1. Lecture 1
BNFO 601
Usman Roshan

2. Course overview Perl progamming language (and some Unix basics)
Intro Perl exercises
Dynamic programming and Viterbi algorithm in Perl
Sequence analysis
Algorithms for exact and heuristic pairwise alignment
Hidden Markov models
BLAST
Short read alignments
Whole genome sequence alignment
High performance methods for very large datasets

3. Overview (contd)
Grade: Two 25% exams, 30% final exam, and six programming assignments (20%)
Exams will cover Perl and bioinformatics algorithms
Recommended Texts:
Introduction to Bioinformatics Algorithms by Pavel Pevzner and Neil Jones
Biological sequence analysis by Durbin et. al.
Introduction to Bioinformatics by Arthur Lesk
Beginning Perl for Bioinformatics by James Tisdall
Introduction to Machine Learning by Ethem Alpaydin

4. Nothing in biology makes sense, except in the light of evolution
-3 mil yrs
-2 mil yrs
-1 mil yrs
today
TAGGCCTT
(Human)
TAGCCCTTA
(Monkey)
A_C_CTT
(Cat)
A_CACTTC
(Lion)
_G_GCTT
(Mouse)
_GGGCTT
TAGACCTT
A_CACTT
AAGGCTT
T_GACTT
AAGACTT
AAGACTT
T_GACTT
AAGGCTT
_GGGCTT
TAGACCTT
A_CACTT
ACCTT
(Cat)
ACACTTC
(Lion)
TAGCCCTTA
(Monkey)
TAGGCCTT
(Human)
GGCTT
(Mouse)
_GGGCTT
TAGACCTT
A_CACTT
AAGGCTT
T_GACTT
AAGACTT
T_GACTT
AAGGCTT
AAGACTT
AAGACTT

5. Representing DNA in a format manipulatable by computers
DNA is a double-helix molecule made up of four nucleotides:
Adenosine (A)
Cytosine (C)
Thymine (T)
Guanine (G)
Since A (adenosine) always pairs with T (thymine) and C (cytosine) always pairs with G (guanine) knowing only one side of the ladder is enough
We represent DNA as a sequence of letters where each letter could be A,C,G, or T.
For example, for the helix shown here we would represent this as CAGT.

6. Transcription and translation

7. Amino acids Proteins are chains of amino acids. There are
twenty different amino
acids that chain in
different ways to form
different proteins.
For example,
FLLVALCCRFGH
(this is how we could store
it in a file)
This sequence of amino
acids folds to form a 3-D
structure

8. Protein folding

9. Protein folding The protein folding problem is to determine
the 3-D protein structure
from the sequence.
Experimental techniques
are very expensive.
Computational are cheap
but difficult to solve.
By comparing sequences
we can deduce the
evolutionary conserved
portions which are also
functional (most of the time).

10. Protein structure Primary structure: sequence of amino acids.
Secondary structure: parts of the
chain organizes itself into alpha helices, beta sheets, and coils. Helices and sheets are usually evolutionarily conserved and can aid sequence alignment.
Tertiary structure: 3-D structure of entire chain
Quaternary structure: Complex of several chains

11. Key points
DNA can be represented as strings consisting of four letters: A, C, G, and T. They could be very long, e.g. thousands and even millions of letters
Proteins are also represented as strings of 20 letters (each letter is an amino acid). Their 3-D structure determines the function to a large extent.

12. Comparison of sequences
Fundamental in bioinformatics
Many applications
Evolutionary conserved regions
Functional sites in proteins
Phylogeny reconstruction
Protein structural contact prediction
Gene splicing
Genome assembly
Database search

13. Pairwise sequence alignment
Comparison of two sequences
We want to determine the substitutions, insertions, and deletions that occurred between the two sequences

14. Pairwise alignment
Definition of a sequence alignment

15. Pairwise alignment

16. Pairwise alignment X: ACA, Y: GACAT Match=8, mismatch=2, gap-5
ACA-- -ACA- --ACA ACA----
GACAT GACAT GACAT G—ACAT
Score =

17. Traceback
We can compute an alignment of DNA (or protein or RNA) sequences X and Y with a traceback matrix T.
Sequence X is aligned along the rows and Y along the columns.
Each entry of the matrix T contains D, L, or U specifying diagonal, left or upper

18. Traceback
X: ACA, Y=TACAG T A C G L U D

19. Traceback
X: ACA, Y=TACAG T A C G L U D

20. Traceback pseudocode aligned_seq1 = "" aligned_seq2 = "" i = len(seq1)
j = len(seq2)
while(i !=0 or j != 0):
if(T[i][j] == “L”):
aligned_seq1 = “-” + aligned_seq1
aligned_seq2 = seq2[j-1] + aligned_seq2
j = j - 1
elif(T[i][j] == "U"):
aligned_seq2 = "-" + aligned_seq2
aligned_seq1 = seq1[i-1] + aligned_seq1
i = i - 1
else:

21. Optimal alignment
An alignment can be specified by the traceback matrix.
How do we determine the traceback for the highest scoring alignment?
Needleman-Wunsch algorithm for global alignment
First proposed in 1970
Widely used in genomics/bioinformatics
Dynamic programming algorithm

22. Needleman-Wunsch Input: Notation:
X = x1x2…xn, Y=y1y2…ym
(X is seq1 and Y is seq2)
Notation:
X1..i = x1x2…xi
V(X1..i,Y1..j) = Optimal alignment score of sequences X1..i and Y1..j.
Suppose we know the optimal alignment scores of
X1…i-1 and Y1…j-1
X1…i and Y1...j-1
X1...i-1 and Y1…j

23. Needleman-Wunsch
Then the optimal alignment score of X1…i and Y1…j is the maximum of
V(X1…i-1,Y1…j-1) + match/mismatch
V (X1…i,Y1…j-1) + gap
V (X1…i-1,Y1…j) + gap
We build on this observation to compute V(X1...n,Y1...m)

24. Needleman-Wunsch
Define V to be a two dimensional matrix with len(X)+1 rows and len(Y)+1 columns
Let V[i][j] be the score of the optimal alignment of X1…i and Y1…j.
Let m be the match cost, mm be mismatch, and g be the gap cost.

25. NW pseudocode Initialization:
for i = 1 to length of seq1 { V[i][0] = i*g; }
For i = 1 to length of seq2 { V[0][i] = i*g; }
Recurrence:
for i = 1 to length of seq1{
for j = 1 to length of seq2{
V[i-1][j-1] + m(or mm)
V[i][j] = max { V[i-1][j] + g
V[i][j-1] + g
if(maximum is V[i-1][j-1] + m(or mm)) then T[i][j] = ‘D’
else if (maximum is V[i-1][j] + g) then T[i][j] = ‘U’
else then T[i][j] = ‘L’ }

26. NW example V G A C A T A C T Input: seq1: ACA seq2: GACAT m = 5
mm = -4
gap = -20
seq1 is lined along the rows
and seq2 is along the columns
G A C A T
-20
-40
-60
-80
-100 -4
-15
-35
-55
-75
-24 -8
-10
-30
-50
-44
-19
-12 -5
-25 A C T L U D

27. NW parameters How do we pick gap penalty and match/mismatch costs?
Proteins/RNA both have 3-D structure.
We can produce high quality manual alignments by hand if the structure is available.
These alignments can then serve as a benchmark to train gap parameters so that the alignment program produces correct alignments.

28. Structural alignment - example 1
Alignment of thioredoxins from
human and fly taken from the
Wikipedia website. This protein
is found in nearly all organisms
and is essential for mammals.
PDB ids are 3TRX and 1XWC.

29. Structural alignment - example 2
Taken from
Unaligned proteins.
2bbm and 1top are
proteins from fly and
chicken respectively.
Computer generated
aligned proteins

30. Benchmark alignments Protein alignment benchmarks
BAliBASE, SABMARK, PREFAB, HOMSTRAD are frequently used in studies for protein alignment.
Proteins benchmarks are generally large and have been in the research community for sometime now.
BAliBASE 3.0

31. NW parameters
With a procedure called cross-validation we use the benchmark to train gap parameters so that the alignment program produces correct alignments.
How can we quantify the “correctness” of a test alignment with respect to a reference?
We measure the number of pairs in the test that are also aligned in the reference alignment.
This is also called the sum-of-pairs accuracy.

32. NW parameters
For match and mismatch costs we use a more direct approach: log likelihood scores
We calculate probabilities from the benchmark
The same cannot be done for gap penalties because in benchmarks because of uncertainties in gap placements

33. Biologically realistic scoring matrices
PAM and BLOSUM are most popular
PAM was developed by Margaret Dayhoff and co-workers in 1978 by examining 1572 mutations between 71 families of closely related proteins
BLOSUM is more recent and computed from blocks of sequences with sufficient similarity

34. Computing scoring matrices
Start with a set of reference alignments
Suppose we want to compute the score of A aligning to C
Count the number of times A aligns to C
Count the number of A’s and C’s
Compute pAC the probability of A aligning to C and pA and pC the background probabilities of A and C
Compute the log likelihood ratio

35. Next week Basics of Unix Perl programming Basics Exercises
Dynamic programming solution to sequence alignment in Perl