Bioinformatics Methods Course Multiple Sequence Alignment Burkhard Morgenstern University of G

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Bioinformatics Methods CourseMultiple Sequence
AlignmentBurkhard Morgenstern University of
GöttingenInstitute of Microbiology and Genetics
Department of BioinformaticsGöttingen,
October/November 2006
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Tools for multiple sequence alignment

• T Y I M R E A Q Y E
• T C I V M R E A Y E

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Tools for multiple sequence alignment

• T Y I - M R E A Q Y E
• T C I V M R E A - Y E

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Tools for multiple sequence alignment

• T Y I M R E A Q Y E
• T C I V M R E A Y E
• Y I M Q E V Q Q E
• Y I A M R E Q Y E

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Tools for multiple sequence alignment

• T Y I - M R E A Q Y E
• T C I V M R E A - Y E
• Y - I - M Q E V Q Q E
• Y I A M R E - Q Y E

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Tools for multiple sequence alignment

• T Y I - M R E A Q Y E
• T C I V M R E A - Y E
• - Y I - M Q E V Q Q E
• Y I A M R E - Q Y E
• Astronomical Number of possible alignments!

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Tools for multiple sequence alignment

• T Y I - M R E A Q Y E
• T C I V - M R E A Y E
• - Y I - M Q E V Q Q E
• Y I A M R E - Q Y E
• Astronomical Number of possible alignments!

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Tools for multiple sequence alignment

• T Y I - M R E A Q Y E
• T C I V M R E A - Y E
• - Y I - M Q E V Q Q E
• Y I A M R E - Q Y E
• Which one is the best ???

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Tools for multiple sequence alignment

• Questions in development of alignment programs
• (1) What is a good alignment?
• ? objective function (score)
• (2) How to find a good alignment?
• ? optimization algorithm
• First question far more important !

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Tools for multiple sequence alignment

• Before defining an objective function (scoring
  scheme)
• What is a biologically good alignment ??

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Tools for multiple sequence alignment

• Criteria for alignment quality
• 3D-Structure align residues at corresponding
  positions in 3D structure of protein!
• Evolution align residues with common ancestors!

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Tools for multiple sequence alignment

• T Y I - M R E A Q Y E
• T C I V - M R E A Y E
• - Y I - M Q E V Q Q E
• - Y I A M R E - Q Y E
• Alignment hypothesis about sequence evolution
• Search for most plausible hypothesis!

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Tools for multiple sequence alignment

• Compute for amino acids a and b
• Probability pa,b of substitution
• a ? b (or b ? a),
• Frequency qa of a
• Define
• s(a,b) log (pa,b / qa qb)

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Tools for multiple sequence alignment
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Tools for multiple sequence alignment

• Traditional objective functions
• Define Score of alignments as
• Sum of individual similarity scores s(a,b)
• Gap penalty g for each gap in alignment
• Needleman-Wunsch scoring system (1970) for
  pairwise alignment ( alignment of two sequences)

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• T Y W I V
• T - - L V
• Example
• Score s(T,T) s(I,L) s (V,V) 2 g

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• T Y W I V
• T - - L V
• Idea alignment with optimal (maximal) score
  probably biologically meaningful.
• Dynamic programming algorithm finds optimal
  alignment for two sequences efficiently
  (Needleman and Wunsch, 1970).

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Tools for multiple sequence alignment

• Traditional Objective functions can be
  generalized to multiple alignment (e.g.
  sum-of-pair score, tree alignment)
• Needleman-Wunsch algorithm can also be
  generalized to find optimal multiple alignment,
  but
• Very time and memory consuming!
• -gt Heuristic algorithm needed, i.e. fast but
  sub-optimal solution

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Tools for multiple sequence alignment

• Most commonly used heuristic for multiple
  alignment
• Progressive alignment
• (mid 1980s)

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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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CLUSTAL W

• Most important software program
• CLUSTAL W
• J. Thompson, T. Gibson, D. Higgins (1994),
  CLUSTAL W improving the sensitivity of
  progressive multiple sequence alignment Nuc.
  Acids. Res. 22, 4673 - 4680
• ( 20.000 citations in the literature)

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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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Gibbs Motive Sampler
Local multiple alignment without gaps C.E.
Lawrence et al. (1993) Detecting subtle sequence
signals a Gibbs Sampling Strategy for Multiple
Alignment Science, 262, 208 - 214
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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

• Morgenstern, Dress, Werner (1996),
• PNAS 93, 12098-12103
• Combination of global and local methods
• Assemble multiple alignment from
• gap-free local pair-wise alignments
• (,,fragments)

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The DIALIGN approach

• atctaatagttaaactcccccgtgcttag
• cagtgcgtgtattactaacggttcaatcgcg
• caaagagtatcacccctgaattgaataa

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The DIALIGN approach

• atctaatagttaaactcccccgtgcttag
• cagtgcgtgtattactaacggttcaatcgcg
• caaagagtatcacccctgaattgaataa

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The DIALIGN approach

• atctaatagttaaactcccccgtgcttag
• cagtgcgtgtattactaacggttcaatcgcg
• caaagagtatcacccctgaattgaataa

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The DIALIGN approach

• atctaatagttaaactcccccgtgcttag
• cagtgcgtgtattactaacggttcaatcgcg
• caaagagtatcacccctgaattgaataa

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The DIALIGN approach

• atctaatagttaaactcccccgtgcttag
• cagtgcgtgtattactaacggttcaatcgcg
• caaagagtatcacccctgaattgaataa

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The DIALIGN approach

• atctaatagttaaactcccccgtgcttag
• cagtgcgtgtattactaacggttcaatcgcg
• caaagagtatcacccctgaattgaataa

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The DIALIGN approach

• atc------taatagttaaactcccccgtgcttag
• cagtgcgtgtattactaacggttcaatcgcg
• caaagagtatcacccctgaattgaataa

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The DIALIGN approach

• atc------taatagttaaactcccccgtgcttag
• cagtgcgtgtattactaacggttcaatcgcg
• caaa--gagtatcacccctgaattgaataa

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The DIALIGN approach

• atc------taatagttaaactcccccgtgcttag
• cagtgcgtgtattactaacggttcaatcgcg
• caaa--gagtatcacc----------cctgaattgaataa

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The DIALIGN approach

• atc------taatagttaaactcccccgtgc-ttag
• cagtgcgtgtattactaac----------gg-ttcaatcgcg
• caaa--gagtatcacc----------cctgaattgaataa

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The DIALIGN approach
Consistency!

• atc------taatagttaaactcccccgtgc-ttag
• cagtgcgtgtattactaac----------gg-ttcaatcgcg
• caaa--gagtatcacc----------cctgaattgaataa

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The DIALIGN approach

• atc------TAATAGTTAaactccccCGTGC-TTag
• cagtgcGTGTATTACTAAc----------GG-TTCAATcgcg
• caaa--GAGTATCAcc----------CCTGaaTTGAATaa

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The DIALIGN approach

• Multiple alignment
• atctaatagttaaactcccccgtgcttag
• cagtgcgtgtattactaacggttcaatcgcg
• caaagagtatcacccctgaattgaataa

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The DIALIGN approach

• Multiple alignment
• atctaatagttaaactcccccgtgcttag
• cagtgcgtgtattactaacggttcaatcgcg
• caaccctgaattgaagagtatcacataa
• (1) Calculate all optimal pair-wise alignments

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The DIALIGN approach

• Multiple alignment
• atctaatagttaaactcccccgtgcttag
• cagtgcgtgtattactaacggttcaatcgcg
• caaagagtatcacccctgaattgaataa
• (1) Calculate all optimal pair-wise alignments

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The DIALIGN approach

• Multiple alignment
• atctaatagttaaactcccccgtgcttag
• cagtgcgtgtattactaacggttcaatcgcg
• caaagagtatcacccctgaattgaataa
• (1) Calculate all optimal pair-wise alignments

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The DIALIGN approach

• Fragments from optimal pair-wise alignments
• might be inconsistent

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The DIALIGN approach

• atctaatagttaaactcccccgtgcttag
• cagtgcgtgtattactaacggttcaatcgcg
• caaagagtatcacccctgaattgaataa

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The DIALIGN approach

• atctaatagttaaactcccccgtgcttag
• cagtgcgtgtattactaacggttcaatcgcg
• caaagagtatcacccctgaattgaataa

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The DIALIGN approach

• atctaatagttaaactcccccgtgcttag
• cagtgcgtgtattactaacggttcaatcgcg
• caaagagtatcacccctgaattgaataa

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The DIALIGN approach

• atctaatagttaaactcccccgtgcttag
• cagtgcgtgtattactaacggttcaatcgcg
• caaa--gagtatcacccctgaattgaataa

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The DIALIGN approach

• atc------taatagttaaactcccccgtgcttag
• cagtgcgtgtattactaacggttcaatcgcg
• caaa--gagtatcacccctgaattgaataa

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The DIALIGN approach

• atctaatagttaaactcccccgtgcttag
• cagtgcgtgtattactaacggttcaatcgcg
• caaagagtatcacccctgaattgaataa

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The DIALIGN approach

• Score of alignment
• Define weight score for fragments based on
  probability of random occurrence
• Score of alignment sum of weight scores of
  fragments
• Goal find consistent set of fragments with
  maximum total weight

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The DIALIGN approach

• Advantages of segment-based approach
• Program can produce global and local alignments!
• Sequence families alignable that cannot be
  aligned with standard methods

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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.

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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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T-COFFEE

• T-COFFEE
• Idea
• Build library of pairwise alignments
• Alignment from seq i, j and seq j, k supports
  alignmetn from seq i, k.

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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

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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Result DIALIGN best method for distantly related
sequences, T-Coffee best for globally related
proteins

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Evaluation of multi-alignment methods

• BAliBASE 5 categories of benchmark sequences
  (globally related, internal gaps, end gaps)
• CLUSTAL W, T-COFFEE, MAFFT, PROBCONS perform well
  on globally related sequences, DIALIGN superior
  for local similarities

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Evaluation of multi-alignment methods

• Conclusion no single best multi alignment
  program!
• Advice try different methods!

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Anchored sequence alignment

• Idea semi-automatic alignment
• use expert knowledge to define constraints
  instead of fully automated alignment
• Define parts of the sequences where biologically
  correct alignment is known as anchor points,
  align rest of the sequences automatically.

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Anchored sequence alignment

• NLFVALYDFVASGDNTLSITKGEKLRVLGYNHN
• IIHREDKGVIYALWDYEPQNDDELPMKEGDCMT
• GYQYRALYDYKKEREEDIDLHLGDILTVNKGSLVALGFS

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Anchored sequence alignment

• NLFVALYDFVASGDNTLSITKGEKLRVLGYNHN
• IIHREDKGVIYALWDYEPQNDDELPMKEGDCMT
• GYQYRALYDYKKEREEDIDLHLGDILTVNKGSLVALGFS
• Anchor points in multiple alignment

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Anchored sequence alignment

• NLFV ALYDFVASGDNTLSITKGEKLRVLGYNHN
• IIHREDKGVIYALWDYEPQND DELPMKEGDCMT
• GYQYRALYDYKKEREEDIDLHLGDILTVNKGSLVALGFS
• Anchor points in multiple alignment

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Anchored sequence alignment

• -------NLF V-ALYDFVAS GD-------- NTLSITKGEk
  lrvLGYNhn
• iihredkGVI Y-ALWDYEPQ ND-------- DELPMKEGDC
  MT-------
• -------GYQ YrALYDYKKE REedidlhlg DILTVNKGSL
  VA-LGFS--
• Anchored multiple alignment

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Algorithmic questions

• Goal
• Find optimal alignment (consistent set of
  fragments) under costraints given by
  user-specified anchor points!

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Algorithmic questions

• Additional input file with anchor points
• 1 3 215 231 5 4.5
• 2 3 34 78 23 1.23
• 1 4 317 402 8 8.5

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Algorithmic questions

• NLFVALYDFVASGDNTLSITKGEKLRVLGYNHN
• IIHREDKGVIYALWDYEPQNDDELPMKEGDCMT
• GYQYRALYDYKKEREEDIDLHLGDILTVNKGSLVALGFS

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Algorithmic questions

• Additional input file with anchor points
• 1 3 215 231 5 4.5
• 2 3 34 78 23 1.23
• 1 4 317 402 8 8.5

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Algorithmic questions

• Additional input file with anchor points
• 1 3 215 231 5 4.5
• 2 3 34 78 23 1.23
• 1 4 317 402 8 8.5
• Sequences

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Algorithmic questions

• Additional input file with anchor points
• 1 3 215 231 5 4.5
• 2 3 34 78 23 1.23
• 1 4 317 402 8 8.5
• Sequences start positions

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Algorithmic questions

• Additional input file with anchor points
• 1 3 215 231 5 4.5
• 2 3 34 78 23 1.23
• 1 4 317 402 8 8.5
• Sequences start positions length

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Algorithmic questions

• Additional input file with anchor points
• 1 3 215 231 5 4.5
• 2 3 34 78 23 1.23
• 1 4 317 402 8 8.5
• Sequences start positions length
  score