1. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 Multiple alignments, PATTERNS, PSI-BLAST

2. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 Overview Multiple alignments How-to, Goal, problems, use Patterns PROSITE database, syntax, use PSI-BLAST BLAST, matrices, use [ Profiles/HMMs ] …

3. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 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-VTEVAKKGGELWRGLKD wheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE trybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP mouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP ***. :::.:... :.. *. *: * chite AATAKQNYIRALQEYERNGG- wheat ANKLKGEYNKAIAAYNKGESA trybr AEKDKERYKREM--------- mouse AKDDRIRYDNEMKSWEEQMAE * :.*. :

4. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 How can I use a multiple alignment? chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD wheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE trybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP unknown -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP ***. :::.:... :.. *. *: * chite AATAKQNYIRALQEYERNGG- wheat ANKLKGEYNKAIAAYNKGESA trybr AEKDKERYKREM--------- unknown AKDDRIRYDNEMKSWEEQMAE * :.*. : Extrapolation SwissProt Unkown Sequence Homology?

5. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 How can I use a multiple alignment? SwissProt Unkown Sequence Match? chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD wheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE trybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP mouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP ***. :::.:... :.. *. *: * chite AATAKQNYIRALQEYERNGG- wheat ANKLKGEYNKAIAAYNKGESA trybr AEKDKERYKREM--------- mouse AKDDRIRYDNEMKSWEEQMAE * :.*. : Extrapolation Prosite Patterns

6. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 How can I use a multiple alignment? Extrapolation Prosite Patterns chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD wheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE trybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP mouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-IQGKLKLVNEAWKNLSP ***. :::.:... :.. *. *: * chite AATAKQNYIRALQEYERNGG- wheat ANKLKGEYNKAIAAYNKGESA trybr AEKDKERYKREM--------- mouse AKDDRIRYDNEMKSWEEQMAE * :.*. : L? K>R Prosite Profiles -More Sensitive -More Specific A F D E F G H Q I V L W

7. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 How can I use a multiple alignment? Phylogeny chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD wheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE trybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP mouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP ***. :::.:... :.. *. *: * chite AATAKQNYIRALQEYERNGG- wheat ANKLKGEYNKAIAAYNKGESA trybr AEKDKERYKREM--------- mouse AKDDRIRYDNEMKSWEEQMAE * :.*. : chite wheat trybr mouse -Evolution -Paralogy/Orthology

8. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 How can I use a multiple alignment? Phylogeny chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD wheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE trybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP mouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP ***. :::.:... :.. *. *: * chite AATAKQNYIRALQEYERNGG- wheat ANKLKGEYNKAIAAYNKGESA trybr AEKDKERYKREM--------- mouse AKDDRIRYDNEMKSWEEQMAE * :.*. : Struc. Prediction PhD For secondary Structure Prediction: 75% Accurate. Threading: is improving but is not yet as good.

9. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 How can I use a multiple alignment? Phylogeny Struc. Prediction chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD wheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE trybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP mouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP ***. :::.:... :.. *. *: * chite AATAKQNYIRALQEYERNGG- wheat ANKLKGEYNKAIAAYNKGESA trybr AEKDKERYKREM--------- mouse AKDDRIRYDNEMKSWEEQMAE * :.*. : Caution! Automatic Multiple Sequence Alignment methods are not always perfect…

10. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 The problem why is it difficult to compute a multiple sequence alignment? chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD wheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE trybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP mouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP ***. :::.:... :.. *. *: * Computation What is the good alignment? Biology What is a good alignment?

11. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 The problem why is it difficult to compute a multiple sequence alignment? CIRCULAR PROBLEM.... Good Sequences Good Alignment

12. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 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?

13. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 An alignment is a story ADKPKRPLSAYMLWLN ADKPRRPLS-YMLWLN ADKPKRPKPRLSAYMLWLN Mutations + Selection ADKPRRP---LS-YMLWLN ADKPKRPKPRLSAYMLWLN Insertion Deletion Mutation

14. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 Homology Same sequences -> same origin? -> same function? -> same 3D fold? Length %Sequence Identity 30% 100 Same 3D Fold Twilight Zone

15. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 Convergent evolution AFGP with (ThrAlaAla)n Similar To Trypsynogen AFGP with (ThrAlaAla)n NOT Similar to Trypsinogen N S Chen et al, 97, PNAS, 94, 3811-16

16. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 Residues and mutations All residues are equal, but some more than others… P G S C L I T V A W Y F Q H K R E DN Aliphatic Aromatic Hydrophobic Polar Small M Accurate matrices are data driven rather than knowledge driven G C

17. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 Substitution matrices Different Flavors: Pam: 250, 350 Blosum: 45, 62 …

18. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 What is the best substition matrix? Mutation rates depend on families Choosing the right matrix may be tricky Gonnet250 > BLOSUM62 > PAM250 Depends on the family, the program used and its tuning FamilySN Histone36.40 Insulin4.00.1 Interleukin I4.61.4  Globin5.10.6 Apolipoprot. AI4.51.6 Interferon G8.62.8 Rates in Substitutions/site/Billion Years as measured on Mouse Vs Human (0.08 Billion years)

19. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 Insertions and deletions? Indel Cost L Cost L L Affine Gap Penalty Cost=GOP+GEP*L

20. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 How to align many sequences? Exact algorithms are computing time consuming Needlemann & Wunsch Smith & Waterman -> heuristic required! 2 Globins =>1 sec

21. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 3 Globins =>2 mn How to align many sequences? Exact algorithms are computing time consuming Needlemann & Wunsch Smith & Waterman -> heuristic required!

22. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 4 Globins =>5 hours How to align many sequences? Exact algorithms are computing time consuming Needlemann & Wunsch Smith & Waterman -> heuristic required!

23. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 5 Globins =>3 weeks How to align many sequences? Exact algorithms are computing time consuming Needlemann & Wunsch Smith & Waterman -> heuristic required!

24. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 6 Globins =>9 years How to align many sequences? Exact algorithms are computing time consuming Needlemann & Wunsch Smith & Waterman -> heuristic required!

25. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 How to align many sequences? Exact algorithms are computing time consuming Needlemann & Wunsch Smith & Waterman -> heuristic required! 7 Globins =>1000 years

26. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 8 Globins =>150 000 years How to align many sequences? Exact algorithms are computing time consuming Needlemann & Wunsch Smith & Waterman -> heuristic required!

27. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 Existing methods 1-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

28. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 Progressive alignment Feng and Dolittle, 1980; Taylor 1981 Dynamic Programming Using A Substitution Matrix

29. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 Progressive alignment Feng 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.

30. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 Selecting sequences from a BLAST output

31. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 A common mistake Sequences too closely related Identical sequences brings no information Multiple sequence alignments thrive on diversity PRVA_MACFU SMTDLLNAEDIKKAVGAFSAIDSFDHKKFFQMVGLKKKSADDVKKVFHILDKDKSGFIEE PRVA_HUMAN SMTDLLNAEDIKKAVGAFSATDSFDHKKFFQMVGLKKKSADDVKKVFHMLDKDKSGFIEE PRVA_GERSP SMTDLLSAEDIKKAIGAFAAADSFDHKKFFQMVGLKKKTPDDVKKVFHILDKDKSGFIEE PRVA_MOUSE SMTDVLSAEDIKKAIGAFAAADSFDHKKFFQMVGLKKKNPDEVKKVFHILDKDKSGFIEE PRVA_RAT SMTDLLSAEDIKKAIGAFTAADSFDHKKFFQMVGLKKKSADDVKKVFHILDKDKSGFIEE PRVA_RABIT AMTELLNAEDIKKAIGAFAAAESFDHKKFFQMVGLKKKSTEDVKKVFHILDKDKSGFIEE :**::*.*******:***:* :****************..::******:*********** PRVA_MACFU DELGFILKGFSPDARDLSAKETKTLMAAGDKDGDGKIGVDEFSTLVAES PRVA_HUMAN DELGFILKGFSPDARDLSAKETKMLMAAGDKDGDGKIGVDEFSTLVAES PRVA_GERSP DELGFILKGFSSDARDLSAKETKTLLAAGDKDGDGKIGVEEFSTLVSES PRVA_MOUSE DELGSILKGFSSDARDLSAKETKTLLAAGDKDGDGKIGVEEFSTLVAES PRVA_RAT DELGSILKGFSSDARDLSAKETKTLMAAGDKDGDGKIGVEEFSTLVAES PRVA_RABIT EELGFILKGFSPDARDLSVKETKTLMAAGDKDGDGKIGADEFSTLVSES :*** ******.******.**** *:************.:******:**

32. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 Respect information! PRVA_MACFU ------------------------------------------SMTDLLN----AEDIKKA PRVA_HUMAN ------------------------------------------SMTDLLN----AEDIKKA PRVA_GERSP ------------------------------------------SMTDLLS----AEDIKKA PRVA_MOUSE ------------------------------------------SMTDVLS----AEDIKKA PRVA_RAT ------------------------------------------SMTDLLS----AEDIKKA PRVA_RABIT ------------------------------------------AMTELLN----AEDIKKA TPCC_MOUSE MDDIYKAAVEQLTEEQKNEFKAAFDIFVLGAEDGCISTKELGKVMRMLGQNPTPEELQEM : :*..*:::: PRVA_MACFU VGAFSAIDS--FDHKKFFQMVG------LKKKSADDVKKVFHILDKDKSGFIEEDELGFI PRVA_HUMAN VGAFSATDS--FDHKKFFQMVG------LKKKSADDVKKVFHMLDKDKSGFIEEDELGFI PRVA_GERSP IGAFAAADS--FDHKKFFQMVG------LKKKTPDDVKKVFHILDKDKSGFIEEDELGFI PRVA_MOUSE IGAFAAADS--FDHKKFFQMVG------LKKKNPDEVKKVFHILDKDKSGFIEEDELGSI PRVA_RAT IGAFTAADS--FDHKKFFQMVG------LKKKSADDVKKVFHILDKDKSGFIEEDELGSI PRVA_RABIT IGAFAAAES--FDHKKFFQMVG------LKKKSTEDVKKVFHILDKDKSGFIEEEELGFI TPCC_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

33. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 Selecting diverse sequences PRVB_CYPCA -AFAGVLNDADIAAALEACKAADSFNHKAFFAKVGLTSKSADDVKKAFAIIDQDKSGFIE PRVB_BOACO -AFAGILSDADIAAGLQSCQAADSFSCKTFFAKSGLHSKSKDQLTKVFGVIDRDKSGYIE PRV1_SALSA MACAHLCKEADIKTALEACKAADTFSFKTFFHTIGFASKSADDVKKAFKVIDQDASGFIE PRVB_LATCH -AVAKLLAAADVTAALEGCKADDSFNHKVFFQKTGLAKKSNEELEAIFKILDQDKSGFIE PRVB_RANES -SITDIVSEKDIDAALESVKAAGSFNYKIFFQKVGLAGKSAADAKKVFEILDRDKSGFIE PRVA_MACFU -SMTDLLNAEDIKKAVGAFSAIDSFDHKKFFQMVGLKKKSADDVKKVFHILDKDKSGFIE PRVA_ESOLU --AKDLLKADDIKKALDAVKAEGSFNHKKFFALVGLKAMSANDVKKVFKAIDADASGFIE : *:.:..*.:*. * ** *: * : * :* * **:** PRVB_CYPCA EDELKLFLQNFKADARALTDGETKTFLKAGDSDGDGKIGVDEFTALVKA- PRVB_BOACO EDELKKFLQNFDGKARDLTDKETAEFLKEGDTDGDGKIGVEEFVVLVTKG PRV1_SALSA VEELKLFLQNFCPKARELTDAETKAFLKAGDADGDGMIGIDEFAVLVKQ- PRVB_LATCH DEELELFLQNFSAGARTLTKTETETFLKAGDSDGDGKIGVDEFQKLVKA- PRVB_RANES QDELGLFLQNFRASARVLSDAETSAFLKAGDSDGDGKIGVEEFQALVKA- PRVA_MACFU EDELGFILKGFSPDARDLSAKETKTLMAAGDKDGDGKIGVDEFSTLVAES PRVA_ESOLU EEELKFVLKSFAADGRDLTDAETKAFLKAADKDGDGKIGIDEFETLVHEA :**.*:.*.* *: ** ::.* **** **::** ** -A REASONABLE model now exists. -Going further:remote homologues.

34. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 Aligning remote homologues PRVA_MACFU ------------------------------------------SMTDLLNA----EDIKKA PRVA_ESOLU -------------------------------------------AKDLLKA----DDIKKA PRVB_CYPCA ------------------------------------------AFAGVLND----ADIAAA PRVB_BOACO ------------------------------------------AFAGILSD----ADIAAG PRV1_SALSA -----------------------------------------MACAHLCKE----ADIKTA PRVB_LATCH ------------------------------------------AVAKLLAA----ADVTAA PRVB_RANES ------------------------------------------SITDIVSE----KDIDAA TPCS_RABIT -TDQQAEARSYLSEEMIAEFKAAFDMFDADGG-GDISVKELGTVMRMLGQTPTKEELDAI TPCS_PIG -TDQQAEARSYLSEEMIAEFKAAFDMFDADGG-GDISVKELGTVMRMLGQTPTKEELDAI TPCC_MOUSE MDDIYKAAVEQLTEEQKNEFKAAFDIFVLGAEDGCISTKELGKVMRMLGQNPTPEELQEM : :: PRVA_MACFU VGAFSAIDS--FDHKKFFQMVG------LKKKSADDVKKVFHILDKDKSGFIEEDELGFI PRVA_ESOLU LDAVKAEGS--FNHKKFFALVG------LKAMSANDVKKVFKAIDADASGFIEEEELKFV PRVB_CYPCA LEACKAADS--FNHKAFFAKVG------LTSKSADDVKKAFAIIDQDKSGFIEEDELKLF PRVB_BOACO LQSCQAADS--FSCKTFFAKSG------LHSKSKDQLTKVFGVIDRDKSGYIEEDELKKF PRV1_SALSA LEACKAADT--FSFKTFFHTIG------FASKSADDVKKAFKVIDQDASGFIEVEELKLF PRVB_LATCH LEGCKADDS--FNHKVFFQKTG------LAKKSNEELEAIFKILDQDKSGFIEDEELELF PRVB_RANES LESVKAAGS--FNYKIFFQKVG------LAGKSAADAKKVFEILDRDKSGFIEQDELGLF TPCS_RABIT IEEVDEDGSGTIDFEEFLVMMVRQMKEDAKGKSEEELAECFRIFDRNADGYIDAEELAEI TPCS_PIG IEEVDEDGSGTIDFEEFLVMMVRQMKEDAKGKSEEELAECFRIFDRNMDGYIDAEELAEI TPCC_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---ASGEHVTDEEIESLMKDGDKNNDGRIDFDEFLKMMEGVQ TPCS_PIG FR---ASGEHVTDEEIESIMKDGDKNNDGRIDFDEFLKMMEGVQ TPCC_MOUSE LQ---ATGETITEDDIEELMKDGDKNNDGRIDYDEFLEFMKGVE ::.. :: : ::.* :.** *. :** ::

35. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 Going further… PRVA_MACFU VGAFSAIDS--FDHKKFFQMVG------LKKKSADDVKKVFHILDKDKSGFIEEDELGFI PRVB_BOACO LQSCQAADS--FSCKTFFAKSG------LHSKSKDQLTKVFGVIDRDKSGYIEEDELKKF PRV1_SALSA LEACKAADT--FSFKTFFHTIG------FASKSADDVKKAFKVIDQDASGFIEVEELKLF TPCS_RABIT IEEVDEDGSGTIDFEEFLVMMVRQMKEDAKGKSEEELAECFRIFDRNADGYIDAEELAEI TPCS_PIG IEEVDEDGSGTIDFEEFLVMMVRQMKEDAKGKSEEELAECFRIFDRNMDGYIDAEELAEI TPCC_MOUSE IDEVDEDGSGTVDFDEFLVMMVRCMKDDSKGKSEEELSDLFRMFDKNADGYIDLDELKMM TPC_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 :. :: : :: * :..* :. :** ::

36. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 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 Size and hydropathy conserved Size or hydropathy conserved The ultimate evaluation is a matter of personal judgment and knowledge

37. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 Avoiding pitfalls

38. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 Keep a biological perspective chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD wheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE trybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP mouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP ***. :::.:... :.. *. *: * chite AATAKQNYIRALQEYERNGG- wheat ANKLKGEYNKAIAAYNKGESA trybr AEKDKERYKREM--------- mouse AKDDRIRYDNEMKSWEEQMAE * :.*. : chite AD--K----PKR-PLYMLWLNS-ARESIKRENPDFK-VT-EVAKKGGELWRGL- wheat -DPNK----PKRAP-FFVFMGE-FREEFKQKNPKNKSVA-AVGKAAGERWKSLS trybr -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-GESA trybr RKVYEEMAEKDKERY----K--RE-M------- mouse KQAYIQLAKDDRIRYDNEMKSWEEQMAE----- : : * :.* : DIFFERENT PARAMETERS

39. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 Do not overtune!!! DO NOT PLAY WITH PARAMETERS! IF YOU KNOW THE ALIGNMENT YOU WANT: MAKE IT YOURSELF! chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD wheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE trybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP mouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP ***. :::.:... :.. *. *: * chite AATAKQNYIRALQEYERNGG- wheat ANKLKGEYNKAIAAYNKGESA trybr AEKDKERYKREM--------- mouse AKDDRIRYDNEMKSWEEQMAE * :.*. : chite ---ADKPKRPL-SAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD wheat --DPNKPKRAP-SAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE trybr KKDSNAPKRAMTSFMFFSSDFRS-----KHSDLS-IVEMSKAAGAAWKELGP mouse -----KPKRPR-SAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP ***. *.:... :.. *. *: * chite AATAKQNYIRALQEYERNGG- wheat ANKLKGEYNKAIAAYNKGESA trybr AEKDKERYKREM--------- mouse AKDDRIRYDNEMKSWEEQMAE * :.*. :

40. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 Choosing the right method PROBLEM PROGRAM ClustalW MSA DIALIGN II METHOD Source: BaliBase Thompson et al, NAR, 1999

41. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2002.10 Conclusion The best alignment method: Your brain The right data The best evaluation method: Your eyes Experimental information (SwissProt) What can I conclude? Homology -> information extrapolation How can I go further? Patterns Profiles HMMs …