Exon prediction by Genomic Sequence alignment Burkhard Mo

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Burkhard Morgenstern Institut für Mikrobiologie
und Genetik Grundlagen der Bioinformatik Multipl
es Sequenzalignment Juni 2007


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Progressive Alignment

• Most popular approach to (global) multiple
  sequence alignment
• Progressive Alignment
• Since mid-Eighties Feng/Doolittle,
  Higgins/Sharp, Taylor,

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Progressive Alignment

• WCEAQTKNGQGWVPSNYITPVN
• WWRLNDKEGYVPRNLLGLYP
• AVVIQDNSDIKVVPKAKIIRD
• YAVESEAHPGSFQPVAALERIN
• WLNYNETTGERGDFPGTYVEYIGRKKISP

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Progressive Alignment

• WCEAQTKNGQGWVPSNYITPVN
• WWRLNDKEGYVPRNLLGLYP
• AVVIQDNSDIKVVPKAKIIRD
• YAVESEAHPGSFQPVAALERIN
• WLNYNETTGERGDFPGTYVEYIGRKKISP
• Guide tree

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Progressive Alignment

• WCEAQTKNGQGWVPSNYITPVN
• WW--RLNDKEGYVPRNLLGLYP-
• AVVIQDNSDIKVVP--KAKIIRD
• YAVESEASFQPVAALERIN
• WLNYNEERGDFPGTYVEYIGRKKISP
• Profile alignment, once a gap - always a gap

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Progressive Alignment

• WCEAQTKNGQGWVPSNYITPVN
• WW--RLNDKEGYVPRNLLGLYP-
• AVVIQDNSDIKVVP--KAKIIRD
• YAVESEASVQ--PVAALERIN------
• WLN-YNEERGDFPGTYVEYIGRKKISP
• Profile alignment, once a gap - always a gap

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Progressive Alignment

• WCEAQTKNGQGWVPSNYITPVN-
• WW--RLNDKEGYVPRNLLGLYP-
• AVVIQDNSDIKVVP--KAKIIRD
• YAVESEASVQ--PVAALERIN------
• WLN-YNEERGDFPGTYVEYIGRKKISP
• Profile alignment, once a gap - always a gap

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Progressive Alignment

• WCEAQTKNGQGWVPSNYITPVN--------
• WW--RLNDKEGYVPRNLLGLYP--------
• AVVIQDNSDIKVVP--KAKIIRD-------
• YAVESEA---SVQ--PVAALERIN------
• WLN-YNE---ERGDFPGTYVEYIGRKKISP
• Profile alignment, once a gap - always a gap

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Progressive Alignment

• WCEAQTKNGQGWVPSNYITPVN--------
• WW--RLNDKEGYVPRNLLGLYP--------
• AVVIQDNSDIKVVP--KAKIIRD-------
• YAVESEA---SVQ--PVAALERIN------
• WLN-YNE---ERGDFPGTYVEYIGRKKISP
• Most important implementation CLUSTAL W

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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

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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.

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Tools for multiple sequence alignment

• Problems with traditional approach
• But
• Many sequence families share only local
  similarity
• E.g. sequences share one conserved motif

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Local sequence alignment
EYENS

ERYENS
ERYAS
Find common motif in sequences ignore the rest
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Local sequence alignment
E-YENS

ERYENS
ERYA-S
Find common motif in sequences ignore the rest
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Local sequence alignment

E-YENS
ERYENS
ERYA-S
Find common motif in sequences ignore the rest
Local alignment
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Local sequence alignment
Traditional alignment approaches Either global
or local methods!
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New question sequence families with multiple
local similarities


Neither local nor global methods appliccable
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New question sequence families with multiple
local similarities


Alignment possible if order conserved
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The DIALIGN approach
The DIALIGN approach
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The DIALIGN approach
The DIALIGN approach
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The DIALIGN approach
The DIALIGN approach
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The DIALIGN approach
The DIALIGN approach
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The DIALIGN approach
The DIALIGN approach
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The DIALIGN approach
The DIALIGN approach
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The DIALIGN approach
The DIALIGN approach
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The DIALIGN approach
The DIALIGN approach
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The DIALIGN approach
The DIALIGN approach
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The DIALIGN approach
The DIALIGN approach
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The DIALIGN approach
The DIALIGN approach

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The DIALIGN approach
The DIALIGN approach
Consistency!
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The DIALIGN approach
The DIALIGN approach
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The DIALIGN approach
The DIALIGN approach
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The DIALIGN approach
The DIALIGN approach
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The DIALIGN approach
The DIALIGN approach
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The DIALIGN approach
The DIALIGN approach
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The DIALIGN approach
The DIALIGN approach
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The DIALIGN approach
The DIALIGN approach
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The DIALIGN approach
The DIALIGN approach
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The DIALIGN approach
The DIALIGN approach
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The DIALIGN approach
The DIALIGN approach
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The DIALIGN approach
The DIALIGN approach
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The DIALIGN approach
The DIALIGN approach
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The DIALIGN approach
The DIALIGN approach
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The DIALIGN approach
The DIALIGN approach
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The DIALIGN approach
The DIALIGN approach
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The DIALIGN approach
The DIALIGN approach
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The DIALIGN approach
The DIALIGN approach
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The DIALIGN approach
The DIALIGN approach
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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
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T-COFFEE

SeqA GARFIELD THE LAST FAT CAT SeqB GARFIELD
THE FAST CAT SeqC GARFIELD THE VERY FAST
CAT SeqD THE FAT CAT
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T-COFFEE

SeqA GARFIELD THE LAST FAT CAT SeqB GARFIELD
THE FAST CAT SeqC GARFIELD THE VERY FAST
CAT SeqD THE FAT CAT
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T-COFFEE

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T-COFFEE

• Idea consider different pairwise alignments
  (local and global)
• check how these alignments support each other

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T-COFFEE

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T-COFFEE

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T-COFFEE

• T-COFFEE
• Less sensitive to spurious pairwise similarities
• Can handle local homologies better than CLUSTAL

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Evaluation of multi-alignment methods

• Alignment evaluation by comparison to trusted
  benchmark alignments.
• True alignment known by information about
  structure or evolution.

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Evaluation of multi-alignment methods

• For protein alignment
• M. McClure et al. (1994)
• 4 protein families, known functional sites
• J. Thompson et al. (1999)
• Benchmark data base, 130 known 3D structures
  (BAliBASE)
• T. Lassmann E. Sonnhammer (2002)
  BAliBASE simulated evolution (ROSE)

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Evaluation of multi-alignment methods

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Evaluation of multi-alignment methods

• Alignment evaluation by comparison to trusted
  benchmark alignments.
• True alignment known by information about
  structure or evolution.

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Evaluation of multi-alignment methods

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Evaluation of multi-alignment methods

1aboA 1 .NLFVALYDfvasgdntlsitkGEKLRVLgynhn
..............gE 1ycsB 1
kGVIYALWDyepqnddelpmkeGDCMTIIhrede............deiE
1pht 1 gYQYRALYDykkereedidlhlGDILTVNkgs
lvalgfsdgqearpeeiG 1ihvA 1
.NFRVYYRDsrd......pvwkGPAKLLWkg.................eG
1vie 1 .drvrkksga.........awqGQIVGWYctn
lt.............peG 1aboA 36
WCEAQt..kngqGWVPSNYITPVN...... 1ycsB 39
WWWARl..ndkeGYVPRNLLGLYP...... 1pht 51
WLNGYnettgerGDFPGTYVEYIGrkkisp 1ihvA 27
AVVIQd..nsdiKVVPRRKAKIIRd..... 1vie 28
YAVESeahpgsvQIYPVAALERIN...... Key alpha
helix RED beta strand GREEN core blocks
UNDERSCORE
BAliBASE Reference alignments
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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!

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Evaluation of multi-alignment methods

• T. Lassmann E. Sonnhammer (2002)
  BAliBASE simulated evolution (ROSE)

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Result DIALIGN best for distantly related
sequences, TCOFFEE best for closely related
sequences