GGeeoorrggiiaa SSttaattee UUnniivveerrssiittyy SScchhoollaarrWWoorrkkss @@ GGeeoorrggiiaa SSttaattee UUnniivveerrssiittyy Computer Science Dissertations Department of Computer Science Fall 12-14-2011 MMuullttiippllee BBiioollggiiccaall SSeeqquueennccee AAlliiggnnmmeenntt:: SSccoorriinngg FFuunnccttiioonnss,, AAllggoorriitthhmmss,, aanndd EEvvaalluuaattiioonnss Ken D. Nguyen Georgia State University Follow this and additional works at: https://scholarworks.gsu.edu/cs_diss Part of the Computer Sciences Commons RReeccoommmmeennddeedd CCiittaattiioonn Nguyen, Ken D., "Multiple Biolgical Sequence Alignment: Scoring Functions, Algorithms, and Evaluations." Dissertation, Georgia State University, 2011. doi: https://doi.org/10.57709/2160321 This Dissertation is brought to you for free and open access by the Department of Computer Science at ScholarWorks @ Georgia State University. It has been accepted for inclusion in Computer Science Dissertations by an authorized administrator of ScholarWorks @ Georgia State University. For more information, please contact [email protected]. MULTIPLE BIOLOGICAL SEQUENCE ALIGNMENT: SCORING FUNCTIONS, ALGORITHMS, AND EVALUATIONS by KEN D. NGUYEN Under the Direction of Dr. Yi Pan ABSTRACT Aligning multiple biological sequences such as protein sequences or DNA/RNA sequences is a fundamen- tal task in bioinformatics and sequence analysis. These alignments may contain invaluable information that scientists need to predict the sequences’ structures, determine the evolutionary relationships between them, or discover drug-like compounds that can bind to the sequences. Unfortunately, multiple sequence alignment (MSA) is NP-Complete. In addition, the lack of a reliable scoring method makes it very hard to align the sequences reliably and to evaluate the alignment outcomes. In this dissertation, we have designed a new scoring method for use in multiple sequence alignment. Our scoring method encapsulates stereo-chemical properties of sequence residues and their substitution probabilities into a tree-structure scoring scheme. This new technique provides a reliable scoring scheme with low computational complexity. Inaddition to the newscoring scheme, wehavedesigned anoverlappingsequenceclustering algorithm to use in our new three multiple sequence alignment algorithms. One of our alignment algorithms uses a dynamic weighted guidance tree to perform multiple sequence alignment in progressive fashion. The use of dynamicweightedtreeallowserrorsintheearlyalignmentstagestobecorrectedinthesubsequencestages. Other two algorithms utilize sequence knowledge-bases and sequence consistency to produce biological meaningful sequence alignments. To improve the speed of the multiple sequence alignment, we have developed a parallel algorithm that can be deployed on reconfigurable computer models. Analytically, our parallel algorithm is the fastest progressive multiple sequence alignment algorithm. INDEX WORDS: Abstract, Dissertation, Graduate degree, Georgia State University, Multiple sequence alignments, Parallel multiple sequence alignment Algorithms, Reliability in multiple sequence alignments, Algorithms, Scoring functions BIOLOGICAL MULTIPLE SEQUENCE ALIGNMENT: SCORING FUNCTIONS, ALGORITHMS, AND EVALUATIONS by KEN D. NGUYEN A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in the College of Arts and Sciences Georgia State University 2011 Copyright by Ken Dinh Nguyen 2011 BIOLOGICAL MULTIPLE SEQUENCE ALIGNMENT:SCORING FUNCTIONS, ALGORITHMS, AND EVALUATIONS by KEN D. NGUYEN Committee Chair: Yi Pan Committee: Guantao Chen Alex Zelikovsky Anu G. Bourgeois Electronic Version Approved: Office of Graduate Studies College of Arts and Sciences Georgia State University August 2011 iv ACKNOWLEDGEMENTS I would like to thank my advisor, Dr. Yi Pan, for his long and continuous encouragement and guidance. His everlasting patience and supports give me the opportunity to complete this very long process. He has always been there helping and guiding me in taking initiative steps to success. All the grants, fellowships, and awards given to me are the results of his efforts. I would like to thank my committee members: Dr. Alex Zelikovsky, Dr. Anu G. Bourgeois and Dr. Guan- tao Chen for their patience, criticism, and help. Special thanks to Dr. Raj Sunderraman and Dr. Kimberly N. King whose have been my most favorite professors, especially their passionate teaching styles - One is very detail-oriented and the other is very energetic. Special thanks to Dr. Martin Fraser, our Computer Science Department first Chair, whose had infused me with the idea that teaching could be a career for me and then offered me a job. I would like to thank the Molecular Basis of Disease (MBD) program at Georgia State University for their scholarly and financial supports. I also would like to thank the National Science Foundation for indirect financial supports. Finally,Iwouldliketothankourcomputersciencedepartment,includingallthefaculty,staffs,andstudents who I have been working with. It has been a long and pleasure journey for me. It’s been the best place to be in, either for studying or working. I’m sure I’ll be missing you all. v TABLE OF CONTENTS ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . iv LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . x LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . xviii LIST OF ABBREVIATIONS . . . . . . . . . . . . . . . . . . . . . xix Chapter 1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 The Organization of This Study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Sequence Fundamentals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3.1 Protein . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3.2 DNA/RNA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3.3 Sequence Formats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3.4 Motifs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.3.5 Sequence Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Chapter 2 PROTEIN/DNA/RNA PAIRWISE SEQUENCE ALIGNMENT . . 10 2.1 Sequence Alignment Fundamentals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2 Dot-Plot Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3 Dynamic Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3.1 Needle-Wunch’s Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3.2 Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.3.3 Smith-Waterman’s Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.3.4 Affine gap penalty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.4 Word Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.5 Searching Sequence Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.5.1 FASTA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.5.2 BLAST . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 vi Chapter 3 QUANTIFYING SEQUENCE ALIGNMENTS . . . . . . . . 24 3.1 Evolution and Measuring Evolution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.1.1 Jukes and Cantor’s Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.1.2 Measuring Relatedness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.2 Substitution Matrices and Scoring Matrices . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.2.1 Identity Scores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.2.2 Substitution/Mutation Scores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.3 Gaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.3.1 Sequence distances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.3.2 Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.4 Scoring Multiple Sequence Alignments . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.4.1 Sum-of-Pair Score . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.5 Circular Sum Score . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.6 Conservation Score Schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.6.1 Wu and Kabat’s Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.6.2 Jores’s Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.6.3 Lockless and Ranganathan’s Method . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.7 Diversity Scoring Schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.7.1 background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.7.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.8 Stereochemical Property Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.8.1 Valdar’s Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.9 A New Contribution: Hierarchical Expected matching Probability scoring metric (HEP) 44 3.9.1 Building an AACCH scoring Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.9.2 The scoring metric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.9.3 Proof of scoring metric correctness . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.9.4 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.9.5 Scoring metric and sequence weighting factor . . . . . . . . . . . . . . . . . . . . 49 3.9.6 Evaluation Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.9.7 Evaluation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 vii Chapter 4 SEQUENCE CLUSTERING . . . . . . . . . . . . . . . . 60 4.1 Unweighted Pair Group Method with Arithmetic mean - UPGMA . . . . . . . . . . . . 61 4.2 Neighborhood Joining Method - NJ. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.3 A new Contribution: Overlapping Sequence Clustering . . . . . . . . . . . . . . . . . . . 66 Chapter 5 MULTIPLE SEQUENCES ALIGNMENT ALGORITHMS . . . . 70 5.1 Dynamic Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 5.1.1 DCA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 5.2 Progressive Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 5.2.1 Clustal Family . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 5.2.2 PIMA: Pattern-induced multi-sequence alignment . . . . . . . . . . . . . . . . . 75 5.2.3 PRIME: Profile-based Randomized Iteration Method . . . . . . . . . . . . . . . . 77 5.2.4 DIALIGN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 5.3 Consistency and Probabilistic MSA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 5.3.1 POA: Partial Order graph Alignment . . . . . . . . . . . . . . . . . . . . . . . . 79 5.3.2 PSAlign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.3.3 ProbCons: Probabilistic consistency-based multiple sequence alignment . . . . . 80 5.3.4 T-COFFEE: Tree-based Consistency Objective Function For alignment Evaluation 82 5.3.5 MAFFT: MSA based on Fast Fourier Transform . . . . . . . . . . . . . . . . . . 83 5.3.6 AVID . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 5.3.7 Eulerian path MSA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 5.4 Genetic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 5.4.1 SAGA: sequence alignment by genetic algorithm . . . . . . . . . . . . . . . . . . 86 5.4.2 GA and Self-organizing Neural Networks. . . . . . . . . . . . . . . . . . . . . . . 88 5.4.3 FAlign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 5.5 New Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 5.5.1 KB-MSA: Knowledge-based Multiple Sequence Alignment . . . . . . . . . . . . . 89 5.5.2 PADT: Progressive multiple sequence Alignment based on Dynamic weighted Tree 99 5.6 Test Data and Alignment Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 5.7 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 5.7.1 Measuring Alignment Quality . . . . . . . . . . . . . . . . . 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