1. CS 6293 Advanced Topics: Translational Bioinformatics
Lectures 3-4: Pair-wise Sequence Alignment

2. Outline Biological background Global sequence alignment
Local sequence alignment
Optional: linear-space alignment algorithm
Heuristic alignment: BLAST

3. Evolution at the DNA levelC
…ACGGTGCAGTCACCA…
…ACGTTGC-GTCCACCA…
DNA evolutionary events (sequence edits):
Mutation, deletion, insertion

4. Sequence conservation implies function
next generation OK OK OK X X
Still OK?

5. Why sequence alignment?
Conserved regions are more likely to be functional
Can be used for finding genes, regulatory elements, etc.
Similar sequences often have similar origin and function
Can be used to predict functions for new genes / proteins
Sequence alignment is one of the most widely used computational tools in biology

6. Global Sequence Alignment
AGGCTATCACCTGACCTCCAGGCCGATGCCC
TAGCTATCACGACCGCGGTCGATTTGCCCGAC T S’
-AGGCTATCACCTGACCTCCAGGCCGA--TGCCC---
TAG-CTATCAC--GACCGC--GGTCGATTTGCCCGAC T’
Definition
An alignment of two strings S, T is a pair of strings S’, T’ (with spaces) s.t.
|S’| = |T’|, and (|S| = “length of S”)
removing all spaces in S’, T’ leaves S, T

7. What is a good alignment?
The “best” way to match the letters of one sequence with those of the other
How do we define “best”?

8. The score of aligning (characters or spaces) x & y is σ (x,y).
S’: -AGGCTATCACCTGACCTCCAGGCCGA--TGCCC---
T’: TAG-CTATCAC--GACCGC--GGTCGATTTGCCCGAC
The score of aligning (characters or spaces) x & y is σ (x,y).
Score of an alignment:
An optimal alignment: one with max score

9. Scoring Function Sequence edits: AGGCCTC Scoring Function:
Mutations AGGACTC
Insertions AGGGCCTC
Deletions AGG-CTC
Scoring Function:
Match: +m ~~~AAC~~~
Mismatch: -s ~~~A-A~~~
Gap (indel): -d

10. Match = 2, mismatch = -1, gap = -1
Score = 3 x 2 – 2 x 1 – 1 x 1 = 3

11. More complex scoring function
Substitution matrix
Similarity score of matching two letters a, b should reflect the probability of a, b derived from the same ancestor
It is usually defined by log likelihood ratio
Active research area. Especially for proteins.
Commonly used: PAM, BLOSUM

12. An example substitution matrixC G T 3 -2 -1

13. How to find an optimal alignment?
A naïve algorithm:
for all subseqs A of S, B of T s.t. |A| = |B| do
align A[i] with B[i], 1 ≤i ≤|A|
align all other chars to spaces
compute its value
retain the max
end
output the retained alignment
S = abcd A = cd
T = wxyz B = xz
-abc-d a-bc-d
w--xyz -w-xyz

14. Analysis Assume |S| = |T| = n Cost of evaluating one alignment: ≥n
How many alignments are there:
pick n chars of S,T together
say k of them are in S
match these k to the k unpicked chars of T
Total time:
E.g., for n = 20, time is > 240 >1012 operations

15. Dynamic Programming for sequence alignment
Suppose we wish to align
x1……xM
y1……yN
Let F(i,j) = optimal score of aligning
x1……xi
y1……yj
Scoring Function:
Match: +m
Mismatch: -s
Gap (indel): -d

16. Optimal substructure ... x: y:1 2 i M j N x: y:
If x[i] is aligned to y[j] in the optimal alignment between x[1..M] and y[1..N], then
The alignment between x[1..i] and y[1..j] is also optimal
Easy to prove by contradiction

17. Recursive solution ~~~~~~~ yN ~~~~~~~  ~~~~~~~ yN max
Notice three possible cases:
xM aligns to yN
~~~~~~~ xM
~~~~~~~ yN
2. xM aligns to a gap
~~~~~~~ xM
~~~~~~~ 
yN aligns to a gap
~~~~~~~ yN
m, if xM = yN
F(M,N) = F(M-1, N-1) +
-s, if not
max
F(M,N) = F(M-1, N) - d
F(M,N) = F(M, N-1) - d

18. Recursive solution Generalize: F(i,j) = max F(i-1, j) – d
F(i-1, j-1) + (Xi,Yj)
F(i,j) = max F(i-1, j) – d
F(i, j-1) – d
(Xi,Yj) = m if Xi = Yj, and –s otherwise
Boundary conditions:
F(0, 0) = 0.
F(0, j) = ?
F(i, 0) = ?
-jd: y[1..j] aligned to gaps.
-id: x[1..i] aligned to gaps.

19. What order to fill? F(0,0) F(M,N) F(i, j) F(i, j-1) F(i-1, j)2 3 i j

20. What order to fill?
F(0,0)
F(M,N)

21. Example A G T F(i,j) i = 0 1 2 3 4 j = 0 1 2 3 x = AGTA m = 1
y = ATA s = 1
d = 1
F(i,j) i = A G T
j = 0 1 2 3

22. Example A G T -1 -2 -3 -4 F(i,j) i = 0 1 2 3 4 j = 0 1 2 3
x = AGTA m = 1
y = ATA s = 1
d = 1
F(i,j) i = A G T -1 -2 -3 -4
j = 0 1 2 3

23. Example A G T -1 -2 -3 -4 1 F(i,j) i = 0 1 2 3 4 j = 0 1 2 3
x = AGTA m = 1
y = ATA s = 1
d = 1
F(i,j) i = A G T -1 -2 -3 -4 1
j = 0 1 2 3

24. Example A G T -1 -2 -3 -4 1 F(i,j) i = 0 1 2 3 4 j = 0 1 2 3
x = AGTA m = 1
y = ATA s = 1
d = 1
F(i,j) i = A G T -1 -2 -3 -4 1
j = 0 1 2 3

25. Example A G T -1 -2 -3 -4 1 2 F(i,j) i = 0 1 2 3 4 Optimal Alignment:
x = AGTA m = 1
y = ATA s = 1
d = 1
F(i,j) i = A G T -1 -2 -3 -4 1 2
j = 0
Optimal Alignment:
F(4,3) = 2 1 2 3

26. Example A G T -1 -2 -3 -4 1 2 F(i,j) i = 0 1 2 3 4 Optimal Alignment:
x = AGTA m = 1
y = ATA s = 1
d = 1
F(i,j) i = A G T -1 -2 -3 -4 1 2
Optimal Alignment:
F(4,3) = 2
This only tells us the best score
j = 0 1 2 3

27. Trace-back A G T -1 -2 -3 -4 1 2 A F(i,j) i = 0 1 2 3 4 j = 0 1 2 3
x = AGTA m = 1
y = ATA s = 1
d = 1
F(i-1, j-1) + (Xi,Yj)
F(i,j) = max F(i-1, j) – d
F(i, j-1) – d
F(i,j) i = A G T -1 -2 -3 -4 1 2
j = 0 1 A 2 3

28. Trace-back A G T -1 -2 -3 -4 1 2 T A F(i,j) i = 0 1 2 3 4 j = 0 1 2 3
x = AGTA m = 1
y = ATA s = 1
d = 1
F(i-1, j-1) + (Xi,Yj)
F(i,j) = max F(i-1, j) – d
F(i, j-1) – d
F(i,j) i = A G T -1 -2 -3 -4 1 2
j = 0 1 T A 2 3

29. Trace-back A G T -1 -2 -3 -4 1 2 G T A - F(i,j) i = 0 1 2 3 4 j = 0 1
x = AGTA m = 1
y = ATA s = 1
d = 1
F(i-1, j-1) + (Xi,Yj)
F(i,j) = max F(i-1, j) – d
F(i, j-1) – d
F(i,j) i = A G T -1 -2 -3 -4 1 2
j = 0 1 G T A - 2 3

30. Trace-back A G T -1 -2 -3 -4 1 2 A G T - F(i,j) i = 0 1 2 3 4 j = 0 1
x = AGTA m = 1
y = ATA s = 1
d = 1
F(i-1, j-1) + (Xi,Yj)
F(i,j) = max F(i-1, j) – d
F(i, j-1) – d
F(i,j) i = A G T -1 -2 -3 -4 1 2
j = 0 1 A G T - 2 3

31. Trace-back A G T -1 -2 -3 -4 1 2 F(i,j) i = 0 1 2 3 4
x = AGTA m = 1
y = ATA s = 1
d = 1
F(i-1, j-1) + (Xi,Yj)
F(i,j) = max F(i-1, j) – d
F(i, j-1) – d
F(i,j) i = A G T -1 -2 -3 -4 1 2
j = 0
Optimal Alignment:
F(4,3) = 2
AGTA
ATA 1 2 3

32. Using trace-back pointers
x = AGTA m = 1
y = ATA s = 1
d = 1
F(i,j) i = A G T -1 -2 -3 -4
j = 0 1 2 3

33. Using trace-back pointers
x = AGTA m = 1
y = ATA s = 1
d = 1
F(i,j) i = A G T -1 -2 -3 -4 1
j = 0 1 2 3

34. Using trace-back pointers
x = AGTA m = 1
y = ATA s = 1
d = 1
F(i,j) i = A G T -1 -2 -3 -4 1
j = 0 1 2 3

35. Using trace-back pointers
x = AGTA m = 1
y = ATA s = 1
d = 1
F(i,j) i = A G T -1 -2 -3 -4 1 2
j = 0 1 2 3

36. Using trace-back pointers
x = AGTA m = 1
y = ATA s = 1
d = 1
F(i,j) i = A G T -1 -2 -3 -4 1 2
j = 0 1 2 3

37. Using trace-back pointers
x = AGTA m = 1
y = ATA s = 1
d = 1
F(i,j) i = A G T -1 -2 -3 -4 1 2
j = 0 1 2 3

38. Using trace-back pointers
x = AGTA m = 1
y = ATA s = 1
d = 1
F(i,j) i = A G T -1 -2 -3 -4 1 2
j = 0 1 2 3

39. Using trace-back pointers
x = AGTA m = 1
y = ATA s = 1
d = 1
F(i,j) i = A G T -1 -2 -3 -4 1 2
j = 0
Optimal Alignment:
F(4,3) = 2
AGTA
ATA 1 2 3

40. The Needleman-Wunsch Algorithm
Initialization.
F(0, 0) = 0
F(0, j) = - j  d
F(i, 0) = - i  d
Main Iteration. Filling in scores
For each i = 1……M
For each j = 1……N
F(i-1,j) – d [case 1]
F(i, j) = max F(i, j-1) – d [case 2]
F(i-1, j-1) + σ(xi, yj) [case 3]
UP, if [case 1]
Ptr(i,j) = LEFT if [case 2]
DIAG if [case 3]
Termination. F(M, N) is the optimal score, and from Ptr(M, N) can trace back optimal alignment

41. Complexity Time: Space:
O(NM)
Space:
Linear-space algorithms do exist (with the same time complexity)

42. Equivalent graph problem
S1 = A G T A
(0,0)
: a gap in the 2nd sequence
: a gap in the 1st sequence
: match / mismatch 1 1
S2 = A 1 T
Value on vertical/horizontal line: -d
Value on diagonal: m or -s 1 1 A
(3,4)
Number of steps: length of the alignment
Path length: alignment score
Optimal alignment: find the longest path from (0, 0) to (3, 4)
General longest path problem cannot be found with DP. Longest path on this graph can be found by DP since no cycle is possible.

43. A variant of the basic algorithm
Scoring scheme: m = s = d: 1
Seq1: CAGCA-CTTGGATTCTCGG
|| |:|||
Seq2: ---CAGCGTGG
Seq1: CAGCACTTGGATTCTCGG
|||| | | ||
Seq2: CAGC-----G-T----GG
The first alignment may be biologically more realistic in some cases (e.g. if we know s2 is a subsequence of s1)
Score = -7
Score = -2

44. A variant of the basic algorithm
Maybe it is OK to have an unlimited # of gaps in the beginning and end:
CTATCACCTGACCTCCAGGCCGATGCCCCTTCCGGC
GCGAGTTCATCTATCAC--GACCGC--GGTCG
Then, we don’t want to penalize gaps in the ends

45. The Overlap Detection variant
Changes:
Initialization
For all i, j,
F(i, 0) = 0
F(0, j) = 0
Termination
maxi F(i, N)
FOPT = max maxj F(M, j)
x1 ……………………………… xM
yN ……………………………… y1

46. Different types of overlapsx x y y

47. The local alignment problem
Given two strings X = x1……xM,
Y = y1……yN
Find substrings x’, y’ whose similarity (optimal global alignment value) is maximum
e.g. X = abcxdex X’ = cxde
Y = xxxcde Y’ = c-de x y

48. Why local alignment Conserved regions may be a small part of the whole
Global alignment might miss them if flanking “junk” outweighs similar regions
Genes are shuffled between genomes A B C D B D A C

49. Naïve algorithm for all substrings X’ of X and Y’ of Y
Align X’ & Y’ via dynamic programming
Retain pair with max value
end ;
Output the retained pair
Time: O(n2) choices for A, O(m2) for B, O(nm) for DP, so O(n3m3 ) total.

50. Reminder The overlap detection algorithm
We do not give penalty to gaps at either end
Free gap
Free gap

51. The local alignment idea
Do not penalize the unaligned regions (gaps or mismatches)
The alignment can start anywhere and ends anywhere
Strategy: whenever we get to some low similarity region (negative score), we restart a new alignment
By resetting alignment score to zero

52. The Smith-Waterman algorithm
Initialization: F(0, j) = F(i, 0) = 0
F(i – 1, j) – d
F(i, j – 1) – d
F(i – 1, j – 1) + (xi, yj)
Iteration: F(i, j) = max

53. The Smith-Waterman algorithm
Termination:
If we want the best local alignment…
FOPT = maxi,j F(i, j)
If we want all local alignments scoring > t
For all i, j find F(i, j) > t, and trace back

54. x c d e a b
Match: 2
Mismatch: -1
Gap: -1

55. x c d e a b
Match: 2
Mismatch: -1
Gap: -1

56. x c d e a b 2 1
Match: 2
Mismatch: -1
Gap: -1

57. x c d e a b 2 1
Match: 2
Mismatch: -1
Gap: -1

58. x c d e a b 2 1 3
Match: 2
Mismatch: -1
Gap: -1

59. x c d e a b 2 1 3 5
Match: 2
Mismatch: -1
Gap: -1

60. x c d e a b 2 1 3 5 4
Match: 2
Mismatch: -1
Gap: -1

61. Trace back x c d e a b 2 1 3 5 4
Match: 2
Mismatch: -1
Gap: -1

62. Trace back x c d e a b 2 1 3 5 4 cxde | || c-de x-de | || xcdea b 2 1 3 5 4
Match: 2
Mismatch: -1
Gap: -1
cxde
| ||
c-de
x-de
| ||
xcde

63. No negative values in local alignment DP array
Optimal local alignment will never have a gap on either end
Local alignment: “Smith-Waterman”
Global alignment: “Needleman-Wunsch”

64. Analysis Time: Memory: O(MN) for finding the best alignment
Time to report all alignments depends on the number of sub-opt alignments
Memory:
O(MN)
O(M+N) possible

65. Optional: more efficient alignment algorithms

66. Given two sequences of length M, N Time: O(MN) Space: O(MN)
Ok (?)
Space: O(MN)
bad
1Mb seq x 1Mb seq = 1000G memory
Can we do better?

67. Bounded alignment
Good alignment should appear near the diagonal

68. Bounded Dynamic Programming
If we know that x and y are very similar
Assumption: # gaps(x, y) < k xi
Then, | implies | i – j | < k yj

69. Bounded Dynamic Programming
Initialization:
F(i,0), F(0,j) undefined for i, j > k
Iteration:
For i = 1…M
For j = max(1, i – k)…min(N, i+k)
F(i – 1, j – 1)+ (xi, yj)
F(i, j) = max F(i, j – 1) – d, if j > i – k
F(i – 1, j) – d, if j < i + k
Termination: same
x1 ………………………… xM
yN ………………………… y1 k

70. Analysis Time: O(kM) << O(MN) Space: O(kM) with some tricks
=> M M 2k 2k

72. Given two sequences of length M, N Time: O(MN) Space: O(MN)ok
Space: O(MN)
bad
1mb seq x 1mb seq = 1000G memory
Can we do better?

73. Linear space algorithm
If all we need is the alignment score but not the alignment, easy!
We only need to keep two rows
(You only need one row, with a little trick)
But how do we get the alignment?

74. Linear space algorithm
When we finish, we know how we have aligned the ends of the sequences XM YN
Naïve idea: Repeat on the smaller subproblem F(M-1, N-1)
Time complexity: O((M+N)(MN))

75. (0, 0)
M/2
(M, N)
Key observation: optimal alignment (longest path) must use an intermediate point on the M/2-th row. Call it (M/2, k), where k is unknown.

76. (0,0)
(3,0)
(3,2)
(3,4)
(3,6)
(6,6)
Longest path from (0, 0) to (6, 6) is max_k (LP(0,0,3,k) + LP(6,6,3,k))

77. Hirschberg’s idea Divide and conquer! Forward algorithmY X
Forward algorithm
Align x1x2…xM/2 with Y
M/2
F(M/2, k) represents the best alignment between x1x2…xM/2 and y1y2…yk

78. Backward Algorithm Backward algorithmY X
Backward algorithm
Align reverse(xM/2+1…xM) with reverse(Y)
M/2
B(M/2, k) represents the best alignment between reverse(xM/2+1…xM) and reverse(ykyk+1…yN )

79. Linear-space alignment
Using 2 (4) rows of space, we can compute
for k = 1…N, F(M/2, k), B(M/2, k)
M/2

80. Linear-space alignment
Now, we can find k* maximizing F(M/2, k) + B(M/2, k)
Also, we can trace the path exiting column M/2 from k*
Conclusion: In O(NM) time, O(N) space, we found optimal alignment path at row M/2

81. Linear-space alignment
Iterate this procedure to the two sub-problems!
M/2 k*
M/2
N-k*

82. Analysis
Memory: O(N) for computation, O(N+M) to store the optimal alignment
Time:
MN for first iteration
k M/2 + (N-k) M/2 = MN/2 for second … k
M/2
M/2
N-k

83. MN + MN/2 + MN/4 + MN/8 + … = MN (1 + ½ + ¼ + 1/8 + 1/16 + …)
= 2MN = O(MN)
MN/8

84. Heuristic Local Sequence Alignment: BLAST

85. State of biological databases
Sequenced Genomes:
Human 109 Yeast 1.2107
Mouse 2.7109 Rat 2.6109
Neurospora 4107 Fugu fish 3.3108
Tetraodon 3108 Mosquito 2.8108
Drosophila 1.2108 Worm 1.0108
Rice 1.0109 Arabidopsis 1.2108
sea squirts 108
Current rate of sequencing (before new-generation sequencing):
4 big labs  3 109 bp /year/lab
10s small labs
Private sectors
With new-generation sequencing:
Easily generating billions of reads daily

86. Some useful applications of alignments
Given a newly discovered gene,
- Does it occur in other species?
Assume we try Smith-Waterman:
Our new gene
104
The entire genomic database
May take several weeks!

87. Some useful applications of alignments
Given a newly sequenced organism,
- Which subregions align with other organisms?
- Potential genes
- Other functional units
Assume we try Smith-Waterman:
Our newly sequenced mammal
3109
The entire genomic database
> 1000 years ???

88. BLAST Basic Local Alignment Search Tool
Altschul, Gish, Miller, Myers, Lipman, J Mol Biol 1990
The most widely used bioinformatics tool
Which is better: long mediocre match or a few nearby, short, strong matches with the same total score?
Score-wise, exactly equivalent
Biologically, later may be more interesting, & is common
At least, if must miss some, rather miss the former
BLAST is a heuristic algorithm emphasizing the later
speed/sensitivity tradeoff: BLAST may miss former, but gains greatly in speed

89. BLAST
Available at NCBI (National Center for Biotechnology Information) for download and online use.
Along with many sequence databases
Main idea:
Construct a dictionary of all the words in the query
Initiate a local alignment for each word match between query and DB
Running Time: O(MN)
However, orders of magnitude faster than Smith-Waterman
query DB

90. BLAST  Original Version
Dictionary:
All words of length k (~11 for DNA, 3 for proteins)
Alignment initiated between words of alignment score  T (typically T = k)
Alignment:
Ungapped extensions until score
below statistical threshold
Output:
All local alignments with score
> statistical threshold ……
query ……
scan DB
query

91. BLAST  Original Version
A C G A A G T A A G G T C C A G T
Example:
k = 4, T = 4
The matching word GGTC initiates an alignment
Extension to the left and right with no gaps until alignment falls < 50%
Output:
GTAAGGTCC
GTTAGGTCC
C C C T T C C T G G A T T G C G A

92. Gapped BLAST Added features: Pairs of words can initiate alignment
A C G A A G T A A G G T C C A G T
Added features:
Pairs of words can initiate alignment
Extensions with gaps in a band around anchor
Output:
GTAAGGTCCAGT
GTTAGGTC-AGT
C T G A T C C T G G A T T G C G A

93. Example Query: gattacaccccgattacaccccgattaca (29 letters) [2 mins]
Database: All GenBank+EMBL+DDBJ+PDB sequences (but no EST, STS, GSS, or phase 0, 1 or 2 HTGS sequences) 1,726,556 sequences; 8,074,398,388 total letters
>gi| |gb|AC | Oryza sativa chromosome 3 BAC OSJNBa0087C10 genomic sequence, complete sequence Length = Score = 34.2 bits (17), Expect = 4.5 Identities = 20/21 (95%) Strand = Plus / Plus
Query: tacaccccgattacaccccga 24
||||||| |||||||||||||
Sbjct: tacacccagattacaccccga
Score = 34.2 bits (17),
Expect = 4.5 Identities = 20/21 (95%) Strand = Plus / Plus
Query: tacaccccgattacaccccga 24
Sbjct: tacacccagattacaccccga
>gi| |gb|AC | Oryza sativa chromosome 3 BAC OSJNBa0052F07 genomic sequence, complete sequence Length = Score = 34.2 bits (17), Expect = 4.5 Identities = 20/21 (95%) Strand = Plus / Plus
Sbjct: tacacccagattacaccccga 3911

94. Example Query: Human atoh enhancer, 179 letters [1.5 min]
Result: 57 blast hits
gi| |gb|AF |AF Homo sapiens ATOH1 enhanc e-95
gi| |gb|AC | Mus musculus Strain C57BL6/J ch e-68
gi| |gb|AF |AF Mus musculus Atoh1 enhanc e-66
gi| |gb|AF | Gallus gallus CATH1 (CATH1) gene e-12
gi| |emb|AL | Zebrafish DNA sequence from clo e-05
gi| |gb|AC | Oryza sativa chromosome 10 BAC O
gi| |ref|NM_ | Mus musculus suppressor of Ty
gi| |gb|BC | Mus musculus, Similar to suppres
gi| |gb|AF |AF Mus musculus Atoh1 enhancer sequence Length = 1517
Score = 256 bits (129), Expect = 9e-66 Identities = 167/177 (94%),
Gaps = 2/177 (1%) Strand = Plus / Plus
Query: 3 tgacaatagagggtctggcagaggctcctggccgcggtgcggagcgtctggagcggagca 62
||||||||||||| ||||||||||||||||||| ||||||||||||||||||||||||||
Sbjct: 1144 tgacaatagaggggctggcagaggctcctggccccggtgcggagcgtctggagcggagca 1203
Query: 63 cgcgctgtcagctggtgagcgcactctcctttcaggcagctccccggggagctgtgcggc 122
|||||||||||||||||||||||||| ||||||||| |||||||||||||||| |||||
Sbjct: 1204 cgcgctgtcagctggtgagcgcactc-gctttcaggccgctccccggggagctgagcggc 1262
Query: 123 cacatttaacaccatcatcacccctccccggcctcctcaacctcggcctcctcctcg 179
||||||||||||| || ||| |||||||||||||||||||| |||||||||||||||
Sbjct: 1263 cacatttaacaccgtcgtca-ccctccccggcctcctcaacatcggcctcctcctcg 1318

95. Different types of BLAST
blastn: search nucleic acid databases
blastp: search protein databases
blastx: you give a nucleic acid sequence, search protein databases
tblastn: you give a protein sequence, search nucleic acid databases
tblastx: you give a nucleic sequence, search nucleic acid database, implicitly translate both into protein sequences

96. BLAST cons and pros Advantages Disadvantages New improvement Fast!!!!
A few minutes to search a database of 1011 bases
Disadvantages
Sensitivity may be low
Often misses weak homologies
New improvement
Make it even faster
Mainly for aligning very similar sequences or really long sequences
E.g. whole genome vs whole genome
Make it more sensitive
PSI-BLAST: iteratively add more homologous sequences
PatternHunter: discontinuous seeds

97. Variants of BLAST NCBI-BLAST: most widely used version
WU-BLAST: (Washington University BLAST): another popular version
Optimized, added features
MEGABLAST:
Optimized to align very similar sequences. Linear gap penalty
BLAT: Blast-Like Alignment Tool
BlastZ:
Optimized for aligning two genomes
PSI-BLAST:
BLAST produces many hits
Those are aligned, and a pattern is extracted
Pattern is used for next search; above steps iterated
Sensitive for weak homologies
Slower

98. Things we’ve covered so far
Global alignment
Needleman-Wunsch and variants
Local Alignment
Smith-Waterman
Improvement on space and time
Heuristic algorithms
BLAST families
Things we did not cover:
Statistics for sequence alignment
To handle gaps more accurately: affine gap penalty
Multiple sequence alignment