Description
Reading: Jones and Pevzner Ch 8, 10;
Gusfield Ch 16, 17; JXZ Ch 5, 11
Q1: We will discuss a space-saving technique for pairwise sequence alignment
based on divide-and-conquer this week. The technique can be easily
extended to multiple sequence alignment. Your task is to design an
O(m*n) space, O(m*n*k) time SP alignment algorithm for three DNA
sequences of lengths m, n and k, respectively. Implement your algorithm
in C/C++/Java/Python, and test it on real data.
You may find discussions on the space-saving technique for pairwise
sequence comparison in the books by Jones and Pevzner, by Gusfield,
and by Jiang, Xu, and Zhang (Current Topics in Computational Biology,
MIT Press Series on Computational Molecular Biology, 2002). The
following original papers provide more detailed information:
D.S. Hirschberg. Algorithms for the longest common subsequence
problem. J.ACM, 24:664-75, 1977.
E. W. Myers and W. Miller. Optimal alignments in linear space.
Comp. Appl. Biosciences, 4:11-17, 1988.
At UCR Science Lib: call number is QH324.2 .C66
Chao, Hardison, and Miller. Recent developments in linear-space
alignment methods: a survey. Journal of Computational Biology.
Vol. 1-4, pp. 271-291. 1994.
(i) For simplicity, let’s consider global alignment and the basic
SP score model where gaps are not specially treated.
(ii) Your program should work for any score function on nucleotides.
In other words, the user should be able to input a score function
in the form of a 5×5 matrix indexed by A, C, G, T, and space.
The SP-score of a column of letters/spaces is the sum of the scores
of each pair of letters/spaces in the column.
To test your program, use the Blast scores: Match = 5, Mismatch = -4,
and Indel = -8. The score between two spaces is 0.
(iii) Test your program on the following five sets of sequences:
NM_013096. Rattus norvegicus hemoglobin alpha, adult chain 2 (Hba-a2),
mRNA. 557 bps.
NM_008218. Mus musculus hemoglobin alpha, adult chain 1 (Hba-a1),
mRNA. 569 bps.
NM_000558. Homo sapiens hemoglobin, alpha 1 (HBA1), mRNA. 577 bps.
NM_001030004. Homo sapiens hepatocyte nuclear factor 4, alpha
(HNF4A), transcript variant 6, mRNA. 1558 bps.
NM_178850. Homo sapiens hepatocyte nuclear factor 4, alpha
(HNF4A), transcript variant 3, mRNA. 1652 bps.
NM_001287184. Homo sapiens hepatocyte nuclear factor 4 alpha (HNF4A),
transcript variant 10, mRNA. 1780 bps.
NM_010019. Mus musculus death-associated protein kinase 2 (Dapk2),
mRNA, 1792 bps.
NM_001243563. Sus scrofa death-associated protein kinase 2 (DAPK2),
mRNA, 1825 bps.
NM_014326. Homo sapiens death-associated protein kinase 2 (DAPK2),
mRNA, 2791 bps.
NM_008261. Mus musculus hepatic nuclear factor 4 (Hnf4). 4391 bps
NM_000457. Homo sapiens hepatocyte nuclear factor 4, alpha (HNF4A),
transcript variant 2, mRNA. 4739 bps
XM_016937951. PREDICTED: Pan troglodytes hepatocyte nuclear factor 4
alpha (HNF4A), transcript variant X1, mRNA, 4724 bps
NM_000492. Homo sapiens cystic fibrosis transmembrane conductance
regulator (ATP-binding cassette sub-family C, member 7)
(CFTR), mRNA. 6070 bps
NM_021050. Mus musculus cystic fibrosis transmembrane conductance
regulator homolog (Cftr), mRNA. 6305 bps.
NM_031506. Rattus norvegicus cystic fibrosis transmembrane
conductance regulator homolog (Cftr), mRNA. 6287 bps.
You may retrieve the sequences at http://www.ncbi.nih.gov/
using the accession numbers NM_013096, etc. Select “nucleotide” in
the search option box. The sequence data is given at the bottom of
the search result page. Note that the last dataset may take your
algorithms quite some time to run, especially on an old computer.
For this question, submit a report (hard copy) along with HW3 with
————–
(a) a high-level description of your algorithm (i.e. high-level
pseudo-code), and the data structures used,
(b) your source code, and
(c) the result of your program on each dataset, including the
score of the optimal alignment obtained, the length of the alignment,
the number of columns with perfectly matched nucleotides in the
alignment, and the running time and memory (on a PC or laptop
with specification of CPU and memory). Please do not include
the actual alignment in the report (because it is going to be quite
loooong :-).
(d) Do you see any obvious conserved regions captured in your alignments?
Although this question is optional for HW2, it could be a very good idea
that you complete the dynamic programming routine for computing the
optimal score of a 3-sequence alignment in O(m*n) space, and test it on
some small data to make sure that it is correct. The recurrence relation is
given in Chapter 6.10 of the textbook. Note that this algorithm will need
the dynamic programming algorithm for pairwise sequence alignment to
initialize its matrix.
Also, although your goal is to implement the O(mn)-space algorithm, it
will be useful for you to implement the standard O(mnk)-space dynamic
programming algorithm for 3-sequence alignment and use it as a subroutine
in the divide-and-conquer process when one of the sequences degenerates
to one or zero letters.
Q2: Problem 6.57, p. 224, of Jones and Pevzner.
What is the running time of your improved algorithm?
Note that besides Figure 6.28, a useful recurrence
relation is also given on page 33 of “Updated slides on
similarity based gene prediction”. You should show how
to incorporate the function P(i,j) into your improved
recurrence relation (i.e., your new recurrence relation
should define both functions S(i,j,B) and P(i,j)).
Q3: Consider the following additive distance matrix:
| A B C D E
——————
A | 0 5 7 6 9
B | 5 0 8 5 8
C | 7 8 0 9 12
D | 6 5 9 0 7
E | 9 8 12 7 0
Use the algorithm given on Slide 54 based on Buneman’s Theorem
to iteratively reconstruct the underlining evolutionary tree.
Can you also determine the branch lengths?
