Sequence Alignment and Phylogenetic Analysis
Evolution
Sequence Alignment
-AGGCTATCACCTGACCTCCAGGCCGA--TGCCC---TAG-CTATCAC--GACCGC--GGTCGATTTGCCCGAC
DefinitionGiven two strings x = x1x2...xM, y = y1y2…yN,
an alignment is an assignment of gaps to positions0,…, N in x, and 0,…, N in y, so as to line up each
letter in one sequence with either a letter, or a gapin the other sequence
AGGCTATCACCTGACCTCCAGGCCGATGCCCTAGCTATCACGACCGCGGTCGATTTGCCCGAC
What is a good alignment?AGGCTAGTT, AGCGAAGTTT
AGGCTAGTT- 6 matches, 3 mismatches, 1 gapAGCGAAGTTT
AGGCTA-GTT- 7 matches, 1 mismatch, 3 gapsAG-CGAAGTTT
AGGC-TA-GTT- 7 matches, 0 mismatches, 5 gapsAG-CG-AAGTTT
Scoring Function• Sequence edits:
AGGCCTC
• Mutations AGGACTC
• Insertions AGGGCCTC
• Deletions AGG . CTC
Scoring Function:Match: +mMismatch: -sGap: -d
Score F = (# matches) m - (# mismatches) s – (#gaps) d
G -
A G T A
0 -1 -2 -3 -4
A -1 1 0 -1 -2
T -2 0 0 1 0
A -3 -1 -1 0 2
F(i,j) i = 0 1 2 3 4
Examplex = AGTA m = 1y = ATA s = -1
d = -1
j = 0
1
2
3
F(1, 1) = max{F(0,0) + s(A, A), F(0, 1) – d, F(1, 0) – d} =
max{0 + 1, -1 – 1, -1 – 1} = 1
AA
TT
AA
The Needleman-Wunsch Matrixx1 ……………………………… xMy
1 ……
……
……
……
……
……
yN
Every nondecreasing path
from (0,0) to (M, N)
corresponds to an alignment of the two sequences
An optimal alignment is composed of optimal subalignments
8
Example
H E A G A W G H E E
0 -8 -16
-24
-32
-40
-48
-56
-64
-72
-80
P -8 -2 -9 -17
-25
-33
-42
-49
-57
-65
-73
A -16
W -24
H -32
E -40
A -48
E -56
A E G H W
A 5 -1 0 -2 -3
E -1 6 -3 0 -3
H -2 0 -2 10 -3
P -1 -1 -2 -2 -4
W -3 -3 -3 -3 15
9
H E A G A W G H E E
0 -8 -16
-24
-32
-40
-48
-56
-64
-72
-80
P -8 -2 -9 -17
-25
-33
-42
-49
-57
-65
-73
A -16
-10
-3 -4 -12
-20
-28
-36
-44
-52
-60
W -24
-18
-11
-6 -7 -15
-5 -13
-21
-29
-37
H -32
-14
-18
-13
-8 -9 -13
-7 -3 -11
-19
E -40
-22
-8 -16
-16
-9 -12
-15
-7 3 -5
A -48
-30
-16
-3 -11
-11
-12
-12
-15
-5 2
E -56
-38
-24
-11
-6 -12
-14
-15
-12
-9 1
A E G H W
A 5 -1 0 -2 -3
E -1 6 -3 0 -3
H -2 0 -2 10 -3
P -1 -1 -2 -2 -4
W -3 -3 -3 -3 15
PAMX
• PAMx = PAM1x
• PAM250 = PAM1250
• PAM250 is a widely used scoring matrix:
Ala Arg Asn Asp Cys Gln Glu Gly His Ile Leu Lys ... A R N D C Q E G H I L K ...Ala A 13 6 9 9 5 8 9 12 6 8 6 7 ...Arg R 3 17 4 3 2 5 3 2 6 3 2 9Asn N 4 4 6 7 2 5 6 4 6 3 2 5Asp D 5 4 8 11 1 7 10 5 6 3 2 5Cys C 2 1 1 1 52 1 1 2 2 2 1 1Gln Q 3 5 5 6 1 10 7 3 7 2 3 5...Trp W 0 2 0 0 0 0 0 0 1 0 1 0Tyr Y 1 1 2 1 3 1 1 1 3 2 2 1Val V 7 4 4 4 4 4 4 4 5 4 15 10
The Blosum50 Scoring Matrix
Affine Gap Penalties
• In nature, a series of k indels often come as a single event rather than a series of k single nucleotide events:
Normal scoring would give the same score for both alignments
This is more likely.
This is less likely.
Affine gaps(n) = d + (n – 1)e
| | gap gap open extend
To compute optimal alignment,
F(i, j): score of alignment x1…xi to y1…yjifif xi aligns to yj
G(i, j): score ifif xi aligns to a gap after yjH(i, j): score ifif yj aligns to a gap after xi
V(i, j) = best score of alignment x1…xi to y1…yj
de
(n)
Needleman-Wunsch with affine gaps
Initialization:V(i, 0) = d + (i – 1)eV(0, j) = d + (j – 1)e
Iteration:V(i, j) = max{ F(i, j), G(i, j), H(i, j) }
F(i, j) = V(i – 1, j – 1) + s(xi, yj)
V(i, j – 1) – d G(i, j) = max
G(i, j – 1) – e
V(i – 1, j) – d H(i, j) = max
H(i – 1, j) – e
Termination: similar
Pairwise Alignment Tools
Some Typical Dot-plot Comparisons
• Divergent sequences where only a segment is homologous
• Long insertions and deletions• Tandem repeats
• The square shape of the pattern is characteristic of these repeats
Using Dotlet• Dotlet is one of the handiest tools for making
dot plots• Dotlet is a Java applet• Open and download the applet at the following
site: http://myhits.isb-sib.ch/cgi-bin/dotlet• Use Firefox or IE
Window size
Dot plot window
Alignment window
Threshold window for fine tuning
Window size
Dot plot window
Alignment window
Threshold window for fine tuning
Window size
Dot plot window
Alignment window
Threshold window for fine tuning
Looking at Repeated Domains with Dotlet
• The square shape is typical of tandem repeats
• The repeats are not perfect because the sequences have diverged after their duplication
Comparing a Gene and Its Product
• Eukaryotic genes are transcribed into RNA
• The RNA is then spliced to remove the introns’ sequences
• It may be necessary to compare the gene and its product
• Dotlet makes this comparative analysis easy
Lalign and BLAST• Lalign is like a very precise BLAST
• It works on only two sequences at a time
• You must provide both sequences
LaLignhttp://www.ch.embnet.org/software/LALIGN_form.html
Lalign Output• Lalign produces an output
similar to the alignment section of BLAST
• The E-value indicates the significance of each alignment
• Low E-value good alignment
Multiple Alignment
Example
4 Ways of Using MSAs . . .
4 More Ways of Using MSAs
Generalizing the Notion of Pairwise Alignment
• Alignment of 2 sequences is represented as a 2-row matrix• In a similar way, we represent alignment of 3 sequences
as a 3-row matrix
A T _ G C G _ A _ C G T _ A A T C A C _ A
• Score: more conserved columns, better alignment
Aligning Three Sequences• Same strategy as aligning
two sequences• Use a 3-D “”, with each
axis representing a sequence to align
• For global alignments, go from source to sink
source
sink
Architecture of 3-D Alignment Cell(i-1,j-1,k-1)
(i,j-1,k-1)
(i,j-1,k)
(i-1,j-1,k) (i-1,j,k)
(i,j,k)
(i-1,j,k-1)
(i,j,k-1)
Multiple Alignment: Dynamic Programming
• si,j,k = max
(x, y, z) is an entry in the 3-D scoring matrix
si-1,j-1,k-1 + (vi, wj, uk)
si-1,j-1,k + (vi, wj, _ )
si-1,j,k-1 + (vi, _, uk)
si,j-1,k-1 + (_, wj, uk)
si-1,j,k + (vi, _ , _)
si,j-1,k + (_, wj, _)
si,j,k-1 + (_, _, uk)
cube diagonal: no indels
face diagonal: one indel
edge diagonal: two indels
Multiple Alignment: Running Time
• For 3 sequences of length n, the run time is 7n3; O(n3)
• For k sequences, build a k-dimensional Manhattan, with run time (2k-1)(nk); O(2knk)
• Conclusion: dynamic programming approach for alignment between two sequences is easily extended to k sequences but it is impractical due to exponential running time
Sum of Pairs Score(SP-Score)
• Consider pairwise alignment of sequences ai and aj
imposed by a multiple alignment of k sequences • Denote the score of this suboptimal (not necessarily
optimal) pairwise alignment as s*(ai, aj)• Sum up the pairwise scores for a multiple alignment:
s(a1,…,ak) = Σi,j s*(ai, aj)
SP-Score: Examplea1
.ak
ATG-C-AATA-G-CATATATCCCATTT
ji
jik aaSaaS,
*1 ),()...(
2
nPairs of Sequences
A
A A11
1
G
C G1
Score=3 Score = 1 –
Column 1 Column 3
s s*(
To calculate each column:
Multiple Alignment Induces Pairwise Alignments
Every multiple alignment induces pairwise alignments
x: AC-GCGG-C y: AC-GC-GAG z: GCCGC-GAG
Induces:
x: ACGCGG-C; x: AC-GCGG-C; y: AC-GCGAGy: ACGC-GAC; z: GCCGC-GAG; z: GCCGCGAG
Reverse Problem: Constructing Multiple Alignment from Pairwise Alignments
Given 3 arbitrary pairwise alignments:
x: ACGCTGG-C; x: AC-GCTGG-C; y: AC-GC-GAGy: ACGC--GAC; z: GCCGCA-GAG; z: GCCGCAGAG
can we construct a multiple alignment that inducesthem?
Reverse Problem: Constructing Multiple Alignment from Pairwise Alignments
Given 3 arbitrary pairwise alignments:
x: ACGCTGG-C; x: AC-GCTGG-C; y: AC-GC-GAGy: ACGC--GAC; z: GCCGCA-GAG; z: GCCGCAGAG
can we construct a multiple alignment that inducesthem? NOT ALWAYS
Pairwise alignments may be inconsistent
Profile Representation of Multiple Alignment
- A G G C T A T C A C C T G T A G – C T A C C A - - - G C A G – C T A C C A - - - G C A G – C T A T C A C – G G C A G – C T A T C G C – G G
A 1 1 .8 C .6 1 .4 1 .6 .2G 1 .2 .2 .4 1T .2 1 .6 .2- .2 .8 .4 .8 .4
Multiple Alignment: Greedy Approach
• Choose most similar pair of strings and combine into a profile , thereby reducing alignment of k sequences to an alignment of of k-1 sequences/profiles. Repeat
• This is a heuristic greedy method
u1= ACGTACGTACGT…
u2 = TTAATTAATTAA…
u3 = ACTACTACTACT…
…
uk = CCGGCCGGCCGG
u1= ACg/tTACg/tTACg/cT…
u2 = TTAATTAATTAA…
…
uk = CCGGCCGGCCGG…
kk-1
Greedy Approach: Example
• Consider these 4 sequencess1 GATTCAs2 GTCTGAs3 GATATTs4 GTCAGC
Greedy Approach: Example (cont’d)
• There are = 6 possible alignments
2
4
s2 GTCTGAs4 GTCAGC (score = 2)
s1 GAT-TCAs2 G-TCTGA (score = 1)
s1 GAT-TCAs3 GATAT-T (score = 1)
s1 GATTCA--s4 G—T-CAGC(score = 0)
s2 G-TCTGAs3 GATAT-T (score = -1)
s3 GAT-ATTs4 G-TCAGC (score = -1)
Greedy Approach: Example (cont’d)
s2 and s4 are closest; combine:
s2 GTCTGAs4 GTCAGC
s2,4 GTCt/aGa/cA (profile)
s1 GATTCAs3 GATATTs2,4 GTCt/aGa/c
new set of 3 sequences:
Progressive Alignment
• Progressive alignment is a variation of greedy algorithm with a somewhat more intelligent strategy for choosing the order of alignments.
• Progressive alignment works well for close sequences, but deteriorates for distant sequences• Gaps in consensus string are permanent• Use profiles to compare sequences
ClustalW
• Popular multiple alignment tool today• ‘W’ stands for ‘weighted’ (different parts
of alignment are weighted differently).• Three-step process
1.) Construct pairwise alignments2.) Build Guide Tree3.) Progressive Alignment guided by the tree
Step 1: Pairwise Alignment
Step 2: Guide Tree
• Create Guide Tree using the similarity matrix
• ClustalW uses the neighbor-joining method
• Guide tree roughly reflects evolutionary relations
Step 3: Progressive Alignment• Start by aligning the two most similar
sequences• Following the guide tree, add in the next
sequences, aligning to the existing alignment• Insert gaps as necessary
Multiple Alignment: History
1975 SankoffFormulated multiple alignment problem and gave dynamic programming solution
1988 Carrillo-LipmanBranch and Bound approach for MSA
1990 Feng-DoolittleProgressive alignment
1994 Thompson-Higgins-Gibson-ClustalWMost popular multiple alignment program
1998 Morgenstern et al.-DIALIGNSegment-based multiple alignment
2000 Notredame-Higgins-Heringa-T-coffeeUsing the library of pairwise alignments
2004 MUSCLE
Practice of MSA
Choosing the Right Sequences• When building an alignment, it is your job to select the sequences• Two main factors when selecting sequences:
• Number of sequences• Nature of the sequences
• A reasonable number of sequences: 20 to 50• Ideal for most methods• Small alignments are easy to display and analyze
• Types of sequences• Well-selected sequences informative alignment
Some Guidelines for Choosing the Right Sequences
DNA or Proteins?• DNA sequences are harder to align than proteins
• DNA-comparison models are less sophisticated
• Most methods work for both DNA and proteins • The results are less useful for DNA
• If your DNA is coding, work on the translated proteins
• If sequences are homologous . . .• Along their entire length use progressive alignment methods (next slide)• In terms of local similarity use motif-discovery methods (end of chapter)
Choosing Sequences That Are Different Enough
• An alignment is useful if . . .• The sequences are correctly aligned • It can be used to produce trees, profiles, and structure predictions
• To obtain this result, the sequences must be• Not too similar • Not too different
• Sequences that are very similar . . .• Are easy to align correctly• Are not informative useless trees and profiles, bad predictions
• Sequences that are very different . . . • Are difficult to align • Are very informative good trees and profiles, good predictions
Steps
• Gathering right sequences• Compute MSA using servers/local programs• Evaluate the results visually• If it is hard to interpret
• Closer examination, remove trouble makers• Redo and trim if needed
Gathering Sequences with BLAST
• The most convenient way to select your sequences is to use a BLAST server
• Some BLAST servers are integrated with multiple-alignment methods:• www.expasy.ch (protein only)• srs.ebi.ac.uk (DNA/protein)• npsa-pbil.ibcp.fr
Gathering Sequences with BLAST
• Select some of the top sequences
• Evenly select some sequences down to the bottom
• The idea is to have many intermediate sequences
ExPASY• www.expasy.ch/tools/blast
>sp|P20472|PRVA_HUMAN Parvalbumin alpha OS=Homo sapiens GN=PVALB PE=1 SV=2 MSMTDLLNAEDIKKAVGAFSATDSFDHKKFFQMVGLKKKSADDVKKVFHMLDKDKSGFIE EDELGFILKGFSPDARDLSAKETKMLMAAGDKDGDGKIGVDEFSTLVAES >sp|P80079|PRVA_FELCA Parvalbumin alpha OS=Felis catus GN=PVALB PE=1 SV=2 MSMTDLLGAEDIKKAVEAFTAVDSFDYKKFFQMVGLKKKSPDDIKKVFHILDKDKSGFIE EDELGFILKGFYPDARDLSVKETKMLMAAGDKDGDGKIDVDEFFSLVAKS >sp|P02627|PRVA_RANES Parvalbumin alpha OS=Rana esculenta PE=1 SV=1 PMTDLLAAGDISKAVSAFAAPESFNHKKFFELCGLKSKSKEIMQKVFHVLDQDQSGFIEK EELCLILKGFTPEGRSLSDKETTALLAAGDKDGDGKIGVDEFVTLVSES >sp|P02626|PRVA_AMPME Parvalbumin alpha OS=Amphiuma means PE=1 SV=1 SMTDVIPEADINKAIHAFKAGEAFDFKKFVHLLGLNKRSPADVTKAFHILDKDRSGYIEE EELQLILKGFSKEGRELTDKETKDLLIKGDKDGDGKIGVDEFTSLVAES >sp|P02619|PRVB_ESOLU Parvalbumin beta OS=Esox lucius PE=1 SV=1 SFAGLKDADVAAALAACSAADSFKHKEFFAKVGLASKSLDDVKKAFYVIDQDKSGFIEED ELKLFLQNFSPSARALTDAETKAFLADGDKDGDGMIGVDEFAAMIKA >sp|P43305|PRVU_CHICK Parvalbumin, thymic CPV3 OS=Gallus gallus PE=1 SV=2 MSLTDILSPSDIAAALRDCQAPDSFSPKKFFQISGMSKKSSSQLKEIFRILDNDQSGFIE EDELKYFLQRFECGARVLTASETKTFLAAADHDGDGKIGAEEFQEMVQS >sp|Q91482|PRVB1_SALSA Parvalbumin beta 1 OS=Salmo salar PE=1 SV=1 MACAHLCKEADIKTALEACKAADTFSFKTFFHTIGFASKSADDVKKAFKVIDQDASGFIE VEELKLFLQNFCPKARELTDAETKAFLKAGDADGDGMIGIDEFAVLVKQ >sp|P02620|PRVB_MERME Parvalbumin beta OS=Merluccius merluccius PE=1 SV=1 AFAGILADADITAALAACKAEGSFKHGEFFTKIGLKGKSAADIKKVFGIIDQDKSDFVEE DELKLFLQNFSAGARALTDAETATFLKAGDSDGDGKIGVEEFAAMVKG >sp|P02622|PRVB_GADCA Parvalbumin beta OS=Gadus callarias PE=1 SV=1 AFKGILSNADIKAAEAACFKEGSFDEDGFYAKVGLDAFSADELKKLFKIADEDKEGFIEE DELKLFLIAFAADLRALTDAETKAFLKAGDSDGDGKIGVDEFGALVDKWGAKG
If Know Protein Sequences• www.expasy.ch/sprot/sprot-retrieve-
list.html
Aligning Your Sequences
• Aligning sequences correctly is very difficult• It’s hard to align protein sequences with less than 25%
identity (70% identity for DNA)
• All methods are approximate• Alignment methods use the progressive algorithm
• Compares the sequences two by two• Builds a guide tree• Aligns the sequences in the order indicated by the tree
Selecting a Method
• Many alternative methods exist for MSAs
• Most of them use the progressive algorithm• They all are approximate methods• None is guaranteed to deliver the best alignments
• All existing methods have pros and cons• ClustalW is the most popular (21,000 citations)• T-Coffee and ProbCons are more accurate but slower• MUSCLE is very fast, ideal for very large datasets
Selecting a Method (cont’d.)• It’s impossible to guess in advance which method will do
best.• Accuracy is merely an average estimation
• Methods are tested on reference datasets• Their accuracy is the average accuracy obtained on the reference
• The most accurate method can always be outperformed by a less accurate method on a given dataset.
• An alternative: Use consensus methods such as MCOFFEE
ClustalW
• www.ebi.ac.uk/clustalw• pir.georgetown.edu/pirwww/search/
multialn.shtml• www.ddbj.nig.ac.jp/search/clustalw-e.html
Tcoffee
• TCOFFEE: www.tcoffee.org• CORE: evaluate MSA• MCOFFEE: run many and combine• EXPRESSO: with structural information
Running Many Methods at Once• MCOFFEE is a a meta-method
• It runs all the individual MSA methods• It gathers all the produced MSAs• It combines the MSAs into a single MSA
• MCOFFEE is more accurate than any individual method
• Its color output lets you estimate the reliability of your MSA
• MCOFFEE is available on www.tcoffee.org
MCOFFEE Color Output• Red and orange residues are probably well
aligned• Yellow should be treated with caution• Green and blue are probably incorrectly aligned
MCOFFEE
TCOFFEE
TCOFFEE Results
Interpreting Your MSA• Don’t put blind trust in the output of the servers
• Specialists always edit their MSAs by hand
• You must always estimate the biological accuracy of your MSA• Use the color code of Tcoffee • Use the conservation patterns of ClustalW:
• ‘*’ Completely conserved position• ‘:’ Highly conserved position • ‘.’ Conserved position
• Use experimental knowledge of your proteins
Understanding Conserved Positions
Finding Information from Alignment
• Conserved regions• Insert/delete• Phylogenetic Reconstruction• Motif• …
>sp|P02586|TNNC2_RABIT Troponin C, skeletal muscle OS=Oryctolagus cuniculus GN=TNNC2 PE=1 SV=2 MTDQQAEARSYLSEEMIAEFKAAFDMFDADGGGDISVKELGTVMRMLGQTPTKEELDAII EEVDEDGSGTIDFEEFLVMMVRQMKEDAKGKSEEELAECFRIFDRNADGYIDAEELAEIF RASGEHVTDEEIESLMKDGDKNNDGRIDFDEFLKMMEGVQ>sp|P20472|PRVA_HUMAN Parvalbumin alpha OS=Homo sapiens GN=PVALB PE=1 SV=2 MSMTDLLNAEDIKKAVGAFSATDSFDHKKFFQMVGLKKKSADDVKKVFHMLDKDKSGFIE EDELGFILKGFSPDARDLSAKETKMLMAAGDKDGDGKIGVDEFSTLVAES >sp|P80079|PRVA_FELCA Parvalbumin alpha OS=Felis catus GN=PVALB PE=1 SV=2 MSMTDLLGAEDIKKAVEAFTAVDSFDYKKFFQMVGLKKKSPDDIKKVFHILDKDKSGFIE EDELGFILKGFYPDARDLSVKETKMLMAAGDKDGDGKIDVDEFFSLVAKS >sp|P02627|PRVA_RANES Parvalbumin alpha OS=Rana esculenta PE=1 SV=1 PMTDLLAAGDISKAVSAFAAPESFNHKKFFELCGLKSKSKEIMQKVFHVLDQDQSGFIEK EELCLILKGFTPEGRSLSDKETTALLAAGDKDGDGKIGVDEFVTLVSES >sp|P02626|PRVA_AMPME Parvalbumin alpha OS=Amphiuma means PE=1 SV=1 SMTDVIPEADINKAIHAFKAGEAFDFKKFVHLLGLNKRSPADVTKAFHILDKDRSGYIEE EELQLILKGFSKEGRELTDKETKDLLIKGDKDGDGKIGVDEFTSLVAES >sp|P02619|PRVB_ESOLU Parvalbumin beta OS=Esox lucius PE=1 SV=1 SFAGLKDADVAAALAACSAADSFKHKEFFAKVGLASKSLDDVKKAFYVIDQDKSGFIEED ELKLFLQNFSPSARALTDAETKAFLADGDKDGDGMIGVDEFAAMIKA >sp|P43305|PRVU_CHICK Parvalbumin, thymic CPV3 OS=Gallus gallus PE=1 SV=2 MSLTDILSPSDIAAALRDCQAPDSFSPKKFFQISGMSKKSSSQLKEIFRILDNDQSGFIE EDELKYFLQRFECGARVLTASETKTFLAAADHDGDGKIGAEEFQEMVQS >sp|Q91482|PRVB1_SALSA Parvalbumin beta 1 OS=Salmo salar PE=1 SV=1 MACAHLCKEADIKTALEACKAADTFSFKTFFHTIGFASKSADDVKKAFKVIDQDASGFIE VEELKLFLQNFCPKARELTDAETKAFLKAGDADGDGMIGIDEFAVLVKQ >sp|P02620|PRVB_MERME Parvalbumin beta OS=Merluccius merluccius PE=1 SV=1 AFAGILADADITAALAACKAEGSFKHGEFFTKIGLKGKSAADIKKVFGIIDQDKSDFVEE DELKLFLQNFSAGARALTDAETATFLKAGDSDGDGKIGVEEFAAMVKG >sp|P02622|PRVB_GADCA Parvalbumin beta OS=Gadus callarias PE=1 SV=1 AFKGILSNADIKAAEAACFKEGSFDEDGFYAKVGLDAFSADELKKLFKIADEDKEGFIEE DELKLFLIAFAADLRALTDAETKAFLKAGDSDGDGKIGVDEFGALVDKWGAKG
When Sequences Are Hard to Align
• Most MSA programs assume your sequences are related along their whole length
• When this assumption is not true, the progressive approach will not work
• The only alternative is to compare multiple sequences locally
Local Multiple-Comparison Methods
• Gibbs Sampler• Will make a local multiple alignment• Will ignore unrelated segments of your sequences• Ideal for finding DNA patterns such as promoters
• Motif discovery methods• Will look for motifs conserved in your sequences• The sequences do not need to be aligned
• The most popular motif-discovery methods:• TEIRESIAS, MEME, SMILE, PRATT
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