Multiple Biolgical Sequence Alignment: Scoring Functions, Algorithms, and Evaluations

Aligning multiple biological sequences such as protein sequences or DNA/RNA sequences is a fundamental task in bioinformatics and sequence analysis. These alignments may contain invaluable information that scientists need to predict the sequences\u27 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. In addition to the new scoring scheme, we have designed an overlapping sequence clustering 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 dynamic weighted tree allows errors in the early alignment stages to be corrected in the subsequence stages. 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

Using Sequence Similarity Networks for Visualization of Relationships Across Diverse Protein Superfamilies

The dramatic increase in heterogeneous types of biological data—in particular, the abundance of new protein sequences—requires fast and user-friendly methods for organizing this information in a way that enables functional inference. The most widely used strategy to link sequence or structure to function, homology-based function prediction, relies on the fundamental assumption that sequence or structural similarity implies functional similarity. New tools that extend this approach are still urgently needed to associate sequence data with biological information in ways that accommodate the real complexity of the problem, while being accessible to experimental as well as computational biologists. To address this, we have examined the application of sequence similarity networks for visualizing functional trends across protein superfamilies from the context of sequence similarity. Using three large groups of homologous proteins of varying types of structural and functional diversity—GPCRs and kinases from humans, and the crotonase superfamily of enzymes—we show that overlaying networks with orthogonal information is a powerful approach for observing functional themes and revealing outliers. In comparison to other primary methods, networks provide both a good representation of group-wise sequence similarity relationships and a strong visual and quantitative correlation with phylogenetic trees, while enabling analysis and visualization of much larger sets of sequences than trees or multiple sequence alignments can easily accommodate. We also define important limitations and caveats in the application of these networks. As a broadly accessible and effective tool for the exploration of protein superfamilies, sequence similarity networks show great potential for generating testable hypotheses about protein structure-function relationships

A Parallel Non-Alignment Based Approach to Efficient Sequence Comparison using Longest Common Subsequences

Biological sequence comparison programs have revolutionized the practice of biochemistry, and molecular and evolutionary biology. Pairwise comparison of genomic sequences is a popular method of choice for analyzing genetic sequence data. However the quality of results from most sequence comparison methods are significantly affected by small perturbations in the data and furthermore, there is a dearth of computational tools to compare sequences beyond a certain length. In this paper, we describe a parallel algorithm for comparing genetic sequences using an alignment free-method based on computing the Longest Common Subsequence (LCS) between genetic sequences. We validate the quality of our results by comparing the phylogenetic tress obtained from ClustalW and LCS. We also show through complexity analysis of the isoefficiency and by empirical measurement of the running time that our algorithm is very scalable

Improvements on Seeding Based Protein Sequence Similarity Search

The primary goal of bioinformatics is to increase an understanding in the biology of organisms. Computational, statistical, and mathematical theories and techniques have been developed on formal and practical problems that assist to achieve this primary goal. For the past three decades, the primary application of bioinformatics has been biological data analysis. The DNA or protein sequence similarity search is perhaps the most common, yet vitally important task for analyzing biological data. The sequence similarity search is a process of finding optimal sequence alignments. On the theoretical level, the problem of sequence similarity search is complex. On the applicational level, the sequences similarity search onto a biological database has been one of the most basic tasks today. Using traditional quadratic time complexity solutions becomes a challenge due to the size of the database. Seeding (or filtration) based approaches, which trade sensitivity for speed, are a popular choice among those available. Two main phases usually exist in a seeding based approach. The first phase is referred to as the hit generation, and the second phase is referred to as the hit extension. In this thesis, two improvements on the seeding based protein sequence similarity search are presented. First, for the hit generation, a new seeding idea, namely spaced k-mer neighbors, is presented. We present our effective algorithms to find a good set of spaced k-mer neighbors. Secondly, for the hit generation, a new method, namely HexFilter, is proposed to reduce the number of hit extensions while achieving better selectivity. We show our HexFilters with optimized configurations

Algoritmi za učinkovitu usporedbu sekvenci bez korištenja sravnjivanja

Sequence comparison is an essential tool in modern biology. It is used to identify homologous regions between sequences, and to detect evolutionary relationships between organisms. Sequence comparison is usually based on alignments. However, aligning whole genomes is computationally difficult. As an alternative approach, alignment-free sequence comparison can be used. In my thesis, I concentrate on two problems that can be solved without alignment: (i) estimation of substitution rates between nucleotide sequences, and (ii) detection of local sequence homology. In the first part of my thesis, I developed and implemented a new algorithm for the efficient alignment-free computation of the number of nucleotide substitutions per site, and applied it to the analysis of large data sets of complete genomes. In the second part of my thesis, I developed and implemented a new algorithm for detecting matching regions between nucleotide sequences. I applied this solution to the classification of circulating recombinant forms of HIV, and to the analysis of bacterial genomes subject to horizontal gene transfer.Table of Contents 1. GENERAL INTRODUCTION.........................................................................1 1.1. Suffix trees and other index data structures used in biological sequence analysis.....................................................................................................................9 1.1.1. Suffix Tree..........................................................................................11 1.1.2. The space and the time complexity of the algorithms for the suffix tree construction.......................................................................................................13 1.1.3. Suffix Array........................................................................................14 1.1.4. The space and the time complexity of the algorithms for suffix array construction.......................................................................................................15 1.1.5. Enhanced Suffix Array.......................................................................17 1.1.6. The 64-bit implementation of the lightweight suffix array construction algorithm 21 1.1.7. Self-index...........................................................................................22 1.1.8. Burrows-Wheeler transform..............................................................23 1.1.9. The FM-Index and the backward search algorithm..........................25 1.1.10. The space and the time-complexity of the FM-index.........................29 2. EFFICIENT ESTIMATION OF PAIRWISE DISTANCES BETWEEN GENOMES...............................................................................................................31 2.1. Introduction................................................................................................31 2.2. Methods.....................................................................................................33 2.2.1. Definition of an alignment-free estimator of the rate of substitution, Kr 33 2.2.2. Problem statement.............................................................................35 2.2.3. Time complexity analysis of the previous approach (kr 1)................35 2.2.4. Time complexity analysis of the new approach (kr 2).......................37 2.2.5. Algorithm 1: Computation of all Kr values during the traversal of a generalized suffix tree of n sequences................................................................38 2.2.6. The implementation of kr version 2...................................................44 2.3. Analysis of Kr on simulated data sets........................................................45 2.3.1. Auxiliary programs............................................................................45 2.3.2. Consistency of Kr...............................................................................46 i 2.3.3. The affect of horizontal gene transfer on the accuracy of Kr............48 2.3.4. The effect of genome duplication on the accuracy of Kr....................49 2.3.5. Run time comparison of kr 1 and kr 2...............................................50 2.4. Application of kr version 2........................................................................53 2.4.1. Auxililary software used for the analysis of real data sets................56 2.4.2. The analysis of 12 Drosophila genomes............................................57 2.4.3. The analysis of 13 Escherichia coli and Shigella genomes...............58 2.4.4. The analysis of 825 HIV-1 pure subtype genomes.............................61 2.5. Discussion..................................................................................................62 3. EFFICIENT ALIGNMENT-FREE DETECTION OF LOCAL SEQUENCE HOMOLOGY....................................................................................66 3.1. Introduction................................................................................................66 3.2. Methods.....................................................................................................69 3.2.1. Problem statement – determining subtype(s) of a query sequence....69 3.2.2. Construction of locally homologous segments..................................71 3.2.3. Time complexity of computing a list of intervals Ii............................72 3.2.4. Algorithm 2: Construction of an interval tree...................................73 3.2.5. Computing a list of segements Gi.......................................................80 3.3. Analysis of st on simulated data sets.........................................................82 3.3.1. Run-time and memory usage analysis of st........................................82 3.3.2. Consistency of st................................................................................85 3.3.3. Comparison to SCUEAL on simulated data sets...............................92 3.4. Application of st.........................................................................................97 3.4.1. The analysis of Neisseria meningitidis..............................................98 3.4.2. The analysis of a recombinant form of HIV-1...................................99 3.4.3. The analysis of circulating recombinant forms of HIV-1................103 3.4.4. The analysis of an avian pathogenic Escherichia coli strain..........104 3.5. Discussion................................................................................................107 4. CONCLUSION..............................................................................................110 5. REFERENCES..............................................................................................112 6. ELECTRONIC SOURCES...........................................................................121 7. LIST OF ABBREVIATIONS AND SYMBOLS.........................................122 ii iii ABSTRACT............................................................................................................124 SAŽETAK..............................................................................................................125 CURRICULUM VITAE........................................................................................126 ŽIVOTOPIS...........................................................................................................12

Estimates of the effect of natural selection on protein-coding content

Analysis of natural selection is key to understanding many core biological processes, including the emergence of competition, cooperation, and complexity, and has important applications in the targeted development of vaccines. Selection is hard to observe directly but can be inferred from molecular sequence variation. For protein-coding nucleotide sequences, the ratio of nonsynonymous to synonymous substitutions (ω) distinguishes neutrally evolving sequences (ω = 1) from those subjected to purifying (ω 1) selection. We show that current models used to estimate ω are substantially biased by naturally occurring sequence compositions. We present a novel model that weights substitutions by conditional nucleotide frequencies and which escapes these artifacts. Applying it to the genomes of pathogens causing malaria, leprosy, tuberculosis, and Lyme disease gave significant discrepancies in estimates with ∼10-30% of genes affected. Our work has substantial implications for how vaccine targets are chosen and for studying the molecular basis of adaptive evolution