This document provides an outline of basic concepts in bioinformatics including sequence alignment, scoring alignments, inserting gaps, dynamic programming, and database searches. It discusses comparing biological sequences to determine similarity and homology for predicting gene/protein function and constructing phylogenies. Scoring matrices like BLOSUM and PAM are described for quantifying sequence similarity. Dynamic programming algorithms like Needleman-Wunsch and Smith-Waterman are summarized for global and local sequence alignment. Database search tools like FASTA and BLAST are introduced for searching sequence databases.

1. Course: B.Sc Biochemistry
Subject: Basic of Bioinformatics
Unit: III

2. OUTLINE
 Sequence Alignment
 Scoring Alignments and Substitution Matrices
 Inserting Gaps
 Dynamic Programming
 Database Searches

3. Sequence Alignment
 Comparing sequences for
– Similarity
– Homology
 Prediction of function of genes and proteins
 Construction of phylogeny
 Finding motifs

4. Sequence Alignment - HOMOLOGY
 Orthologues : any gene pairwise relation
where the ancestor node is a speciation
event. Often have similar function
 Paralogues : any gene pairwise relation
where the ancestor node is a duplication
event. Paralogs tend to have different
functions

5. Sequence Alignment - HOMOLOGY
1.

6. Sequence Alignment - HOMOLOGY
2.

7. Sequence Alignment - PHYLOGENY
3.

8. Sequence Alignment – PROTEIN
FUNCTIONS
4.

9. Scoring Alignments and Substitution
Matrices
 The quality of an alignment is measured by
giving it a quantitative score
 The simplest way of quatifying similarity
between two sequences is percentage
identity.
– Simply measured by counting the number of
identical bases or amino acids matched
between the aligned sequences.

10. Scoring Alignments and Substitution
Matrices
 The dot-plot
gives a visual
assesment of
similarity based
on identity.
[“Understanding Bioinformatics”, M. Zvelebil, J. O. Baum]5.

11. Scoring Alignments and Substitution
Matrices
 Percentage identity is a relatively crude
measure and does bot give a complete
picture of the degree of similarity of two
sequences.
 Scoring identical matches 1 and mismatches
as 0 ignores the fact that the type of amino
acids involved is highly significant.

12. Scoring Alignments and Substitution
Matrices
 Genuine matches may not be identical:
Seq1: T H I S I S A S E Q U E N C E
Seq1: T H A T _ _ _ S E Q U E N C E
Isoleucine – Alanine: both hydrophobic
Serine – Threonine : both polar

13. Scoring Alignments and Substitution
Matrices
 Scoring pairs of amino acids:
– with similar properties  higher scores
– With different properties  lower scores

14. Scoring Alignments and Substitution
Matrices
 To assign scores for alignmens use
SUBSTITUTION MATRICES
[“Understanding Bioinformatics”, M. Zvelebil, J. O. Baum]
5.

15. Scoring Alignments and Substitution
Matrices
 Different types of substitution matrices are
being used based on:
– The number of mutations required for
convertion of one amino acid to the other
– Similarities in physicochemical properties.

16. Scoring Alignments and Substitution
Matrices
 PAM substitution matrices:
– Use closely related protein sequences to
derive substitution frequencies
– Accepted Point Mutations per 100 residues
 250 PAM  250 mutation on 100 residues

17. Scoring Alignments and Substitution
Matrices
 BLOSUM substitution matrices:
– BLOcks of Amino Acid SUbstitution Matrix
– Use mutation data from highly conserved
local regions
– BLOSUM 62  62% identity

18. Scoring Alignments and Substitution
Matrices
 Which matrix to use ?
– Depends on the problem properties,
– Distantly related sequences : PAM 250 –
BLOSUM 50
– Closely related sequences: PAM 120,
BLOSUM 80

19. Scoring Alignments and Substitution
Matrices
 Which matrix to use ?
– Some special purpose matrices (SLIM and
PHAT are designed for membrane proteins)
– The length of the sequende is important
 Short sequences  PAM 40 or BLOSUM 80
 Long sequences  PAM 250 or BLOSUM 50

20. Scoring Alignments and Substitution
Matrices
 BLOSUM – 62 and PAM 120
[“Understanding Bioinformatics”, M. Zvelebil, J. O. Baum] 6.

21. Inserting Gaps
 Gap insertion requires a scoring penalty (gap
penalty).
 To achieve correct matches gaps are
required
 Alignment programs use gap penalties to
limit the introduction of gaps in the
alignments

22. Inserting Gaps
 Insertions tend to be several residues long
rather than just a single residue long
– Fewer insertions and deletions occur in sequences
of structural importance
– Smaller penalty on lengthening an existing gap
(gap extension penalty) than introducing a new
gap
– Gap penaly is high  the number of gaps will be
decreased
– Gap penalty is low  more and large gaps will be
inserted.

23. Inserting Gaps
 Choosing gap penalties:
– Linear
– Affine
 Gap open penalty
 Gap extension penlty

24. Dynamic Programming
 Global and Local alignments
 Pairwise and Multiple alignments
[“Understanding Bioinformatics”, M. Zvelebil, J. O. Baum] 7.

25.  For a pair of sequences there is a large
number of possible alignments.
 2 sequences of length 1000 have
appriximately 10600
different alignments.
Dynamic Programming

26.  Dynamic Programming:
– Problem can be divided into many smaller parts.
– Optimal alignment will not contain parts that are
not themselves optimal.
– Start from sufficiently short sub-sequences.
– Alignement is additive:
Dynamic Programming

27.  Needleman and Wunsch were the first to
propose this method.
 Find optimal global alignments.
 Align sequences:
– Seq1: x (x1x2x3…xm)
– Seq1: y (y1y2y3…yn)
Dynamic Programming

28.  s(a,b) = score of aligning a and b
 F(i,j) = optimal similarity of X(1:i) and Y(1:j)
 Recurrence relation:
– F(i,0) = Σ s(X(k), gap), 0 <= k <= i
– F(0,j) =Σ s(gap, B(k)), 0 <= k <= j
– F(i,j) = max [ F(i,j-1) + s(gap,Y(j),
F(i-1,j) + s(X(i),gap),
F(i-1, j-1) + s(X(i), Y(j)]
– Assume linear gap penalty
Dynamic Programming

29. Dynamic Programming
 Matrix S of optimal scores of sub-sequence
alignments.
[“Understanding Bioinformatics”, M. Zvelebil, J. O. Baum]
9.

30. Dynamic Programming
S(I, T) = -1,
10.

31. Dynamic Programming
S(I, H) = -3,
S(I, gap) = -8,
S(gap, H) = -8
Recurrence relation:
F(i,j) = max [ F(i,j-1) + s(gap,Y(j),
F(i-1,j) + s(X(i),gap),
F(i-1, j-1) + s(X(i), Y(j)]
[“Understanding Bioinformatics”, M. Zvelebil, J. O. Baum]
11.

32. Dynamic Programming
[“Understanding Bioinformatics”, M. Zvelebil, J. O. Baum]
12.

33. Dynamic Programming
–Linear gap penalty (E=4)
[“Understanding Bioinformatics”, M. Zvelebil, J. O. Baum]
13.

34. Dynamic Programming
 Semi – global alignment:
– When we treat terminal gaps differently than
internal gaps
– How to modify dynamic programming to be able
to make semi – global alignment ?

35. Dynamic Programming
 Local alignment:
– If we compare a sequence to whole genome
– Find sub-strings whose optimal global
alignment value is maximum

36. Dynamic Programming
 What is the difference between global and
local alignment ?
 Can we define the recuernce relation of local
alignment similar to global alignment ?

37. Recurrence relation of GLOBAL ALIGNMENT:
(Needleman & Wunsch)
– F(i,0) = Σ s(X(k), gap), 0 <= k <= i
– F(0,j) =Σ s(gap, B(k)), 0 <= k <= j
– F(i,j) = max [ F(i,j-1) + s(gap,Y(j),
F(i-1,j) + s(X(i),gap),
F(i-1, j-1) + s(X(i), Y(j)]
Dynamic Programming

38. Recurrence relation of LOCAL ALIGNMENT:
(Smith-Waterman)
– F(i,0) = 0
– F(0,j) = 0
– F(i,j) = max [ 0,
F(i,j-1) + s(gap,Y(j),
F(i-1,j) + s(X(i),gap),
F(i-1, j-1) + s(X(i), Y(j)]
Dynamic Programming

39. Database Searches
 FASTA and BLAST
 Use some heuristics
 Dynamic Programming Complexity
– Time O(n*m)
– Space O(n*m)

40. Database Searches FASTA
 Good local alignment should have some
exact match subsequence.
 Find all k-tuples. (k=1-2 for proteins, 3-6 for
DNA sequences)
 Protein k – tuples  nc, sp, … (k = 2)
 Nucleotide k – tuples  TAAA, CTCC,…(k = 4)

41. Database Searches FASTA
 If k = 3 for nucleotide sequences.
– There will be 64 possible k – tuples
– Assign a number e( ):
 e(A) = 0, e(C) = 1, e(G) = 2, e(T) = 3
 Each 3 – tuples are represented as  xi xi+1xi+2
 Assign a number to each 3 – tuple
– Ci = e(xi)42
+ e(xi+1)41
+ e(xi+2)40
– For example: AAA 
 AAA 042
+ 041
+ 040
= 0
 CAA 142
+ 041
+ 040
= 16

42. Database Searches FASTA
 Find each occurance of k – tuples in the
sequences.
 Chaining  Look – Up Tables
 Consider TAAAACTCTAAC (if k = 3):
3 - tuples Position
AAA (0) 2, 3
AAC (1) 4, 10
AAG (2) 0
AAT (3) 0
… …

43. Database Searches BLAST
 Use short words to search the database
sequence.
 Searches for k – mers that will score above a
threshold (T) value when aligned with query k -
mer (Remember FASTA looks for k – tuples
which are identical).
 Use a scheme based on finite state automata
(Remember FASTA use hashing and chaining
fot rapid identification of k - tuples)

44. Database Searches BLAST
 From Query Sequence, create query words
(for protein sequences word size is 3)

45. Database Searches BLAST
 Blast uses a list of high scoring words created
from words similar to query words. Considers
the words with a score bigger than a threshold
value.

46. Database Searches BLAST
 Scan each database sequence for an exact
match to the list of words.
 Word hits are then extended in either direction
in an attempt to generate an alignment with a
score exceeding the threshold of "S".

47. Database Searches BLAST
 Keep only the extended matches that have a
score at least S.
 Determine statistical significance of each
remaining match.

48. Database Searches BLAST
http://blast.ncbi.nlm.nih.gov/Blast.cgi
1.
14.

49. Database Searches BLAST
15.

50. Database Searches BLAST
16.

51. Database Searches BLAST
17.

52. Database Searches BLAST
18.

53. Database Searches HISTORY
 1970: NW
 1980: SW
 1985: FASTA
 1989: BLAST

54. Books and Web References
 Books Name :
1. Introduction To Bioinformatics by T. K. Attwood
2. BioInformatics by Sangita
3. Basic Bioinformatics by S.Ignacimuthu, s.j.
 http://en.wikipedia.org/wiki/Sequence_alignment
 http://pages.cs.wisc.edu/~bsettles/ibs08/lectures/02-alignment.pdf
 http://www.ks.uiuc.edu/Training/Tutorials/science/bioinformatics-
tutorial/bioinformatics.pdf
 M. Zvelebil, J. O. Baum, “Understanding Bioinformatics”, 2008,
Garland Science
 Andreas D. Baxevanis, B.F. Francis Ouellette, “Bioinformatics: A
practical guide to the analysis of genes and proteins”, 2001,
Wiley.54

55. Images References
 1.http://gorbi.irb.hr/files/5712/7497/9729/Slide09.jpg
 2.http://www.ensembl.org/info/genome/compara/tree_exa
mple1.png
 3.http://www.nature.com/nature/journal/v496/n7445/imag
es/nature12027-f1.2.jpg
 4.
http://upload.wikimedia.org/wikipedia/commons/e/e6/Spo
mbe_Pop2p_protein_structure_rainbow.png
 5. & 6. Book: Basic Bioinformatics by S.Ignacimuthu, s.j.
 7. to 13. Book: Basic Bioinformatics by S.Ignacimuthu, s.j.
 14. to 18. http://blast.ncbi.nlm.nih.gov/Blast.cgi