overview - vital-it · 2013. 6. 28. · 8 globins =>150 000 years how to align many sequences?...
TRANSCRIPT
CN+LF-2005.02
An introduction to multiple alignments
© Cédric Notredame
Swiss Institute of Bioinformatics
CN+LF-2005.02
Overview
Multiple alignmentsHow-to, Goal, problems, use
PatternsPROSITE database, syntax, use
PSI-BLASTBLAST, matrices, use
[ Profiles/HMMs ] …
CN+LF-2005.02
Overview
What are multiple alignments?How can I use my alignments?How does the computer align the sequences?
The progressive alignment algorithmWhat are the difficulties?Pre-requisite?
How can we compare sequences?How can we align sequences?
CN+LF-2005.02
Sometimes two sequences are not enough
The man with TWO watches NEVER knows the exact time
CN+LF-2005.02
What is a multiple sequence alignment?
What can it do for me?How can I produce one of these?How can I use it?
chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKDwheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSEtrybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGPmouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP
***. ::: .: .. . : . . * . *: *
chite AATAKQNYIRALQEYERNGG-wheat ANKLKGEYNKAIAAYNKGESAtrybr AEKDKERYKREM---------mouse AKDDRIRYDNEMKSWEEQMAE
* : .* . :
CN+LF-2005.02
What is a multiple sequence alignment?
Structural/biochemical criteriaResidues playing a similar role end up in the same column.
Evolution criteriaResidues having the same ancestor end up in the same column.
chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKDwheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSEtrybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGPmouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP
***. ::: .: .. . : . . * . *: *
chite AATAKQNYIRALQEYERNGG-wheat ANKLKGEYNKAIAAYNKGESAtrybr AEKDKERYKREM---------mouse AKDDRIRYDNEMKSWEEQMAE
* : .* . :
CN+LF-2005.02
CN+LF-2005.02
How can I use a multiple alignment?chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKDwheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSEtrybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGPunknown -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP
***. ::: .: .. . : . . * . *: *
chite AATAKQNYIRALQEYERNGG-wheat ANKLKGEYNKAIAAYNKGESAtrybr AEKDKERYKREM---------unknown AKDDRIRYDNEMKSWEEQMAE
* : .* . :
Extrapolation
SwissProt
Unkown Sequence
Homology?
Less Than 30 % idBUT
Conserved where it MATTERS
CN+LF-2005.02
How can I use a multiple alignment?chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKDwheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSEtrybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGPmouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP
***. ::: .: .. . : . . * . *: *
chite AATAKQNYIRALQEYERNGG-wheat ANKLKGEYNKAIAAYNKGESAtrybr AEKDKERYKREM---------mouse AKDDRIRYDNEMKSWEEQMAE
* : .* . :
Extrapolation
Prosite Patterns
P-K-R-[PA]-x(1)-[ST]…
CN+LF-2005.02
How can I use a multiple alignment?
Extrapolation
Prosite Patterns
chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKDwheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSEtrybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGPmouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-IQGKLKLVNEAWKNLSP
***. ::: .: .. . : . . * . *: *
chite AATAKQNYIRALQEYERNGG-wheat ANKLKGEYNKAIAAYNKGESAtrybr AEKDKERYKREM---------mouse AKDDRIRYDNEMKSWEEQMAE
* : .* . :
L?K>R
Prosite Profiles -More Sensitive-More Specific
AFDEFGHQIVLW
CN+LF-2005.02
PROSITE profile (see also HMMs)
A Substitution Cost For Every Amino Acid, At Every Position
CN+LF-2005.02
How can I use a multiple alignment?
Phylogeny
chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKDwheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSEtrybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGPmouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP
***. ::: .: .. . : . . * . *: *
chite AATAKQNYIRALQEYERNGG-wheat ANKLKGEYNKAIAAYNKGESAtrybr AEKDKERYKREM---------mouse AKDDRIRYDNEMKSWEEQMAE
* : .* . :
chite
wheat
trybr
mouse
-Evolution-Paralogy/Orthology
CN+LF-2005.02
How can I use a multiple alignment?
Phylogeny
chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKDwheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSEtrybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGPmouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP
***. ::: .: .. . : . . * . *: *
chite AATAKQNYIRALQEYERNGG-wheat ANKLKGEYNKAIAAYNKGESAtrybr AEKDKERYKREM---------mouse AKDDRIRYDNEMKSWEEQMAE
* : .* . :
Struc. Prediction
Column Constraint
Evolution Constraint
Structure Constraint
CN+LF-2005.02
How can I use a multiple alignment?
Phylogeny
chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKDwheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSEtrybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGPmouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP
***. ::: .: .. . : . . * . *: *
chite AATAKQNYIRALQEYERNGG-wheat ANKLKGEYNKAIAAYNKGESAtrybr AEKDKERYKREM---------mouse AKDDRIRYDNEMKSWEEQMAE
* : .* . :
Struc. Prediction
PsiPred or PhDFor secondary Structure Prediction: 75% Accurate.Threading: is improving but is not yet as good.
CN+LF-2005.02
How can I use a multiple alignment?
Phylogeny
Struc. Prediction
chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKDwheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSEtrybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGPmouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP
***. ::: .: .. . : . . * . *: *
chite AATAKQNYIRALQEYERNGG-wheat ANKLKGEYNKAIAAYNKGESAtrybr AEKDKERYKREM---------mouse AKDDRIRYDNEMKSWEEQMAE
* : .* . :
Caution!
Automatic MultipleSequence Alignment methodsare not always perfect…
CN+LF-2005.02
CN+LF-2005.02
The problem
why is it difficult to compute a multiple sequence alignment?
chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKDwheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSEtrybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGPmouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP
***. ::: .: .. . : . . * . *: *
Computation
What is the good alignment?
Biology
What is a good alignment?
CN+LF-2005.02
The problem
why is it difficult to compute a multiple sequence alignment?
CIRCULAR PROBLEM....
GoodSequences
GoodAlignment
CN+LF-2005.02
The problem
Same as pairwise alignment problemWe 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…
CN+LF-2005.02
The Charlie Chaplin paradox
CN+LF-2005.02
What do I need to know to make a good multiple alignment?
How do sequences evolve?How does the computer align the sequences?How can I choose my sequences?What is the best program?How can I use my alignment?
CN+LF-2005.02
An alignment is a story
ADKPKRPLSAYMLWLN
ADKPKRPLSAYMLWLN ADKPKRPLSAYMLWLN
ADKPRRPLS-YMLWLNADKPKRPKPRLSAYMLWLN
Mutations+
Selection
ADKPRRP---LS-YMLWLNADKPKRPKPRLSAYMLWLN
InsertionDeletion
Mutation
CN+LF-2005.02
Homology
Same sequences -> same origin? -> same function? -> same 3D fold?
Length
%Sequence Identity
30%
100
Same 3D Fold
Twilight Zone
CN+LF-2005.02
Convergent evolution
AFGP with (ThrAlaAla)nSimilar To Trypsinogen
AFGP with (ThrAlaAla)nNOT
Similar to Trypsinogen
N
S
Chen et al, 97, PNAS, 94, 3811-16
CN+LF-2005.02
Residues and mutations
All residues are equal, but some more than others…
PG
SC
LI
T
V A
W
YF QH
K
R
ED N
Aliphatic
Aromatic
Hydrophobic
Polar
SmallM
Accurate matrices are data driven rather than knowledge driven
G
C
CN+LF-2005.02
Substitution matrices
Different Flavors:
• Pam: 250, 350• Blosum: 45, 62• …
CN+LF-2005.02
What is the best substition matrix?
Mutation rates depend on families
Choosing the right matrix may be trickyGonnet250 > BLOSUM62 > PAM250 Depends on the family, the program used and its tuning
Family S N Histone3 6.4 0Insulin 4.0 0.1Interleukin I 4.6 1.4α−Globin 5.1 0.6Apolipoprot. AI 4.5 1.6Interferon G 8.6 2.8
Rates in Substitutions/site/Billion Years as measured on Mouse Vs Human (0.08 Billion years)
CN+LF-2005.02
Insertions and deletions?
Indel Cost
L
Cost
L
Cost
L
Affine Gap PenaltyCost=GOP+GEP*L
CN+LF-2005.02
How to align many sequences?
Exact algorithms are computing time consumingNeedlemann & WunschSmith & Waterman
2 Globins =>1 sec
CN+LF-2005.02
3 Globins =>2 mn
How to align many sequences?
Exact algorithms are computing time consumingNeedlemann & WunschSmith & Waterman
CN+LF-2005.02
4 Globins =>5 hours
How to align many sequences?
Exact algorithms are computing time consumingNeedlemann & WunschSmith & Waterman
-> heuristic wished
CN+LF-2005.02
5 Globins =>3 weeks
How to align many sequences?
Exact algorithms are computing time consumingNeedlemann & WunschSmith & Waterman
-> heuristic really wished!
CN+LF-2005.02
6 Globins =>9 years
How to align many sequences?
Exact algorithms are computing time consumingNeedlemann & WunschSmith & Waterman
-> heuristic required!
CN+LF-2005.02
How to align many sequences?
Exact algorithms are computing time consumingNeedlemann & WunschSmith & Waterman
-> heuristic definitely required!
7 Globins =>1000 years
CN+LF-2005.02
8 Globins =>150 000 years
How to align many sequences?
Exact algorithms are computing time consumingNeedlemann & WunschSmith & Waterman
-> heuristic please!…
CN+LF-2005.02
Existing methods1-Carillo and Lipman:
-MSA, DCA.
-Few Small Closely Related Sequence.
2-Segment Based:
-DIALIGN, MACAW.
-May Align Too Few Residues
-Do Well When They Can Run.
3-Iterative:-HMMs, HMMER, SAM.
-Slow, Sometimes Inacurate
-Good Profile Generators
4-Progressive:
-ClustalW, Pileup, Multalign…
-Fast and Sensitive
CN+LF-2005.02
Progressive alignmentFeng and Dolittle, 1980; Taylor 1981
Dynamic Programming Using A Substitution Matrix
CN+LF-2005.02
Progressive alignmentFeng and Dolittle, 1980; Taylor 1981
-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.
CN+LF-2005.02
Progressive alignment
Works well when phylogeny is denseNo outlayer sequenceExample: river crossing
CN+LF-2005.02
Selecting sequences from a BLAST output
CN+LF-2005.02
A common mistake
Sequences too closely related
Identical sequences brings no informationMultiple sequence alignments thrive on diversity
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
:*** ******.******.**** *:************.:******:**
CN+LF-2005.02
CN+LF-2005.02
Respect information!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
:. . * .*..:*: *: * *. :::..:*:::**: .*:*: :** :
PRVA_MACFU LKGFSPDARDLSAKETKTLMAAGDKDGDGKIGVDEFSTLVAES-PRVA_HUMAN LKGFSPDARDLSAKETKMLMAAGDKDGDGKIGVDEFSTLVAES-PRVA_GERSP LKGFSSDARDLSAKETKTLLAAGDKDGDGKIGVEEFSTLVSES-PRVA_MOUSE LKGFSSDARDLSAKETKTLLAAGDKDGDGKIGVEEFSTLVAES-PRVA_RAT LKGFSSDARDLSAKETKTLMAAGDKDGDGKIGVEEFSTLVAES-PRVA_RABIT LKGFSPDARDLSVKETKTLMAAGDKDGDGKIGADEFSTLVSES-TPCC_MOUSE LQ---ATGETITEDDIEELMKDGDKNNDGRIDYDEFLEFMKGVE
*: . .. :: .: : *: ***:.**:*. :** ::
-This alignment is not informative about the relation between TPCC MOUSE and the rest of the sequences.
-A better spread of the sequences is needed
CN+LF-2005.02
Selecting diverse sequences
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.
CN+LF-2005.02
Aligning remote homologuesPRVA_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
:: .. :: : :: .* :.** *. :** ::
CN+LF-2005.02
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
: . :: : :: * :..* :. :** ::
CN+LF-2005.02
What makes a good alignment…
The more divergent the sequences, the betterThe fewer indels, the betterNice ungapped blocks separated with indelsDifferent classes of residues within a block:
Completely conserved (*)Size and hydropathy conserved (:)Size or hydropathy conserved (.)
The ultimate evaluation is a matter of personaljudgment and knowledge
CN+LF-2005.02
Avoiding pitfalls
CN+LF-2005.02
Naming your sequences the right way
Never use white spaces in your sequence namesNever use special symbols. Stick to plain letters, numbers and the underscore sign (_) to replace spaces. Avoid ALL other signs, especially the mosttempting ones like @, #, |, *, >, <…Never use names longer than 15 charactersNever give the same name to 2 different sequencesin your set. Some programs accept it, others likeClustalW don’t.
CN+LF-2005.02
Do not use too many sequences!
CN+LF-2005.02
Beware of RepeatsThere is a problem when two sequences do not contain the same number ofrepeats
It is then better to manually extarct the repeats and to align them separately. Individual repeats can be recognized using Dotlet or Dotter.
CN+LF-2005.02
Keep a biological perspectivechite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKDwheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSEtrybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGPmouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP
***. ::: .: .. . : . . * . *: *
chite AATAKQNYIRALQEYERNGG-wheat ANKLKGEYNKAIAAYNKGESAtrybr AEKDKERYKREM---------mouse AKDDRIRYDNEMKSWEEQMAE
* : .* . :
chite AD--K----PKR-PLYMLWLNS-ARESIKRENPDFK-VT-EVAKKGGELWRGL-wheat -DPNK----PKRAP-FFVFMGE-FREEFKQKNPKNKSVA-AVGKAAGERWKSLStrybr -K--KDSNAPKR-AMT-MFFSSDFR-S-KH-S-DLS-IV-EMSKAAGAAWKELG mouse ----K----PKR-PRYNIYVSESFQEA-K--D-D-S-AQGKL-KLVNEAWKNLS
* *** .:: ::... : * . . . : * . *: *
chite KSEWEAKAATAKQNY-I--RALQE-YERNG-G-wheat KAPYVAKANKLKGEY-N--KAIAA-YNK-GESAtrybr RKVYEEMAEKDKERY----K--RE-M-------mouse KQAYIQLAKDDRIRYDNEMKSWEEQMAE-----
: : * : .* :
DIFFERENTPARAMETERS
CN+LF-2005.02
Do not overtune!!!
DO NOT PLAY WITHPARAMETERS!
IF YOU KNOW THE ALIGNMENT YOU
WANT: MAKE IT YOURSELF!
chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKDwheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSEtrybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGPmouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP
***. ::: .: .. . : . . * . *: *
chite AATAKQNYIRALQEYERNGG-wheat ANKLKGEYNKAIAAYNKGESAtrybr AEKDKERYKREM---------mouse AKDDRIRYDNEMKSWEEQMAE
* : .* . :
chite ---ADKPKRPL-SAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKDwheat --DPNKPKRAP-SAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSEtrybr KKDSNAPKRAMTSFMFFSSDFRS-----KHSDLS-IVEMSKAAGAAWKELGPmouse -----KPKRPR-SAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP
***. * .: .. . : . . * . *: *
chite AATAKQNYIRALQEYERNGG-wheat ANKLKGEYNKAIAAYNKGESAtrybr AEKDKERYKREM---------mouse AKDDRIRYDNEMKSWEEQMAE
* : .* . :
CN+LF-2005.02
BaliBase classification and benchmarkDescriptionPROBLEM
EvenPhylogenicSpread.
One OutlayerSequence
Two Distantlyrelated Groups
Long InternalIndel
Long Terminal Indel
CN+LF-2005.02
Choosing the right method
Source: BaliBase
Thompson et al, NAR, 1999
PROBLEM Strategy Strategy
ClustalW, T-coffee,MSA, DCA
PrrP,T-Coffee
Dialign II
T-Coffee
T-Coffee
Dialign II
T-Coffee
CN+LF-2005.02
Some interesting links
CN+LF-2005.02
Conclusion
The best alignment method:Your brainThe right data
The best evaluation method:Your eyesExperimental information (SwissProt)
Choosing the sequences well isimportantBeware of repeated elements
What can I conclude?Homology -> information extrapolation
How can I go further?PatternsProfilesHMMs…