html5-img
1 / 41

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?

darci
Download Presentation

Multiple alignments, PATTERNS, PSI-BLAST

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related