Download - Tools for multiple sequence alignment
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Bioinformatics Methods Course
Multiple Sequence Alignment
Burkhard Morgenstern
University of GöttingenInstitute of Microbiology and Genetics
Department of Bioinformatics
Göttingen, 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:
1. 3D-Structure: align residues at corresponding positions in 3D structure of protein!
2. 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!
-> 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
Find common motif in sequences; ignore the rest
EYENS
ERYENS
ERYAS
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Local sequence alignment
Find common motif in sequences; ignore the rest
E-YENS
ERYENS
ERYA-S
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Local sequence alignment
Find common motif in sequences; ignore the rest – Local alignment
E-YENSERYENSERYA-S
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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 AlignmentScience, 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
atc------taatagttaaactcccccgtgc-ttag
cagtgcgtgtattactaac----------gg-ttcaatcgcg
caaa--gagtatcacc----------cctgaattgaataa
Consistency!
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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:
1. Build library of pairwise alignments
2. 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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1aboA 1 .NLFVALYDfvasgdntlsitkGEKLRVLgynhn..............gE 1ycsB 1 kGVIYALWDyepqnddelpmkeGDCMTIIhrede............deiE 1pht 1 gYQYRALYDykkereedidlhlGDILTVNkgslvalgfsdgqearpeeiG 1ihvA 1 .NFRVYYRDsrd......pvwkGPAKLLWkg.................eG 1vie 1 .drvrkksga.........awqGQIVGWYctnlt.............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
Evaluation of multi-alignment methods
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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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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
Algorithmic questions
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Algorithmic questions
NLFVALYDFVASGDNTLSITKGEKLRVLGYNHN IIHREDKGVIYALWDYEPQNDDELPMKEGDCMTGYQYRALYDYKKEREEDIDLHLGDILTVNKGSLVALGFS
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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
Algorithmic questions
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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
Algorithmic questions
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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
Algorithmic questions
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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
Algorithmic questions
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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
Algorithmic questions