Burkhard Morgenstern Institut fr Mikrobiologie und Genetik Grundlagen
![Burkhard Morgenstern Institut für Mikrobiologie und Genetik Grundlagen der Bioinformatik Multiples Sequenzalignment Juni 2007 Burkhard Morgenstern Institut für Mikrobiologie und Genetik Grundlagen der Bioinformatik Multiples Sequenzalignment Juni 2007](https://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-1.jpg)
Burkhard Morgenstern Institut für Mikrobiologie und Genetik Grundlagen der Bioinformatik Multiples Sequenzalignment Juni 2007
![`Progressive´ Alignment Most popular approach to (global) multiple sequence alignment: Progressive Alignment Since mid-Eighties: `Progressive´ Alignment Most popular approach to (global) multiple sequence alignment: Progressive Alignment Since mid-Eighties:](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-2.jpg)
`Progressive´ Alignment Most popular approach to (global) multiple sequence alignment: Progressive Alignment Since mid-Eighties: Feng/Doolittle, Higgins/Sharp, Taylor, …
![`Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVN WWRLNDKEGYVPRNLLGLYP AVVIQDNSDIKVVPKAKIIRD YAVESEAHPGSFQPVAALERIN WLNYNETTGERGDFPGTYVEYIGRKKISP `Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVN WWRLNDKEGYVPRNLLGLYP AVVIQDNSDIKVVPKAKIIRD YAVESEAHPGSFQPVAALERIN WLNYNETTGERGDFPGTYVEYIGRKKISP](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-3.jpg)
`Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVN WWRLNDKEGYVPRNLLGLYP AVVIQDNSDIKVVPKAKIIRD YAVESEAHPGSFQPVAALERIN WLNYNETTGERGDFPGTYVEYIGRKKISP
![`Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVN WWRLNDKEGYVPRNLLGLYP AVVIQDNSDIKVVPKAKIIRD YAVESEAHPGSFQPVAALERIN WLNYNETTGERGDFPGTYVEYIGRKKISP Guide tree `Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVN WWRLNDKEGYVPRNLLGLYP AVVIQDNSDIKVVPKAKIIRD YAVESEAHPGSFQPVAALERIN WLNYNETTGERGDFPGTYVEYIGRKKISP Guide tree](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-4.jpg)
`Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVN WWRLNDKEGYVPRNLLGLYP AVVIQDNSDIKVVPKAKIIRD YAVESEAHPGSFQPVAALERIN WLNYNETTGERGDFPGTYVEYIGRKKISP Guide tree
![`Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVN WW--RLNDKEGYVPRNLLGLYPAVVIQDNSDIKVVP--KAKIIRD YAVESEASFQPVAALERIN WLNYNEERGDFPGTYVEYIGRKKISP Profile alignment, “once a gap - always a `Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVN WW--RLNDKEGYVPRNLLGLYPAVVIQDNSDIKVVP--KAKIIRD YAVESEASFQPVAALERIN WLNYNEERGDFPGTYVEYIGRKKISP Profile alignment, “once a gap - always a](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-5.jpg)
`Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVN WW--RLNDKEGYVPRNLLGLYPAVVIQDNSDIKVVP--KAKIIRD YAVESEASFQPVAALERIN WLNYNEERGDFPGTYVEYIGRKKISP Profile alignment, “once a gap - always a gap”
![`Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVN WW--RLNDKEGYVPRNLLGLYPAVVIQDNSDIKVVP--KAKIIRD YAVESEASVQ--PVAALERIN-----WLN-YNEERGDFPGTYVEYIGRKKISP Profile alignment, “once a gap - always a gap” `Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVN WW--RLNDKEGYVPRNLLGLYPAVVIQDNSDIKVVP--KAKIIRD YAVESEASVQ--PVAALERIN-----WLN-YNEERGDFPGTYVEYIGRKKISP Profile alignment, “once a gap - always a gap”](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-6.jpg)
`Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVN WW--RLNDKEGYVPRNLLGLYPAVVIQDNSDIKVVP--KAKIIRD YAVESEASVQ--PVAALERIN-----WLN-YNEERGDFPGTYVEYIGRKKISP Profile alignment, “once a gap - always a gap”
![`Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVNWW--RLNDKEGYVPRNLLGLYPAVVIQDNSDIKVVP--KAKIIRD YAVESEASVQ--PVAALERIN-----WLN-YNEERGDFPGTYVEYIGRKKISP Profile alignment, “once a gap - always a gap” `Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVNWW--RLNDKEGYVPRNLLGLYPAVVIQDNSDIKVVP--KAKIIRD YAVESEASVQ--PVAALERIN-----WLN-YNEERGDFPGTYVEYIGRKKISP Profile alignment, “once a gap - always a gap”](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-7.jpg)
`Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVNWW--RLNDKEGYVPRNLLGLYPAVVIQDNSDIKVVP--KAKIIRD YAVESEASVQ--PVAALERIN-----WLN-YNEERGDFPGTYVEYIGRKKISP Profile alignment, “once a gap - always a gap”
![`Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVN-------WW--RLNDKEGYVPRNLLGLYP-------AVVIQDNSDIKVVP--KAKIIRD------YAVESEA---SVQ--PVAALERIN-----WLN-YNE---ERGDFPGTYVEYIGRKKISP Profile alignment, “once a gap - always a gap” `Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVN-------WW--RLNDKEGYVPRNLLGLYP-------AVVIQDNSDIKVVP--KAKIIRD------YAVESEA---SVQ--PVAALERIN-----WLN-YNE---ERGDFPGTYVEYIGRKKISP Profile alignment, “once a gap - always a gap”](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-8.jpg)
`Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVN-------WW--RLNDKEGYVPRNLLGLYP-------AVVIQDNSDIKVVP--KAKIIRD------YAVESEA---SVQ--PVAALERIN-----WLN-YNE---ERGDFPGTYVEYIGRKKISP Profile alignment, “once a gap - always a gap”
![`Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVN-------WW--RLNDKEGYVPRNLLGLYP-------AVVIQDNSDIKVVP--KAKIIRD------YAVESEA---SVQ--PVAALERIN-----WLN-YNE---ERGDFPGTYVEYIGRKKISP Most important implementation: CLUSTAL W `Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVN-------WW--RLNDKEGYVPRNLLGLYP-------AVVIQDNSDIKVVP--KAKIIRD------YAVESEA---SVQ--PVAALERIN-----WLN-YNE---ERGDFPGTYVEYIGRKKISP Most important implementation: CLUSTAL W](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-9.jpg)
`Progressive´ Alignment WCEAQTKNGQGWVPSNYITPVN-------WW--RLNDKEGYVPRNLLGLYP-------AVVIQDNSDIKVVP--KAKIIRD------YAVESEA---SVQ--PVAALERIN-----WLN-YNE---ERGDFPGTYVEYIGRKKISP Most important implementation: CLUSTAL W
![`Progressive´ Alignment CLUSTAL W; Thompson et al. , 1994 (~17. 000 citations) Pairwise distances `Progressive´ Alignment CLUSTAL W; Thompson et al. , 1994 (~17. 000 citations) Pairwise distances](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-10.jpg)
`Progressive´ Alignment CLUSTAL W; Thompson et al. , 1994 (~17. 000 citations) Pairwise distances as 1 - percentage of identity Calculate un-rooted tree with Neighbor Joining Define root as central position in tree Define sequence weights based on tree Gap penalties calculated based on various parameters
![Tools for multiple sequence alignment Problems with traditional approach: Results depend on gap penalty Tools for multiple sequence alignment Problems with traditional approach: Results depend on gap penalty](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-11.jpg)
Tools for multiple sequence alignment Problems with traditional approach: Results depend on gap penalty Heuristic guide tree determines alignment; alignment used for phylogeny reconstruction Algorithm produces global alignments.
![Tools for multiple sequence alignment Problems with traditional approach: But: Many sequence families share Tools for multiple sequence alignment Problems with traditional approach: But: Many sequence families share](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-12.jpg)
Tools for multiple sequence alignment Problems with traditional approach: But: Many sequence families share only local similarity E. g. sequences share one conserved motif
![Local sequence alignment EYENS ERYAS Find common motif in sequences; ignore the rest Local sequence alignment EYENS ERYAS Find common motif in sequences; ignore the rest](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-13.jpg)
Local sequence alignment EYENS ERYAS Find common motif in sequences; ignore the rest
![Local sequence alignment E-YENS ERYA-S Find common motif in sequences; ignore the rest Local sequence alignment E-YENS ERYA-S Find common motif in sequences; ignore the rest](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-14.jpg)
Local sequence alignment E-YENS ERYA-S Find common motif in sequences; ignore the rest
![Local sequence alignment E-YENS ERYA-S Find common motif in sequences; ignore the rest – Local sequence alignment E-YENS ERYA-S Find common motif in sequences; ignore the rest –](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-15.jpg)
Local sequence alignment E-YENS ERYA-S Find common motif in sequences; ignore the rest – Local alignment
![Local sequence alignment Traditional alignment approaches: Either global or local methods! Local sequence alignment Traditional alignment approaches: Either global or local methods!](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-16.jpg)
Local sequence alignment Traditional alignment approaches: Either global or local methods!
![New question: sequence families with multiple local similarities Neither local nor global methods appliccable New question: sequence families with multiple local similarities Neither local nor global methods appliccable](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-17.jpg)
New question: sequence families with multiple local similarities Neither local nor global methods appliccable
![New question: sequence families with multiple local similarities Alignment possible if order conserved New question: sequence families with multiple local similarities Alignment possible if order conserved](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-18.jpg)
New question: sequence families with multiple local similarities Alignment possible if order conserved
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-19.jpg)
The DIALIGN approach
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-20.jpg)
The DIALIGN approach
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-21.jpg)
The DIALIGN approach
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-22.jpg)
The DIALIGN approach
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-23.jpg)
The DIALIGN approach
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-24.jpg)
The DIALIGN approach
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-25.jpg)
The DIALIGN approach
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-26.jpg)
The DIALIGN approach
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-27.jpg)
The DIALIGN approach
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-28.jpg)
The DIALIGN approach
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-29.jpg)
The DIALIGN approach
![The DIALIGN approach Consistency! The DIALIGN approach Consistency!](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-30.jpg)
The DIALIGN approach Consistency!
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-31.jpg)
The DIALIGN approach
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-32.jpg)
The DIALIGN approach
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-33.jpg)
The DIALIGN approach
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-34.jpg)
The DIALIGN approach
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-35.jpg)
The DIALIGN approach
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-36.jpg)
The DIALIGN approach
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-37.jpg)
The DIALIGN approach
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-38.jpg)
The DIALIGN approach
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-39.jpg)
The DIALIGN approach
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-40.jpg)
The DIALIGN approach
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-41.jpg)
The DIALIGN approach
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-42.jpg)
The DIALIGN approach
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-43.jpg)
The DIALIGN approach
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-44.jpg)
The DIALIGN approach
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-45.jpg)
The DIALIGN approach
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-46.jpg)
The DIALIGN approach
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-47.jpg)
The DIALIGN approach
![The DIALIGN approach The DIALIGN approach](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-48.jpg)
The DIALIGN approach
![T-COFFEE C. Notredame, D. Higgins, J. Heringa (2000), T-Coffee: A novel algorithm for multiple T-COFFEE C. Notredame, D. Higgins, J. Heringa (2000), T-Coffee: A novel algorithm for multiple](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-49.jpg)
T-COFFEE C. Notredame, D. Higgins, J. Heringa (2000), T-Coffee: A novel algorithm for multiple sequence alignment, J. Mol. Biol. Problem: progressive alignment can go wrong if mistakes are made at an early stage. Example …
![T-COFFEE Seq. A Seq. B Seq. C Seq. D GARFIELD THE LAST FAT CAT T-COFFEE Seq. A Seq. B Seq. C Seq. D GARFIELD THE LAST FAT CAT](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-50.jpg)
T-COFFEE Seq. A Seq. B Seq. C Seq. D GARFIELD THE LAST FAT CAT GARFIELD THE FAST CAT GARFIELD THE VERY FAST CAT THE FAT CAT
![T-COFFEE Seq. A Seq. B Seq. C Seq. D GARFIELD THE LAST FAT CAT T-COFFEE Seq. A Seq. B Seq. C Seq. D GARFIELD THE LAST FAT CAT](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-51.jpg)
T-COFFEE Seq. A Seq. B Seq. C Seq. D GARFIELD THE LAST FAT CAT GARFIELD THE FAST CAT GARFIELD THE VERY FAST CAT THE FAT CAT
![T-COFFEE T-COFFEE](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-52.jpg)
T-COFFEE
![T-COFFEE Idea: consider different pairwise alignments (local and global) check how these alignments support T-COFFEE Idea: consider different pairwise alignments (local and global) check how these alignments support](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-53.jpg)
T-COFFEE Idea: consider different pairwise alignments (local and global) check how these alignments support each other
![T-COFFEE T-COFFEE](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-54.jpg)
T-COFFEE
![T-COFFEE T-COFFEE](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-55.jpg)
T-COFFEE
![T-COFFEE Less sensitive to spurious pairwise similarities Can handle local homologies better than CLUSTAL T-COFFEE Less sensitive to spurious pairwise similarities Can handle local homologies better than CLUSTAL](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-56.jpg)
T-COFFEE Less sensitive to spurious pairwise similarities Can handle local homologies better than CLUSTAL
![Evaluation of multi-alignment methods Alignment evaluation by comparison to trusted benchmark alignments. `True’ alignment Evaluation of multi-alignment methods Alignment evaluation by comparison to trusted benchmark alignments. `True’ alignment](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-57.jpg)
Evaluation of multi-alignment methods Alignment evaluation by comparison to trusted benchmark alignments. `True’ alignment known by information about structure or evolution.
![Evaluation of multi-alignment methods For protein alignment: M. Mc. Clure et al. (1994): 4 Evaluation of multi-alignment methods For protein alignment: M. Mc. Clure et al. (1994): 4](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-58.jpg)
Evaluation of multi-alignment methods For protein alignment: M. Mc. Clure et al. (1994): 4 protein families, known functional sites J. Thompson et al. (1999): Benchmark data base, 130 known 3 D structures (BAli. BASE) T. Lassmann & E. Sonnhammer (2002): BAli. BASE + simulated evolution (ROSE)
![Evaluation of multi-alignment methods Evaluation of multi-alignment methods](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-59.jpg)
Evaluation of multi-alignment methods
![Evaluation of multi-alignment methods Alignment evaluation by comparison to trusted benchmark alignments. `True’ alignment Evaluation of multi-alignment methods Alignment evaluation by comparison to trusted benchmark alignments. `True’ alignment](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-60.jpg)
Evaluation of multi-alignment methods Alignment evaluation by comparison to trusted benchmark alignments. `True’ alignment known by information about structure or evolution.
![Evaluation of multi-alignment methods Evaluation of multi-alignment methods](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-61.jpg)
Evaluation of multi-alignment methods
![Evaluation of multi-alignment methods 1 abo. A 1 ycs. B 1 pht 1 ihv. Evaluation of multi-alignment methods 1 abo. A 1 ycs. B 1 pht 1 ihv.](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-62.jpg)
Evaluation of multi-alignment methods 1 abo. A 1 ycs. B 1 pht 1 ihv. A 1 vie 1 1 1 abo. A 1 ycs. B 1 pht 1 ihv. A 1 vie 36 39 51 27 28 . NLFVALYDfvasgdntlsitk. GEKLRVLgynhn. . . g. E k. GVIYALWDyepqnddelpmke. GDCMTIIhrede. . . dei. E g. YQYRALYDykkereedidlhl. GDILTVNkgslvalgfsdgqearpeei. G. NFRVYYRDsrd. . . pvwk. GPAKLLWkg. . . . e. G. drvrkksga. . awq. GQIVGWYctnlt. . . pe. G WCEAQt. . kngq. GWVPSNYITPVN. . . WWWARl. . ndke. GYVPRNLLGLYP. . . WLNGYnettger. GDFPGTYVEYIGrkkisp AVVIQd. . nsdi. KVVPRRKAKIIRd. . . YAVESeahpgsv. QIYPVAALERIN. . . Key alpha helix RED beta strand GREEN core blocks UNDERSCORE BAli. BASE Reference alignments
![Evaluation of multi-alignment methods 5 categories of benchmark sequences (globally related, internal gaps, end Evaluation of multi-alignment methods 5 categories of benchmark sequences (globally related, internal gaps, end](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-63.jpg)
Evaluation of multi-alignment methods 5 categories of benchmark sequences (globally related, internal gaps, end gaps) CLUSTAL W, RPPR perform well on globally related sequences, DIALIGN superior for local similarities Conclusion: no single best multi alignment program!
![Evaluation of multi-alignment methods T. Lassmann & E. Sonnhammer (2002): BAli. BASE + simulated Evaluation of multi-alignment methods T. Lassmann & E. Sonnhammer (2002): BAli. BASE + simulated](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-64.jpg)
Evaluation of multi-alignment methods T. Lassmann & E. Sonnhammer (2002): BAli. BASE + simulated evolution (ROSE)
![](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-65.jpg)
![Result: DIALIGN best for distantly related sequences, TCOFFEE best for closely related sequences Result: DIALIGN best for distantly related sequences, TCOFFEE best for closely related sequences](http://slidetodoc.com/presentation_image_h/2fcc5996cb1c8801bcb6e68b589c7eea/image-66.jpg)
Result: DIALIGN best for distantly related sequences, TCOFFEE best for closely related sequences
- Slides: 66