Algorithms on Sequences

Bioinformatics
<strong>Algorithms</strong> on Sequences
Potsdam June 2003
Jacques Nicolas
Matching with errors :
Comparison of sequences
Motivations
• Sequence comparison is a basic operation in
molecular biology.
• It occurs, for instance, in :
– Search in Data Banks
– Genome Assembly
– Phylogenic trees construction
– …
• The knowledge of principles of sequence
comparison is necessary for mastering the
interpretation of results given by softwares.

Sequence comparison and alignments
To compare sequences is to look for alignments
MALVEDNNAVAVSFSEEQEALVLKSWAILKKDSANIALRFFLKIFEVAPSASQMF-SF
: .: :...:: .: ... . ..:: . .. . .. ...:.: .:.:...: .:
MPIV-DTGSVA-PLSAAEKTKIRSAWAPVYSNYETSGVDILVKFFTSTPAAQEFFPKF
search in
a bank
local alignment
(blast)
assembly
of genomes
Today lecture
• Dynamic programming applied to
the comparison of sequences
• Alignments
• Substitution matrices
• Validation of comparison results
• Search heuristics
• Introduction to existing softwares
phylogeny
global alignment

Alignment of two sequences
• Matching of 2 subsequences
example : TACGT-AAT
TATGTGAAT
• 2 elementary operations
– substitution
– insertion / deletion : gap
Score of an alignment
• Necessary to «compare» 2 alignments
• Score evaluated as a function of costs of
elementary operations
– substitution cost
• DNA : match : +M
mismatch : -N
• Protein : substitution matrix
– insertion/deletion (indel) cost

Score computation
• Score is the sum of costs of elementary operations
of an alignment.
• Example : M = +5 N = -4 G = -3
GATACGT-AATGCATA
CTATGTGAATTT
5+5-4+5+5-3+5+5+5=28
Dynamic Programming : reminder
– Optimality principle
– Split a problem in simpler subproblems
A
B
C
D
optimal
partial
result
optimal
partial
result
optimal
partial
result
optimal
final solution
optimal
partial
result
optimal
partial
result
optimal
partial
result
optimal
partial
result
optimal
partial
result

DP applied to alignments
• Given 2 sequences :
– X (length = N) et Y (length = M)
• The score S N,M of alignment A N,M :
X 1 . . . X N
Y 1 . . . Y M
is calculated from 3 sub-alignments :
A N-1,M-1 A N,M-1 A N-1,M
X 1 . . . X N-1 X N
Y 1 . . . Y M-1 Y M
substitution
X1 . . . XN -
Y1 . . . YM-1 YM gap
X1 . . . XN-1 XN Y1 . . . YM -
Needleman & Wunsh’s algorithm (1)
gap
• global alignment between 2 sequences
acgtgcataaagccaggataccg
acggtcattagaccccgagataccg
ac.gtgcataa.agccag.gataccg
|| || ||| | | || | |||||||
acggt.cattagaccccgagataccg

Needleman & Wunsh’s algorithm (2)
X = a c t
Y = a g t
optimal a c
alignment a g
+
substitution (t,t)
optimal a c t
alignment a g t
MAX
optimal a c t
alignment a g
+
substitution (t,-)
optimal a c
alignment a g t
+
substitution (-,t)
Needleman & Wunsh’s algorithm (3)
act
agt
act
ag
act
a
ac
agt
ac
ag
ac
a
a
agt
a
ag
a
a

Needleman & Wunsh’s algorithm (4)
• The score S N,M is calculated from the
recurrence equation :
S I,J-1 - Gap
S I,J = Max S I-1,J-1 + Sub (X I ,Y J )
S I-1,J - Gap
Needleman & Wunsh’s algorithm (5)
T
A
G
A
C
T
T A G C T A
i,j
S I,J-1 - Gap
S I,J = Max S I-1,J-1 + Sub (XI ,YJ )
S I-1,J - Gap
Complexity :
filling a matrix of size N x M
Result : S N,M

Needleman & Wunsh’s algorithm (6)
T
A
G
A
C
T
T A A C T
0 -2 -4 -6 -8 -10
-2
-4
-6
-8
-10
-12
2
0
-2
-4
-6
-8
0
4
2
-2
2
-4
-6
0 -2
2 1 -1
0 4 2 0
-2
-4
2
0
6 4
4 8
S I,J-1 - Gap
S I,J = Max S I-1,J-1 + Sub (X I ,Y J )
S I-1,J - Gap
match M = 2
mismatch N = -2
Gap = 2
result = ?
Needleman & Wunsh’s algorithm (7)
T
A
G
A
C
T
T A A C T
0 -2 -4 -6 -8 -10
-2
-4
-6
-8
-10
-12
2 0
0
-2
-4
-6
-8
4
2
-2
2
-4
-6
0 -2
2 0 -2
0 4 2 0
-2
-4
2
0
6 4
4 8
Determination of
the optimal alignment
T
2
T
A
4
A
-
2
G
A
4
A
C
6
C
T
8
T

Smith & Waterman algorithm (1)
• local alignments between 2 sequences
ccggttcttcttacaacgtgcataaagccagcacaagaaagt
cgtagtacggtcattagaccccgcagtgagccttacgt
ac.gtgcataa.agccag
|| || ||| | | || |
acggt.cattagaccccg
Smith & Waterman algorithm (2)
• The score S N,M is calculated from the
recurrence equation :
0
S I,J-1 - Gap
S I,J = Max S I-1,J-1 + Sub (X I ,Y J )
S I-1,J - Gap
Dynamic
Programming

Smith & Waterman algorithm (3)
GCC-UCG
GCCAUUG
C A G C C U C G C U U A G
A 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0
A 0.0 1.0 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.7
U 0.0 0.0 0.8 0.3 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.7
G 0.0 0.0 1.0 0.3 0.0 0.0 0.7 1.0 0.0 0.0 0.7 0.7 1.0
C 1.0 0.0 0.0 2.0 1.3 0.3 1.0 0.3 2.0 0.7 0.3 0.3 0.3
C 1.0 0.7 0.0 1.0 3.0 1.7 1.3 1.0 1.3 1.7 0.3 0.0 0.0
A 0.0 2.0 0.7 0.3 1.7 2.7 1.3 1.0 0.7 1.0 1.3 1.3 0.0
U 0.0 0.7 1.7 0.3 1.3 2.7 2.3 1.0 0.7 1.7 2.0 1.0 1.0
U 0.0 0.3 0.3 1.3 1.0 2.3 2.3 2.0 0.7 1.7 2.7 1.7 1.0
G 0.0 0.0 1.3 0.0 1.0 1.0 2.0 3.3 2.0 1.7 1.3 2.3 2.7
A 0.0 1.0 0.0 1.0 0.3 0.7 0.7 2.0 3.0 1.7 1.3 2.3 2.0
C 1.0 0.0 0.7 1.0 2.0 0.7 1.7 1.7 3.0 2.7 1.3 1.0 2.0
G 0.0 0.7 1.0 0.3 0.7 1.7 0.3 2.7 1.7 2.7 2.3 1.0 2.0
G 0.0 0.0 1.7 0.7 0.3 0.3 1.3 1.3 2.3 1.3 2.3 2.0 2.0
Gotoh’s algorithm (1)
• More realistic modelling of « gaps »;
• Cost of a N characters gap :
– S & W : C N = N x G
– Gotoh : C N = G b + (N-1) x G x
G b = breaking cost
G x = extension cost
• What is the recurrence equation ?

E I,J = MAX
Gotoh’s algorithm (2)
S I,J = MAX
S I,J-1 - G B
E I,J-1 - G X
0
E I,J
F I,J
S I-1,J-1 + sub (X I , Y J)
F I,J = MAX
Cost of a substitution
S I-1,J - G B
F I-1,J - G X
• DNA Sequences
One consider 2 costs :
• cost of a match positive value
• cost of a mismatch negative value
example : BLAST :
default value = +5 and -4
• Proteins
Use a substitution matrix

Substitution Matrices
• Substitution matrices are 2D arrays used to
assign a similarity value between pairs of
characters (bases or amino acids) =
substitution cost
• Matrices are generally symetrical. The value of
the substitution of X with Y is considered to be
equal to th value of the substitution of Y with X.
Several types of matrices
• Identity Matrices
• Matrix based on the genetic code
• Substitution Matrices
– PAM (mutations, Dayhoff)
– BLOSUM (Henikoff)
• Physico-chemical Matrices
– Hydrophobicity
– 3D structure

Identity and default Blast Matrices
A 3
A
T
C
G
A T C G
1
0
0
0
0 0 0
1
0
0
0 0
1
0
0
1
A T C G
Identity matrix Reference matrix in BLAST
A
T
C
G
5
-4 -4 -4
5
-4 -4
Matrix based on the genetic code
A R N D C Q E G H I L K M F P S T W Y V
R 1 3
N 1 1 3
D 2 1 2 3
C 1 2 1 1 3
Q 1 2 1 1 0 3
E 2 1 1 2 0 2 3
G 2 2 1 2 2 1 2 3
H 1 2 2 2 1 2 1 1 3
I 1 2 2 1 1 1 1 1 1 3
L 1 2 1 1 1 2 1 1 2 2 3
K 1 2 2 1 0 2 2 1 1 2 1 3
M 1 2 1 0 0 1 1 1 0 2 2 2 3
F 1 1 1 1 2 0 0 1 1 2 2 0 1 3
P 2 2 1 1 1 2 1 1 2 1 2 1 1 1 3
S 2 2 2 1 2 1 1 2 1 2 2 1 1 2 2 3
T 2 2 2 1 1 1 1 1 1 2 1 2 2 1 2 2 3
W 1 2 0 0 2 1 1 2 0 0 2 1 1 1 1 2 1 3
Y 1 1 2 2 2 1 1 1 2 1 1 1 0 2 1 2 1 1 3
V 2 1 1 2 1 1 2 2 1 2 2 1 2 2 1 1 1 1 1 3
-4
-4
-4
-4
-4
5
-4
-4
5
(number of
modifications
to pass from
an amino acid
to another)

Dayhoff’s Matrices (PAM)
• M.O. Dayhoff, 1978, Atlas of Protein Sequence and
Structure. Initial matrix deduced from the alignment of
71 families of highly homologous proteins (>1000
sequences).
• Replacement frequency Matrix of each amino acid X with
one of the 19 others (relative frequency for
homogeneity reasons) = PAM (Percent Accepted
Mutation).
• The model of evolution of Dayhoff assumes that proteins
have diverged due to the accumulation of random
mutations. PAM N is the power N of the matrix, thus
simulating an evolution rate. Standard value of N =250
Each value is the probability
of substitution of a given aa
with another one. The greater
the value, the more likely the
replacment. 0 is equivalent to
a random mutation.

Other substitution matrices
• Physico-chemical properties may be used to
build score matrices.
• Chemical properties
• Hydrobobicity
• Secondary structures (membrane domains)
• Tertiary structures
• ...

Taylor’s Venn Diagram
For usual protein
properties
Validation of an alignment
• A computer program finds always a solution…
• What is the credit that may be assigned to
resulting alignments ?
– Validation from :
• the score ?
• the percentage of identities ?
• …
• Problem : How to decide that an alignment has
something to do with biological reality

Example 1 : easy case
ALIGN calculates a global alignment of two sequences
version 2.0uPlease cite: Myers and Miller, CABIOS (1989) 4:11-17
AF248645_1 150 aa vs.
AF156936_1 149 aa
scoring matrix: BLOSUM50, gap penalties: -12/-2
44.1% identity; Global alignment score: 428
10 20 30 40 50 60
/tmp/t MPIVDTGSVAPLSAAEKTKIRSAWAPVYSNYETSGVDILVKFFTSTPAAQEFFPKFKGLT
:::.: : . :: ..: :: .: .:.:.: .:. .:..:. ..:.::. ::::..
AF1569 MPITDQGPLPTLSEGDKKAIRESWPQIYQNFEQTGLVVLLEFLQKNPGAQQSFPKFSA--
10 20 30 40 50
70 80 90 100 110 120
/tmp/t TADQLKKSADVRWHAERIINAVNDAVVSMDDTEKMSMKLRDLSGKHAKSFQVDPQYFKVL
: .:... .:.:.: ::::::: .. :: :.. :..::.::.. :::::. :: :
AF1569 TKCNLEQDNEVKWQASRIINAVNHTIGLMDKEAAMKQYLKELSAKHSSEFQVDPKLFKEL
60 70 80 90 100 110
130 140 150
/tmp/t AAVIADTVAAGDAGFEKLMSMICILLRSAY--
.:....:. : :..:::.:.:: ::::.:
AF1569 SAIFVSTIR-GKAAYEKLFSIICTLLRSSYDE
120 130 140
Example 2 : opposite but easy…
ALIGN calculates a global alignment of two sequences
version 2.0uPlease cite: Myers and Miller, CABIOS (1989) 4:11-17
U13831_1 134 aa vs.
AF248645_1 150 aa
scoring matrix: BLOSUM50, gap penalties: -12/-2
15.8% identity; Global alignment score: -47
10 20 30 40 50
/tmp/t MTRDQNGTWEMESNENFEGYMKALDIDFATPKIAVRLTQTKVI---DQDGDNFKTK---T
: ..:. : :. : .: . . : . .: .. .: .
AF2486 MPIVDTGSVAPLS---------------AAEKTKIRSAWAPVYSNYETSGVDILVKFFTS
10 20 30 40
60 70 80 90 100 110
/tmp/t TSTFRNYDVDFTVGVEFDEYTKSLDNR-HVKALVTWEGDVLVCVQKGEKENRGWKQW---
: . ... : . :. :: : : :.. ... .:..: .. :: . ..
AF2486 TPAAQEFFPKFKGLTTADQLKKSADVRWHAERIINAVNDAVVSMDDTEKMSMKLRDLSGK
50 60 70 80 90 100
120 130
/tmp/t ----IEGDKLYLEL---------TCGD--------QVCRQVFKKK
.. : :... . :: ..: . .
AF2486 HAKSFQVDPQYFKVLAAVIADTVAAGDAGFEKLMSMICILLRSAY
110 120 130 140 150

Example 3 ???
ALIGN calculates a global alignment of two sequences
version 2.0uPlease cite: Myers and Miller, CABIOS (1989) 4:11-17
U76030_1 166 aa vs.
AF248645_1 150 aa
scoring matrix: BLOSUM50, gap penalties: -12/-2
20.6% identity; Global alignment score: 94
10 20 30 40 50
/tmp/t MALVEDNNAVAVSFSEEQEALVLKSWAILKKDSANIALRFFLKIFEVAPSASQMF-SF--
: .: :...:: .: ... . ..:: . .. . .. ...:.: .:.:...: .:
AF2486 MPIV-DTGSVA-PLSAAEKTKIRSAWAPVYSNYETSGVDILVKFFTSTPAAQEFFPKFKG
10 20 30 40 50
60 70 80 90 100 110
/tmp/t LRNSDVPLEKNPKLKTHAMSVFVMTCEAAAQLRKAGKVTVRDTTLKRLGATHLK-YGVGD
: ..: :.:. .. :: .. . .:.... . :.... :. :.. : : . :
AF2486 LTTAD-QLKKSADVRWHAERIINAVNDAVVSMDDTEKMSMK---LRDLSGKHAKSFQVDP
60 70 80 90 100 110
120 130 140 150 160
/tmp/t AHFEVVKFALLDTIKEEVPADMWSPAMKSAWSEAYDHLVAAIKQEMKPAE
.:.:. .. ::. .: . ....:.. : .. :
AF2486 QYFKVLAAVIADTV--------------AAGDAGFEKLMSMICILLRSAY
120 130 140 150
Validation of a global alignment
• alignment between S1 and S2 (with gaps)
score S
• Question : is S significant ?
comparison with a score between
random sequences

Validation of a global alignment (2)
S1, S2 score S
S’1, S’2 score S’
Random sequences produced by
• Permutations
• Markovian models
comparison ?
Standardized score : Z-score
Sq1, Sq2
S
Practically :
Significant
score
for Z > 6
Z
=
( S−m
)
σ
Mean
Sq1
Sq2 1 Sq2 2 ... Sq2 K
S 1 S 2 … S k
Standard
deviation

PRSS compares a test sequence to a shuffled sequence
version 2.0u64, May, 1998
s-w est
22 1 0:=
24 0 1:*
26 12 5:==*===
28 23 16:=======*====
30 38 34:================*==
32 68 53:==========================*=======
34 60 64:============================== *
36 68 66:================================*=
38 51 60:========================== *
40 58 51:=========================*===
42 32 40:================ *
44 28 31:============== *
46 12 23:====== *
48 17 17:========*
50 8 12:==== *
52 5 8:===*
54 7 6:==*=
56 3 4:=*
58 3 3:=*
60 1 2:*
62 2 1:*
64 0 1:*
66 1 1:*
68 0 0:
70 0 0:
72 1 0:=
74 0 0:
76 0 0:
78 0 0:
80 1 0:=
82 0 0:
PRSS compares a test sequence to a shuffled sequence
version 2.0u64, May, 1998
s-w est
< 20 0 0:
22 0 0:
24 3 1:*=
26 11 7:===*==
28 36 23:===========*======
30 57 46:======================*======
32 79 65:================================*=======
34 72 73:====================================*
36 59 69:============================== *
38 49 58:========================= *
40 33 46:================= *
42 28 34:============== *
44 22 24:===========*
46 17 17:========*
48 8 12:==== * O
50 6 8:===*
52 10 6:==*==
54 5 4:=*=
56 4 3:=*
58 1 2:*
60 0 1:*
62 0 1:*
64 0 1:*
66 0 0:
> 68 0 0:
67000 residues in 500 sequences,
BLOSUM50 matrix, gap penalties: -12,-2
unshuffled s-w score: 49; shuffled score range: 26 - 60
For 500 sequences, a score >=49 is expected 31 times
PRSS
example 1
74500 residues in 500 sequences,
BLOSUM50 matrix, gap penalties: -12,-2
unshuffled s-w score: 442; shuffled score range: 24 - 82
For 500 sequences, a score >=442 is expected 5.26e-30 times
PRSS
example 2

PRSS compares a test sequence to a shuffled sequence
version 2.0u64, May, 1998
s-w est
28 5 2:*==
30 11 10:====*=
32 45 25:============*==========
34 59 44:=====================*========
36 62 59:=============================*=
38 57 65:============================= *
40 65 62:==============================*==
42 49 55:========================= *
44 31 45:================ *
46 23 35:============ *
48 21 27:=========== *
50 21 20:=========*=
52 7 14:==== *
54 20 10:====*=====
56 6 7:===*
58 5 5:==*
60 5 4:=*=
62 2 3:=*
64 1 2:*
66 2 1:*
68 1 1:*
70 2 1:*
83000 residues in 500 sequences,
BLOSUM50 matrix, gap penalties: -12,-2
unshuffled s-w score: 114; shuffled score range: 29 - 72
For 500 sequences, a score >=114 is expected 0.000902 times
PRSS
example 3
Validation of a local alignment
• Karlin, S & Altschul, S.F. (1990) « Methods for assessing the
statistical significance of molecular sequence features by using
general scoring schemes » Proc. Natl. Acad. Sci. USA 87:2264-2268.
Altschul et al. 1994 «Issues in searching in molecular sequence
databases » Nature Genet. 6:119-129)
• BLAST : comparison sequence / bank
• To evaluate the significance of an optimal score without gap, one
uses a model of random sequences.
– m : size of sequence, n : size of bank
– The expected number of HSP with a score ≥ s is
E=
Kmne
−λs
where K and λ are constants, depending on the substitution matrix … -
RIINAVNDAVVMD
::::::: .. ::
RIINAVNHTIGMD
HSP
High Scoring Pair

Validation of a local alignment (2)
• E is an indication of the degree of surprise one gets
with the observed score : the highest it is, the least
significant is the score.
• A reasonable value of E is between 0.1 et 0.001
Biologists use generally 10 -4
• Blast default searches until 10
• One generally gives score results standardized with
respect to parameters K and λ :
S ' are called « bit scores »
s − ln K
S′
=
λ
Probability : P-value
ln 2
• The random number of HSP with a score ≥ s follows a
Poisson’s law, i.e. the probability to find exactly k HSP with a
score ≥ s is
e k E −
where E is the expected number of HSP previously defined.
• The p-value P associated to score s is the probability to find
at least one HSP :
−E
P
k! E
= 1−
For instance, if the expected number of HSP with a score ≥ s
is 3, the probability to find at least 1 HSP is 0,95.
e

BLASTN 1.4.7 [16-Oct-94] [Build 17:42:06 Mar 10 1995]
Reference: Altschul, Stephen F., Warren Gish, Webb Miller, Eugene W. Myers,
and David J. Lipman (1990). Basic local alignment search tool. J. Mol. Biol.
215:403-10.
Query= gb|X17217|ADAAVAR Avian adenovirus (CELO) DNA encoding VA
(virus-associated) RNA and six open reading frames.
(4898 letters)
Database: smallgenbank.fasta
100 sequences; 205,192 total letters.
Searching.................................................done
Smallest
Sum
High Probability
Sequences producing High-scoring Segment Pairs: Score P(N) N
gb AAUNKDNA 3576 Z17216 Avian adenovirus DNA (CEL06). ... 302
gb AAVSPHERE 8457 M77182 Amsacta entomopoxvirus sphero... 112
gb AAVSPHER 4657 M75889 Amsacta moorei entomopoxvirus ... 94
gb AAFVMAF 3171 M26769 Avian musculoaponeurotic fibros... 92
gb ACU10885 2773 U10885 AcMNPV HR3 p6.9 gene, partial ... 101
gb ACSJUN 1074 M16266 Avian sarcoma virus 17 proviral ... 98
Softwares based on dynamic
programming
• SSEARCH (Smith-Waterman)
• BESTFIT (Smith-Waterman)
• GAP (Needleman-Wunsh)
• ALIGN (Gotoh)
• ...
Not often used for the search in banks :
Computation time too high
BLAST
6.4e-17 1
0.0052 4
0.34 3
0.96 2
0.97 1
0.998 1
Necessary to find some « tricks»
to accelerate the search of alignments

Why DP is so expensive ?
DNA bank
10 6 sequences
(size sequence = 1000)
Dynamic Programming
10 3 x 10 3 matrix-cell
Search Heuristics
Scan :
Computation of
10 12 matrix-cells
50 ns
> 12 hours
• A heuristics is a mechanism that makes profit of an
assumption on data to accelerate a computation
• Assumption :
– an alignment includes at least a subword of size W
– example : W=5
ATGGCG.GTAGGCATAGGACTCA.TAC
||| || | ||||| ||| |||| ||
ATGTCGAGAAGGCACAGGTCTCAGGAC

Principle of the heuristics (1)
1 – Query sequence is splited in subwords
of size W , that are stored in a dictionnary
ATGGACTGGC
12345678
query sequence
ATG 1
TGG 2,7 W=3
GGA 3
GAC 4
ACT 5
CTG 6
GGC 8 dictionnary
Principle of the heuristics (2)
2 – For each sequence of the bank, subwords
of size W (hits) belonging to the dictionnary
are detected
1 sequence of the bank
AATCGGATTGCATAA
ATG 1
TGG 2,7
GGA 3
GAC 4
ACT 5
CTG 6
GGC 8 dictionnary

Principle of the heuristics (3)
3 – Each hit is « extended » to produce an
alignment
hit
Sequence of the bank
AATCGGATTGCATAA
ATGGACTGGC
Query sequence
alignment
ATCGGATTG
|| ||| ||
AT.GGACTG
Speed Gain of the heuristics
• Example : 2 sequences of DNA of size 1000
• Dynamic Programming :
– 1000 x 1000 = 10 6 matrix-cells
• Heuristics with W = 8
– statistically :
nb of hits = 10 6 x (1 / 4 8 ) = 15 (1 for W=10)
– 1000 search in the dictionnary (10 x 10 3 operations)
– 15 extensions (15 x 10 3 operations)
• Gain : 10 6 / 2.5 x 10 4 = 40 (87 for W=10)

Tradeoff sensitivity / rapidity
W
1 12
sensitivity
rapidity
• The highest W, the fastest the computation
•W is a parameter of softwares (Blast, Fasta)
Softwares searching in data banks
• Blast
– 2 implementations :
• NCBI BLAST, National Center for Biotechnology Information
• WU-BLAST, W. Gish, Washington University
• Fasta
– W. Pearson, University of Virginia
• …

BLAST
• Many sites
– check default parameters
• Main parameters :
υ W : size of subword
υ DNA : default value 11
υ Protein : default value 3
υ E : expected value
υ -G : gap opening (default 11)
υ -E : gap extension (default 1)
Various Blasts
NAME Query Bank Common Usage
BLASTN DNA DNA Look for identities
+pattern splicing
BLASTP Protein Proteins Search of homologous proteins
TBLASTN Protein DNA
translated
6 phases
BLASTX DNA
translated
TBLASTX DNA
translated
Search of genes to be annotated
in banks
Proteins Search of homologous genes and
proteins
DNA
translated
Discovery of the structure of
genes

Recent Blasts
• Blast2 (Gapped Blast,1997) : version of Blast with indels,
filtering Dust and Seg and threads for multi-processors.
• PSI-Blast (Position Specific Iterated Blast) : iterative
version of Blast in view of phylogeny studies, where the comparison
matrix used at one step takes into account consensus found at the
previous step, for a multiple alignment of sequences with a sufficient
E-value, until the processus has converged.
• PHI-Blast (Pattern-Hit Initiated Blast) : fast version of
Blast, that may be combined with PSI-Blast, where one gives a
pattern (regular expression Prosite-like) that must be used in the
alignment of sequences)
Dotplots :
The simplest way to see alignments
Dotplot = identity matrix of two sequences
Similarities =
diagonals
• • • • • • •
•
•
•
Filtering, Smoothing
(window 4, 1 error)
DNA /cDNA of actin gene from
muscle of Pisaster ochraceus
(horizontal / vertical)

Dotter : a tool for dot plots
• Karolinska Institutet, unix
& windows versions
ftp://ftp.cgr.ki.se/pub/esr
/dotter/
• Complexity : linear for
space, quadratic for time
(15 mn for 30000x30000)
« A dot-matrix program with dynamic
theshold control suited for genomic DNA and
protein sequences » E. Sonnhammer R. Durbin
Gene 167: GC1-10 1995