Mathematical Models, Algorithms, and Statistics of Sequence Alignment By Tatiana Aleksandrovna ...

107 Pages·2010·5.19 MB·English

Mathematical Models, Algorithms, and Statistics of Sequence
Alignment
By
Tatiana Aleksandrovna Orlova
Specialist
Saratov State University 2002
Submitted in Partial Fulfillment of the Requirements
for the Degree of Master of Science in
Mathematics
College of Arts and Sciences
University of South Carolina
2010
Accepted by:
Eva Czabarka, Director of Thesis
Joshua Cooper, Second Reader
James Buggy, Interim Dean of the Graduate School
Acknowledgments
I would like to thank Dr. Eva Czabarka and Dr. Joshua Cooper for their helpful
advice and patience.
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Abstract
The problem of biological sequence comparison arises naturally in an attempt to
explain many biological phenomena. Due to the combinatorial structure and pat-
tern preserving properties of the sequences it has attracted not only biologists, but
also mathematicians, statisticians and computer scientists. In this work we study
one of the most effective tools widely used for comparison of biological sequences -
sequence alignment. We present the basic theory of sequence alignment from com-
putational, biological, and statistical perspectives. We will also present and analyze
results of computer simulations that effectively illustrate one possible application of
this theory.
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Contents
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
Chapter 1. Introduction: Where Mathematics Meets Biology . . 1
1.1. The Organized Complexity of Life . . . . . . . . . . . . . . . . . . . 1
1.2. Biological Sequences and Their Patterns . . . . . . . . . . . . . . . . 4
1.3. Biological Sequence Comparison . . . . . . . . . . . . . . . . . . . . 9
Chapter 2. Sequence Alignment . . . . . . . . . . . . . . . . . . . . . . 11
2.1. Pairwise Sequence Alignment . . . . . . . . . . . . . . . . . . . . . . 11
2.2. Scoring Schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3. Alignment Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Chapter 3. Statistics of Local Sequence Alignment . . . . . . . . 50
3.1. Hypothesis Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.2. Random Variables and Random Processes . . . . . . . . . . . . . . . 53
3.3. Ungapped Local Alignment Scores Statistics . . . . . . . . . . . . . . 62
3.4. Gapped Local Alignment Scores Statistics . . . . . . . . . . . . . . . 78
Chapter 4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
Appendix A. C++ Code . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
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List of Tables
Table 1.1 Amino acids and their abbreviations. . . . . . . . . . . . . . . 7
Table 3.1 Robinson & Robinson amino acid counts. . . . . . . . . . . . . 80
Table 3.2 Simulations results. . . . . . . . . . . . . . . . . . . . . . . . . 80
Table 3.3 Known estimates for λ and µ. . . . . . . . . . . . . . . . . . . 81
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List of Figures
Figure 1.1 Central dogma of molecular biology . . . . . . . . . . . . . . . 4
Figure 1.2 DNA chemical structure . . . . . . . . . . . . . . . . . . . . . 5
Figure 1.3 DNA complementarity . . . . . . . . . . . . . . . . . . . . . . 5
Figure 1.4 Lambda repressor protein bound to a lambda operator DNA
sequence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Figure 2.1 BLOSUM62 substitution matrix . . . . . . . . . . . . . . . . . 22
Figure 2.2 Alignment graph for sequences ATACTGG and GTCCGTG. . 28
Figure 2.3 Another alignment graph for ATACTGG and GTCCGTG. . . 29
Figure 2.4 Extended alignment graph. . . . . . . . . . . . . . . . . . . . 33
Figure 2.5 Extended alignment network . . . . . . . . . . . . . . . . . . . 45
Figure 2.6 The optimal global path. . . . . . . . . . . . . . . . . . . . . . 46
Figure 2.7 Global alignment score matrix (S(i,j)) . . . . . . . . . . . . . 47
Figure 2.8 The optimal local path. . . . . . . . . . . . . . . . . . . . . . 48
ˆ
Figure 2.9 Local alignment score matrix (S(i,j)) . . . . . . . . . . . . . 49
Figure 3.1 Ungapped alignment and its accumulative score. . . . . . . . . 63
Figure 3.2 Random walk corresponding to the ungapped local alignment. 66
Figure 3.3 Counts of large alignment scores, L = 400 . . . . . . . . . . . 82
Figure 3.4 Probability density plot of simulated and estimated data for
sequences of length L=40. . . . . . . . . . . . . . . . . . . . . 83
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Figure 3.5 Cumulativeprobabilitydistributionplotofsimulatedandestimated
data for sequences of length L = 40. . . . . . . . . . . . . . . 83
Figure 3.6 Probability density plot of simulated and estimated data for
sequences of length L = 100. . . . . . . . . . . . . . . . . . . . 84
Figure 3.7 Cumulativeprobabilitydistributionplotofsimulatedandestimated
data for sequences of length L = 100. . . . . . . . . . . . . . . 84
Figure 3.8 Probability density plot of simulated and estimated data for
sequences of length L = 200. . . . . . . . . . . . . . . . . . . . 85
Figure 3.9 Cumulativeprobabilitydistributionplotofsimulatedandestimated
data for sequences of length L = 200. . . . . . . . . . . . . . . 85
Figure 3.10 Probability density plot of simulated and estimated data for
sequences of length L = 400. . . . . . . . . . . . . . . . . . . . 86
Figure 3.11 Cumulativeprobabilitydistributionplotofsimulatedandestimated
data for sequences of length L = 400. . . . . . . . . . . . . . . 86
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Chapter 1
Introduction: Where Mathematics Meets
Biology
“Biology is mathematics’ next physics, only better”. Joel Cohen [12]
1.1. The Organized Complexity of Life
In 1953 Francis Crick, Rosalind Franklin, James Watson, and Maurice Wilkins
determined the structure of DNA. This important event became a start of theoret-
ical study of biological sequences. In his book “Life Itself. Its Origin and Nature”
[14] Crick gave a very intuitive explanation of the general nature of life from an
information-theoretical perspective, which can serve as a perfect start in establishing
a connection between biology and mathematics.
He starts with the idea that, the high degree of organized complexity of life
implies the need to store and process large amount of information. Thus, Nature has
to provide an effective mechanism for this important task. Such a mechanism must
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perform two functions: information storage and replication. Information storage
must be stable, which means that the information must be stored over a sufficiently
long period of time, and replication must be accurate, which means that the copying
can make only a small number of errors called mutations. Lack of accuracy can lead
to accumulated errors and decay, but perfect accuracy is not required. Moreover, we
musthaveasmallnumberofmutationsandrequirethattheymustbecopiedfaithfully
through generations. Most mutations lead to maladaptive changes but some might
lead to improvement. There is no mechanism to produce only favorable mutations,
which leaves chance to be the only source of true novelty. It is always possible for
any rare chance event to become very common once this change is favored by the
environment. This gives the living system its primary mechanism to improve itself.
Note that neither replication nor mutation alone can explain both similarity and
diversity of life, as we can see it everywhere around. Thus, the presence of both
mechanisms is required [14].
Effectively dealing with large amounts of information in this manner puts some
requirements on the mechanism. Recall that it needs to provide the system with easy
storage, easy replication, easy error correction, and stability of information. The key
idea is that an efficient way to do this requires the use of the combinatorial principle.
That is, we express the information by using only a small number of types of standard
units, but we combine them in many different ways. Similar to how we use words
to form sentences in human language, life uses linear strings of standard units. It is
required that in any language suitable for communication the number of words must
be relatively small compared to the number of sentences they can produce. Nature
follows the same principle requiring a small number of basic units, but the number
of structures these units can form must be very large. Moreover, in order to carry
information these structures must not be completely regular. The natural question
is what Nature chose for these basic units and why.
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Carbon is one the most abundant elements in the universe. It has a perfect
balance between valence electrons available and valence electrons needed to fill the
outer shell. This allows carbon to excel in bonding with itself and other atoms,
and to form long chains, so it can build arbitrarily long and complex structures.
Moreover, energy required to make and break carbon bonds is at the appropriate
level for building molecules which are stable and reactive [46]. Note, that there are
other elements that possess some of these properties but only carbon has all of them
[47]. Taking all the above into account Nature’s choice of carbon compounds as
units is obvious. Crick concludes that information transmission with the required
properties is naturally achieved through chemical reactions between combinatorial
structures made of carbon compounds [14].
We will only consider one-dimensional linear structures, known as biopolymers,
or biological sequences.
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