Aligning Sequences With Genetic Algorithms

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Aligning Sequences With Genetic Algorithms
Cédric Notredame
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How Can I Use A Multiple Sequence Alignment?
chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAK
KGGELWRGLKD wheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPK
NKSVAAVGKAAGERWKSLSE trybr KKDSNAPKRAMTSFMFFSSDFR
S----KHSDLS-IVEMSKAAGAAWKELGP mouse
-----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLS
P . . .. . . .
. chite AATAKQNYIRALQEYERNGG- wheat
ANKLKGEYNKAIAAYNKGESA trybr AEKDKERYKREM---------
mouse AKDDRIRYDNEMKSWEEQMAE . .

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Why Is It Difficult To Compute A multiple
Sequence Alignment?
BIOLOGY
What is A
GOOD
Alignment?
chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGG
ELWRGLKD wheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKS
VAAVGKAAGERWKSLSE trybr KKDSNAPKRAMTSFMFFSSDFRS--
--KHSDLS-IVEMSKAAGAAWKELGP mouse
-----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLS
P . . .. . . .
.
COMPUTATION
What is
THE
good Alignment?
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Why Is It Difficult To Compute A multiple
Sequence Alignment
BIOLOGY
COMPUTATION
CIRCULAR PROBLEM....
Good
Good
Alignment
Sequences
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The Computational Problem
2 Globins gt1 sec
3 Globins gt2 mn
4 Globins gt5 hours
5 Globins gt3 weeks
6 Globins gt9 years
7 Globins gt1000 years
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Existing Solution
1-Carillo and Lipman
-MSA, DCA.
-Few Small Closely Related Sequence.
-Do Well When They Can Run.
2-Segment Based
-DIALIGN, MACAW.
-May Align Too Few Residues
3-Iterative
-HMMs, HMMER, SAM.
-Slow, Sometimes Inaccurate
-Good Profile Generators
4-Progressive
-ClustalW, Pileup, Multalign
-Fast and Sensitive
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Progressive Alignment
Feng and Dolittle, 1980 Taylor 1981
Dynamic Programming Using A Substitution Matrix
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SAGA
Biological Objective Function
SAGA
Alignment
?
Biological Quality
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The Biological Problem. The Charlie Chaplin
Paradox
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An Alignment is a STORY
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Comparing Sequences Reconstructing Evolution
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Most Common Objective Function Sums of Pairs
ModelEvery sequence is the ancestor of every
sequence
PROBLEM -over-estimation of the mutation
costs -Requires a weighting scheme
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T-Coffee A Fast Heuristic for SAGA-COFFEE
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Progressive Alignment Principle and its
Limitations
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The Extended Library Principle
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The Extended Library Principle
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The Extended Library Principle
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The Triplet Assumption
SEQ A
SEQ B
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T-Coffee Progressive Alignment
Notredame, Higgins, Heringa, 2000
Dynamic Programming Using The extended Library
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Mixing Local and Global Alignments
Local Alignment
Global Alignment
Extension
Multiple Sequence Alignment
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Validation Using BaliBase
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Mixing Heterogenous Information With T-Coffee
Local Alignment
Global Alignment
Multiple Alignment
Structural
Specialist
Extension
Multiple Sequence Alignment
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Why Using GAs
Time
SAGA
SAGA-COFFEE
T-COFFEE
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Des Higgins, UCC, Ireland Jaap Heringa, MRC,
UK Liisa Holm, EMBL-EBI, UK Orla OSullivan,
UCC Chantal Abergel, IGS, France
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