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Multiple alignments, PATTERNS, PSI-BLAST

Multiple alignments, PATTERNS, PSI-BLAST. Overview. Multiple alignments How-to, Goal, problems, use Patterns PROSITE database, syntax, use PSI-BLAST BLAST, matrices, use [ Profiles/HMMs ] …. What is a multiple sequence alignment?. What can it do for me? How can I produce one of these?

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Multiple alignments, PATTERNS, PSI-BLAST

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  1. Multiple alignments, PATTERNS, PSI-BLAST

  2. Overview • Multiple alignments • How-to, Goal, problems, use • Patterns • PROSITE database, syntax, use • PSI-BLAST • BLAST, matrices, use • [ Profiles/HMMs ] …

  3. 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. 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 Homology? SwissProt Unkown Sequence

  5. How can I use a multiple alignment? chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD wheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE trybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP mouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP ***. ::: .: .. . : . . * . *: * chite AATAKQNYIRALQEYERNGG- wheat ANKLKGEYNKAIAAYNKGESA trybr AEKDKERYKREM--------- mouse AKDDRIRYDNEMKSWEEQMAE * : .* . : Extrapolation SwissProt Prosite Patterns Match? Unkown Sequence

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

  7. How can I use a multiple alignment? chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD wheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE trybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP mouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP ***. ::: .: .. . : . . * . *: * chite AATAKQNYIRALQEYERNGG- wheat ANKLKGEYNKAIAAYNKGESA trybr AEKDKERYKREM--------- mouse AKDDRIRYDNEMKSWEEQMAE * : .* . : Phylogeny chite wheat -Evolution -Paralogy/Orthology trybr mouse

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

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

  10. The problem • why is it difficult to compute a multiple sequence alignment? Biology What is a good alignment? chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD wheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE trybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP mouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP ***. ::: .: .. . : . . * . *: * Computation What is the good alignment?

  11. The problem • why is it difficult to compute a multiple sequence alignment? CIRCULAR PROBLEM.... Good Good Alignment Sequences

  12. 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. An alignment is a story Deletion Insertion ADKPKRPLSAYMLWLN ADKPRRP---LS-YMLWLN ADKPKRPKPRLSAYMLWLN Mutation Mutations + Selection ADKPKRPLSAYMLWLN ADKPKRPLSAYMLWLN ADKPKRPKPRLSAYMLWLN ADKPRRPLS-YMLWLN

  14. Homology • Same sequences -> same origin? -> same function? -> same 3D fold? %Sequence Identity Same 3D Fold 30% Twilight Zone Length 100

  15. Convergent evolution Chen et al, 97, PNAS, 94, 3811-16 AFGP with (ThrAlaAla)n Similar To Trypsynogen N S AFGP with (ThrAlaAla)n NOT Similar to Trypsinogen

  16. Residues and mutations • All residues are equal, but some more than others… M C P Small L V A G G I Aliphatic C T S D N K Y E F H Q W R Aromatic Hydrophobic Polar Accurate matrices are data driven rather than knowledge driven

  17. Substitution matrices Different Flavors: • Pam: 250, 350 • Blosum: 45, 62 • …

  18. 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 Family S N Histone3 6.4 0 Insulin 4.0 0.1 Interleukin I 4.6 1.4 a-Globin 5.1 0.6 Apolipoprot. AI 4.5 1.6 Interferon G 8.6 2.8 Rates in Substitutions/site/Billion Years as measured on Mouse Vs Human (0.08 Billion years)

  19. Insertions and deletions? Affine Gap Penalty Indel Cost Cost=GOP+GEP*L Cost L Cost L L

  20. 2 Globins =>1 sec How to align many sequences? • Exact algorithms are computing time consuming • Needlemann & Wunsch • Smith & Waterman • -> heuristic required!

  21. 3 Globins =>2 mn How to align many sequences? • Exact algorithms are computing time consuming • Needlemann & Wunsch • Smith & Waterman • -> heuristic required!

  22. 4 Globins =>5 hours How to align many sequences? • Exact algorithms are computing time consuming • Needlemann & Wunsch • Smith & Waterman • -> heuristic required!

  23. 5 Globins =>3 weeks How to align many sequences? • Exact algorithms are computing time consuming • Needlemann & Wunsch • Smith & Waterman • -> heuristic required!

  24. 6 Globins =>9 years How to align many sequences? • Exact algorithms are computing time consuming • Needlemann & Wunsch • Smith & Waterman • -> heuristic required!

  25. 7 Globins =>1000 years How to align many sequences? • Exact algorithms are computing time consuming • Needlemann & Wunsch • Smith & Waterman • -> heuristic required!

  26. 8 Globins =>150 000 years How to align many sequences? • Exact algorithms are computing time consuming • Needlemann & Wunsch • Smith & Waterman • -> heuristic required!

  27. Existing methods 1-Carillo and Lipman: -MSA, DCA. -Few Small Closely Related Sequence. -Do Well When They Can Run. 2-Segment Based: 4-Progressive: -ClustalW, Pileup, Multalign… -DIALIGN, MACAW. -May Align Too Few Residues -Fast and Sensitive 3-Iterative: -HMMs, HMMER, SAM. -Slow, Sometimes Inacurate -Good Profile Generators

  28. Progressive alignment Feng and Dolittle, 1980; Taylor 1981 Dynamic Programming Using A Substitution Matrix

  29. Progressive alignment Feng and Dolittle, 1980; Taylor 1981 -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.

  30. Selecting sequences from a BLAST output

  31. 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. 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. 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. 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. 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. 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. Avoiding pitfalls

  38. 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 * : .* . : DIFFERENT PARAMETERS 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----- : : * : .* :

  39. Do not overtune!!! chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD wheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE trybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP mouse -----KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP ***. ::: .: .. . : . . * . *: * chite AATAKQNYIRALQEYERNGG- wheat ANKLKGEYNKAIAAYNKGESA trybr AEKDKERYKREM--------- mouse AKDDRIRYDNEMKSWEEQMAE * : .* . : DO NOT PLAY WITH PARAMETERS! IF YOU KNOW THE ALIGNMENT YOU WANT: MAKE IT YOURSELF! 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. PROGRAM METHOD PROBLEM ClustalW ClustalW MSA DIALIGN II DIALIGN II Choosing the right method Source: BaliBase Thompson et al, NAR, 1999

  41. 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 … Conclusion

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