNKAIAAYNKGESA
AEKDKERYKREM--------AKDDRIRYDNEMKSWEEQMAE
*
: .* . :
Extrapolation
Motifs/Patterns
Profiles
Phylogeny
Struc. Prediction
PsiPred OR PhD For secondary
Structure Prediction:
75% Accurate.
Threading: is improving
but is not yet as good.
How Can I Use A Multiple Sequence Alignment?
chite
wheat
trybr
mouse
---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD
--DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE
KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP
-----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP
***. ::: .: .. .
: . .
* . *: *
chite
wheat
trybr
mouse
AATAKQNYIRALQEYERNGGANKLKGEYNKAIAAYNKGESA
AEKDKERYKREM--------AKDDRIRYDNEMKSWEEQMAE
*
: .* . :
Automatic Multiple
Sequence Alignment methods
are 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
chite
wheat
trybr
mouse
COMPUTATION
What is THE Good Alignment
---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD
--DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE
KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP
-----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP
***. ::: .: .. .
: . .
* . *: *
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
-A nice set of Sequences
-Substitution Matrix (Blosum)
-Gap Penalties.
-An Evaluation Function
A
A
A
C
C
A
A
A
C
Sums of Pairs: Cost=6
C
Over-estimation of the Substitutions
Easy to compute
The COMPUTATIONAL Problem.
Producing the Alignment
-A nice set of Sequences
-Substitution Matrix (Blosum)
-Gap Penalties.
-An Evaluation Function
-An Alignment Algorithm
Will It Work
?
GLOBAL Alignment
HOW CAN I ALIGN MANY SEQUENCES
2 Globins =>1 Min
HOW CAN I ALIGN MANY SEQUENCES
3 Globins =>2 hours
HOW CAN I ALIGN MANY SEQUENCES
4 Globins => 10 days
HOW CAN I ALIGN MANY SEQUENCES
5 Globins => 3 years
HOW CAN I ALIGN MANY SEQUENCES
!
DHEA
Loaded
6 Globins =>300 years
HOW CAN I ALIGN MANY SEQUENCES
7 Globins =>30. 000 years
Solidified Fossil,
Old stuff
HOW CAN I ALIGN MANY SEQUENCES
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
-ClustalW
-Greedy Heuristic (No Guarranty).
-Fast
Progressive Alignment
Feng and Dolittle, 1988; Taylor 1989
Clustering
Progressive Alignment
Dynamic Programming Using A Substitution Matrix
Progressive Alignment
-Depends on the CHOICE of the sequences.
-Depends on the ORDER of the sequences (Tree).
-Depends on the PARAMETERS:
•Substitution Matrix.
•Penalties (Gop, Gep).
•Sequence Weight.
•Tree making Algorithm.
Progressive Alignment
When Does It Work
Works Well When Phylogeny is Dense
No outlayer Sequence.
Image: River Crossing
Progressive Alignment
When Doesn’t It Work
CLUSTALW (Score=20, Gop=-1, Gep=0, M=1)
SeqA
SeqB
SeqC
SeqD
GARFIELD
GARFIELD
GARFIELD
--------
THE
THE
THE
THE
LAST
FAST
VERY
----
FA-T
CA-T
FAST
FA-T
CAT
--CAT
CAT
LAST
FAST
VERY
----
FA-T
---FAST
FA-T
CAT
CAT
CAT
CAT
CORRECT (Score=24)
SeqA
SeqB
SeqC
SeqD
GARFIELD
GARFIELD
GARFIELD
--------
THE
THE
THE
THE
GARFIELD THE LAST FAT CAT
GARFIELD THE LAST FAT CAT
GARFIELD THE FAST CAT ---
GARFIELD THE FAST CAT
GARFIELD
GARFIELD
GARFIELD
--------
THE
THE
THE
THE
LAST
FAST
VERY
----
FA-T
CA-T
FAST
FA-T
CAT
--CAT
CAT
GARFIELD THE VERY FAST CAT
GARFIELD THE VERY FAST CAT
-------- THE ---- FA-T CAT
THE FAT 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
PRVA_HUMAN
PRVA_GERSP
PRVA_MOUSE
PRVA_RAT
PRVA_RABIT
SMTDLLNAEDIKKAVGAFSAIDSFDHKKFFQMVGLKKKSADDVKKVFHILDKDKSGFIEE
SMTDLLNAEDIKKAVGAFSATDSFDHKKFFQMVGLKKKSADDVKKVFHMLDKDKSGFIEE
SMTDLLSAEDIKKAIGAFAAADSFDHKKFFQMVGLKKKTPDDVKKVFHILDKDKSGFIEE
SMTDVLSAEDIKKAIGAFAAADSFDHKKFFQMVGLKKKNPDEVKKVFHILDKDKSGFIEE
SMTDLLSAEDIKKAIGAFTAADSFDHKKFFQMVGLKKKSADDVKKVFHILDKDKSGFIEE
AMTELLNAEDIKKAIGAFAAAESFDHKKFFQMVGLKKKSTEDVKKVFHILDKDKSGFIEE
:**::*.*******:***:* :****************..::******:***********
PRVA_MACFU
PRVA_HUMAN
PRVA_GERSP
PRVA_MOUSE
PRVA_RAT
PRVA_RABIT
DELGFILKGFSPDARDLSAKETKTLMAAGDKDGDGKIGVDEFSTLVAES
DELGFILKGFSPDARDLSAKETKMLMAAGDKDGDGKIGVDEFSTLVAES
DELGFILKGFSSDARDLSAKETKTLLAAGDKDGDGKIGVEEFSTLVSES
DELGSILKGFSSDARDLSAKETKTLLAAGDKDGDGKIGVEEFSTLVAES
DELGSILKGFSSDARDLSAKETKTLMAAGDKDGDGKIGVEEFSTLVAES
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!
PRVA_MACFU
PRVA_HUMAN
PRVA_GERSP
PRVA_MOUSE
PRVA_RAT
PRVA_RABIT
TPCC_MOUSE
------------------------------------------SMTDLLN----AEDIKKA
------------------------------------------SMTDLLN----AEDIKKA
------------------------------------------SMTDLLS----AEDIKKA
------------------------------------------SMTDVLS----AEDIKKA
------------------------------------------SMTDLLS----AEDIKKA
------------------------------------------AMTELLN----AEDIKKA
MDDIYKAAVEQLTEEQKNEFKAAFDIFVLGAEDGCISTKELGKVMRMLGQNPTPEELQEM
: :*.
.*::::
PRVA_MACFU
PRVA_HUMAN
PRVA_GERSP
PRVA_MOUSE
PRVA_RAT
PRVA_RABIT
TPCC_MOUSE
VGAFSAIDS--FDHKKFFQMVG------LKKKSADDVKKVFHILDKDKSGFIEEDELGFI
VGAFSATDS--FDHKKFFQMVG------LKKKSADDVKKVFHMLDKDKSGFIEEDELGFI
IGAFAAADS--FDHKKFFQMVG------LKKKTPDDVKKVFHILDKDKSGFIEEDELGFI
IGAFAAADS--FDHKKFFQMVG------LKKKNPDEVKKVFHILDKDKSGFIEEDELGSI
IGAFTAADS--FDHKKFFQMVG------LKKKSADDVKKVFHILDKDKSGFIEEDELGSI
IGAFAAAES--FDHKKFFQMVG------LKKKSTEDVKKVFHILDKDKSGFIEEEELGFI
IDEVDEDGSGTVDFDEFLVMMVRCMKDDSKGKSEEELSDLFRMFDKNADGYIDLDELKMM
This Alignment Is not Informative about the relation
Betwwen TPCC MOUSE and the rest of the sequences.
-A better Spread of the
Sequences is needed
Selecting Diverse Sequences (Opus II)
Selecting Diverse Sequences (Opus II)
PRVB_CYPCA
PRVB_BOACO
PRV1_SALSA
PRVB_LATCH
PRVB_RANES
PRVA_MACFU
PRVA_ESOLU
-AFAGVLNDADIAAALEACKAADSFNHKAFFAKVGLTSKSADDVKKAFAIIDQDKSGFIE
-AFAGILSDADIAAGLQSCQAADSFSCKTFFAKSGLHSKSKDQLTKVFGVIDRDKSGYIE
MACAHLCKEADIKTALEACKAADTFSFKTFFHTIGFASKSADDVKKAFKVIDQDASGFIE
-AVAKLLAAADVTAALEGCKADDSFNHKVFFQKTGLAKKSNEELEAIFKILDQDKSGFIE
-SITDIVSEKDIDAALESVKAAGSFNYKIFFQKVGLAGKSAADAKKVFEILDRDKSGFIE
-SMTDLLNAEDIKKAVGAFSAIDSFDHKKFFQMVGLKKKSADDVKKVFHILDKDKSGFIE
--AKDLLKADDIKKALDAVKAEGSFNHKKFFALVGLKAMSANDVKKVFKAIDADASGFIE
:
*: .: . .* .:*. * **
*:
* :
* :* * **:**
PRVB_CYPCA
PRVB_BOACO
PRV1_SALSA
PRVB_LATCH
PRVB_RANES
PRVA_MACFU
PRVA_ESOLU
EDELKLFLQNFKADARALTDGETKTFLKAGDSDGDGKIGVDEFTALVKAEDELKKFLQNFDGKARDLTDKETAEFLKEGDTDGDGKIGVEEFVVLVTKG
VEELKLFLQNFCPKARELTDAETKAFLKAGDADGDGMIGIDEFAVLVKQDEELELFLQNFSAGARTLTKTETETFLKAGDSDGDGKIGVDEFQKLVKAQDELGLFLQNFRASARVLSDAETSAFLKAGDSDGDGKIGVEEFQALVKAEDELGFILKGFSPDARDLSAKETKTLMAAGDKDGDGKIGVDEFSTLVAES
EEELKFVLKSFAADGRDLTDAETKAFLKAADKDGDGKIGIDEFETLVHEA
:** .*:.*
.* *: ** :: .* **** **::** **
-A REASONABLE Model Now Exists.
-Going Further:Remote Homologues.
Aligning Remote Homologues
PRVA_MACFU
PRVA_ESOLU
PRVB_CYPCA
PRVB_BOACO
PRV1_SALSA
PRVB_LATCH
PRVB_RANES
TPCS_RABIT
TPCS_PIG
TPCC_MOUSE
------------------------------------------SMTDLLNA----EDIKKA
-------------------------------------------AKDLLKA----DDIKKA
------------------------------------------AFAGVLND----ADIAAA
------------------------------------------AFAGILSD----ADIAAG
-----------------------------------------MACAHLCKE----ADIKTA
------------------------------------------AVAKLLAA----ADVTAA
------------------------------------------SITDIVSE----KDIDAA
-TDQQAEARSYLSEEMIAEFKAAFDMFDADGG-GDISVKELGTVMRMLGQTPTKEELDAI
-TDQQAEARSYLSEEMIAEFKAAFDMFDADGG-GDISVKELGTVMRMLGQTPTKEELDAI
MDDIYKAAVEQLTEEQKNEFKAAFDIFVLGAEDGCISTKELGKVMRMLGQNPTPEELQEM
:
::
PRVA_MACFU
PRVA_ESOLU
PRVB_CYPCA
PRVB_BOACO
PRV1_SALSA
PRVB_LATCH
PRVB_RANES
TPCS_RABIT
TPCS_PIG
TPCC_MOUSE
VGAFSAIDS--FDHKKFFQMVG------LKKKSADDVKKVFHILDKDKSGFIEEDELGFI
LDAVKAEGS--FNHKKFFALVG------LKAMSANDVKKVFKAIDADASGFIEEEELKFV
LEACKAADS--FNHKAFFAKVG------LTSKSADDVKKAFAIIDQDKSGFIEEDELKLF
LQSCQAADS--FSCKTFFAKSG------LHSKSKDQLTKVFGVIDRDKSGYIEEDELKKF
LEACKAADT--FSFKTFFHTIG------FASKSADDVKKAFKVIDQDASGFIEVEELKLF
LEGCKADDS--FNHKVFFQKTG------LAKKSNEELEAIFKILDQDKSGFIEDEELELF
LESVKAAGS--FNYKIFFQKVG------LAGKSAADAKKVFEILDRDKSGFIEQDELGLF
IEEVDEDGSGTIDFEEFLVMMVRQMKEDAKGKSEEELAECFRIFDRNADGYIDAEELAEI
IEEVDEDGSGTIDFEEFLVMMVRQMKEDAKGKSEEELAECFRIFDRNMDGYIDAEELAEI
IDEVDEDGSGTVDFDEFLVMMVRCMKDDSKGKSEEELSDLFRMFDKNADGYIDLDELKMM
:
. .: .. . *:
* :
* :* : .*:*: :** .
PRVA_MACFU
PRVA_ESOLU
PRVB_CYPCA
PRVB_BOACO
PRV1_SALSA
PRVB_LATCH
PRVB_RANES
TPCS_RABIT
TPCS_PIG
TPCC_MOUSE
LKGFSPDARDLSAKETKTLMAAGDKDGDGKIGVDEFSTLVAESLKSFAADGRDLTDAETKAFLKAADKDGDGKIGIDEFETLVHEALQNFKADARALTDGETKTFLKAGDSDGDGKIGVDEFTALVKA-LQNFDGKARDLTDKETAEFLKEGDTDGDGKIGVEEFVVLVTKGLQNFCPKARELTDAETKAFLKAGDADGDGMIGIDEFAVLVKQ-LQNFSAGARTLTKTETETFLKAGDSDGDGKIGVDEFQKLVKA-LQNFRASARVLSDAETSAFLKAGDSDGDGKIGVEEFQALVKA-FR---ASGEHVTDEEIESLMKDGDKNNDGRIDFDEFLKMMEGVQ
FR---ASGEHVTDEEIESIMKDGDKNNDGRIDFDEFLKMMEGVQ
LQ---ATGETITEDDIEELMKDGDKNNDGRIDYDEFLEFMKGVE
::
.. :: :
:: .* :.** *. :** ::
Some
Guidelines
…
Do Not Use Two Many Sequences…
Reading Your Alignment
Going Further…
PRVA_MACFU
PRVB_BOACO
PRV1_SALSA
TPCS_RABIT
TPCS_PIG
TPCC_MOUSE
TPC_PATYE
VGAFSAIDS--FDHKKFFQMVG------LKKKSADDVKKVFHILDKDKSGFIEEDELGFI
LQSCQAADS--FSCKTFFAKSG------LHSKSKDQLTKVFGVIDRDKSGYIEEDELKKF
LEACKAADT--FSFKTFFHTIG------FASKSADDVKKAFKVIDQDASGFIEVEELKLF
IEEVDEDGSGTIDFEEFLVMMVRQMKEDAKGKSEEELAECFRIFDRNADGYIDAEELAEI
IEEVDEDGSGTIDFEEFLVMMVRQMKEDAKGKSEEELAECFRIFDRNMDGYIDAEELAEI
IDEVDEDGSGTVDFDEFLVMMVRCMKDDSKGKSEEELSDLFRMFDKNADGYIDLDELKMM
SDEMDEEATGRLNCDAWIQLFER---KLKEDLDERELKEAFRVLDKEKKGVIKVDVLRWI
.
: .. . ::
. :
* :* : .* *. : * .
PRVA_MACFU
PRVB_BOACO
PRV1_SALSA
TPCS_RABIT
TPCS_PIG
TPCC_MOUSE
TPC_PATYE
LKGFSPDARDLSAKETKTLMAAGDKDGDGKIGVDEFSTLVAES-LQNFDGKARDLTDKETAEFLKEGDTDGDGKIGVEEFVVLVTKG-LQNFCPKARELTDAETKAFLKAGDADGDGMIGIDEFAVLVKQ--FR---ASGEHVTDEEIESLMKDGDKNNDGRIDFDEFLKMMEGVQFR---ASGEHVTDEEIESIMKDGDKNNDGRIDFDEFLKMMEGVQLQ---ATGETITEDDIEELMKDGDKNNDGRIDYDEFLEFMKGVELS---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!!!
chite
wheat
trybr
mouse
---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD
--DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE
KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP
-----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP
***. ::: .: .. .
: . .
* . *: *
chite
wheat
trybr
mouse
AATAKQNYIRALQEYERNGGANKLKGEYNKAIAAYNKGESA
AEKDKERYKREM--------AKDDRIRYDNEMKSWEEQMAE
*
: .* . :
DO NOT PLAY WITH PARAMETERS IF YOU KNOW THE
ALIGNMENT YOU WANT: MAKE IT YOURSELF!
chite
wheat
trybr
mouse
---ADKPKRPL-SAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD
--DPNKPKRAP-SAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE
KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP
-----KPKRPR-SAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP
***. :*: .: .. .
: . .
* . *: *
chite
wheat
trybr
mouse
AATAKQNYIRALQEYERNGGANKLKGEYNKAIAAYNKGESA
AEKDKERYKREM--------AKDDRIRYDNEMKSWEEQMAE
*
: .* . :
TUNING or NOT TUNING!!!
-PARAMETERS TO TUNE USUALLY INCLUDE:
•GOP/ GEP
•MATRIX
•SENSITIVITY Vs SPEED
Substitution Matrices
(Etzold and al. 1993)
GOP
Gonnet
Blosum50
Pam250
61.7 %
59.7 %
59.2 %
GEP
-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.
KEEP A BIOLOGICAL PERSPECTIVE
chite
wheat
trybr
mouse
---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD
--DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE
KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP
-----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP
***. ::: .: .. .
: . .
* . *: *
DIFFERENT PARAMETERS
chite
wheat
trybr
mouse
AD--K----PKR-PLYMLWLNS-ARESIKRENPDFK-VT-EVAKKGGELWRGL-DPNK----PKRAP-FFVFMGE-FREEFKQKNPKNKSVA-AVGKAAGERWKSLS
-K--KDSNAPKR-AMT-MFFSSDFR-S-KH-S-DLS-IV-EMSKAAGAAWKELG
----K----PKR-PRYNIYVSESFQEA-K--D-D-S-AQGKL-KLVNEAWKNLS
*
*** .:: ::... :
* . . .
: * . *: *
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
Available
Methods
???
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.
-Memory and CPU hungry
-Do Well When They Can Run.
Simultaneous Alignments : DCA
-Few Small Closely Related Sequence,
but less limited than MSA
-Memory and CPU hungry, but
less than MSA
-Do Well When Can Run.
Dialign II
1) Identify best chain of
segments on each pair of
sequence. Assign a Pvalue to
each Segment Pair.
2) Ré-évaluate each
segment pair according to
its consistency with the
others
3) Assemble the alignment
according to the segment
pairs.
Muscle
Iterative Methods
7.16.1 Progressive
-HMMs, HMMER, SAM, MUSCLE
-Slow, Sometimes Inaccurate
-Good Profile Generators
MUSCLE
7.16.1 Progressive
MUSCLE
phylogenomics.berkeley.edu/cgi-bin/muscle/input_muscle.py
7.16.1 Progressive
MAFFT
Fast Fourrier Transformé
Prank
Stachmo
Mixing Heterogenous Data With
T-Coffee
Local Alignment
Global Alignment
Multiple Alignment
Specialist
Structural
Multiple Sequence Alignment
Mixing Sequences and Structures
with T-Coffee
Seq Vs Seq
Seq Vs Struct
Local
Global
Thread
Struct Vs Struct
Superpose
Evaluation on Homestrad
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 ?
I
II
Choosing the right
method
Situation  Solution
Priority  Solution
Method
Priority
Accuracy
Speed
Trees
Profile
2D –Pred
3D-Pred
Func-Pred
Purpose  Solution
Conclusion
Multiple Alignment
-The BEST alignment Method:
Your Brain
The Right Data
-The Best Evaluation Procedure:
Experimental Data (SwissProt)
-Choosing The Sequences Well is Important
-Beware of repeated elements
Multiple Alignment
Know Your Problem: What do you want to do
with your MSA
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