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

New Trends in Artificial Intelligence: Applications of Particle Swarm Optimization in Biomedical Problems

Optimization is a process to discover the most effective element or solution from a set of all possible resources or solutions. Currently, there are various biological problems such as extending from biomolecule structure prediction to drug discovery that can be elevated by opting standard protocol for optimization. Particle swarm optimization (PSO) process, purposed by Dr. Eberhart and Dr. Kennedy in 1995, is solely based on population stochastic optimization technique. This method was designed by the researchers after inspired by social behavior of flocking bird or schooling fishes. This method shares numerous resemblances with the evolutionary computation procedures such as genetic algorithms (GA). Since, PSO algorithms is easy process to subject with minor adjustment of a few restrictions, it has gained more attention or advantages over other population based algorithms. Hence, PSO algorithms is widely used in various research fields like ranging from artificial neural network training to other areas where GA can be used in the system

Upcoming challenges for multiple sequence alignment methods in the high-throughput era

This review focuses on recent trends in multiple sequence alignment tools. It describes the latest algorithmic improvements including the extension of consistency-based methods to the problem of template-based multiple sequence alignments. Some results are presented suggesting that template-based methods are significantly more accurate than simpler alternative methods. The validation of existing methods is also discussed at length with the detailed description of recent results and some suggestions for future validation strategies. The last part of the review addresses future challenges for multiple sequence alignment methods in the genomic era, most notably the need to cope with very large sequences, the need to integrate large amounts of experimental data, the need to accurately align non-coding and non-transcribed sequences and finally, the need to integrate many alternative methods and approaches

On the role of metaheuristic optimization in bioinformatics

Metaheuristic algorithms are employed to solve complex and large-scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics

AccuSyn: Using Simulated Annealing to Declutter Genome Visualizations

We apply Simulated Annealing, a well-known metaheuristic for obtaining near-optimal solutions to optimization problems, to discover conserved synteny relations (similar features) in genomes. The analysis of synteny gives biologists insights into the evolutionary history of species and the functional relationships between genes. However, as even simple organisms have huge numbers of genomic features, syntenic plots initially present an enormous clutter of connections, making the structure difficult to understand. We address this problem by using Simulated Annealing to minimize link crossings. Our interactive web-based synteny browser, AccuSyn, visualizes syntenic relations with circular plots of chromosomes and draws links between similar blocks of genes. It also brings together a huge amount of genomic data by integrating an adjacent view and additional tracks, to visualize the details of the blocks and accompanying genomic data, respectively. Our work shows multiple ways to manually declutter a synteny plot and then thoroughly explains how we integrated Simulated Annealing, along with human interventions as a human-in-the-loop approach, to achieve an accurate representation of conserved synteny relations for any genome. The goal of AccuSyn was to make a fairly complete tool combining ideas from four major areas: genetics, information visualization, heuristic search, and human-in-the-loop. Our results contribute to a better understanding of synteny plots and show the potential that decluttering algorithms have for syntenic analysis, adding more clues for evolutionary development. At this writing, AccuSyn is already actively used in the research being done at the University of Saskatchewan and has already produced a visualization of the recently-sequenced Wheat genome

Filtered Distance Matrix For Constructing High-Throughput Multiple Sequence Alignment On Protein Data

Urutan Penjajaran Berganda (MSA) adalah satu proses yang penting dalam biologi pengkomputeran dan bioinformatik. MSA optima adalah masalah NP-keras sementara membina penjajaran optimum menggunakan pengaturcaraan dinamik merupakan masalah NP lengkap. Multiple sequence alignment (MSA) is a significant process in computational biology and bioinformatics. Optimal MSA is an NP-hard problem, while building optimal alignment using dynamic programming is an NP complete problem. Although numerous algorithms have been proposed for MSA, producing an efficient MSA with high accuracy remains a huge challenge

Interruptional Activity and Simulation of Transposable Elements

Transposable elements (TEs) are interspersed DNA sequences that can move or copy to new positions within a genome. The active TEs along with the remnants of many transposition events over millions of years constitute 46.69% of the human genome. TEs are believed to promote speciation and their activities play a significant role in human disease. The 22 AluY and 6 AluS TE subfamilies have been the most active TEs in recent human history, whose transposition has been implicated in several inherited human diseases and in various forms of cancer by integrating into genes. Therefore, understanding the transposition activities is very important. Recently, there has been some work done to quantify the activity levels of active Alu transposable elements based on variation in the sequence. Here, given this activity data, an analysis of TE activity based on the position of mutations is conducted. Two different methods/simulations are created to computationally predict so-called harmful mutation regions in the consensus sequence of a TE; that is, mutations that occur in these regions decrease the transposition activities dramatically. The methods are applied to AluY, the youngest and most active Alu subfamily, to identify the harmful regions laying in its consensus, and verifications are presented using the activity of AluY elements and the secondary structure of the AluYa5 RNA, providing evidence that the method is successfully identifying harmful mutation regions. A supplementary simulation also shows that the identified harmful regions covering the AluYa5 RNA functional regions are not occurring by chance. Therefore, mutations within the harmful regions alter the mobile activity levels of active AluY elements. One of the methods is then applied to two additional TE families: the Alu family and L1 family, in detecting the harmful regions in these elements computationally. Understanding and predicting the evolution of these TEs is of interest in understanding their powerful evolutionary force in shaping their host genomes. In this thesis, a formal model of TE fragments and their interruptions is devised that provides definitions that are compatible with biological nomenclature, while still providing a suitable formal foundation for computational analysis. Essentially, this model is used for fixing terminology that was misleading in the literature, and it helps to describe further TE problems in a precise way. Indeed, later chapters include two other models built on top of this model: the sequential interruption model and the recursive interruption model, both used to analyze their activity throughout evolution. The sequential interruption model is defined between TEs that occur in a genomic sequence to estimate how often TEs interrupt other TEs, which has been shown to be useful in predicting their ages and their activity throughout evolution. Here, this prediction from the sequential interruptions is shown to be closely related to a classic matrix optimization problem: the Linear Ordering Problem (LOP). By applying a well-studied method of solving the LOP, Tabu search, to the sequential interruption model, a relative age order of all TEs in the human genome is predicted from a single genome. A comparison of the TE ordering between Tabu search and the method used in [47] shows that Tabu search solves the TE problem exceedingly more efficiently, while it still achieves a more accurate result. As a result of the improved efficiency, a prediction on all human TEs is constructed, whereas it was previously only predicted for a minority fraction of the set of the human TEs. When many insertions occurred throughout the evolution of a genomic sequence, the interruptions nest in a recursive pattern. The nested TEs are very helpful in revealing the age of the TEs, but cannot be fully represented by the sequential interruption model. In the recursive interruption model, a specific context- free grammar is defined, describing a general and simple way to capture the recursive nature in which TEs nest themselves into other TEs. Then, each production of the context-free grammar is associated with a probability to convert the context-free grammar into a stochastic context-free grammar that maximizes the applications of the productions corresponding to TE interruptions. A modified version of an algorithm to parse context-free grammars, the CYK algorithm, that takes into account these probabilities is then used to find the most likely parse tree(s) predicting the TE nesting in an efficient fashion. The recursive interruption model produces small parse trees representing local TE interruptions in a genome. These parse trees are a natural way of grouping TE fragments in a genomic sequence together to form interruptions. Next, some tree adjustment operations are given to simplify these parse trees and obtain more standard evolutionary trees. Then an overall TE-interaction network is created by merging these standard evolutionary trees into a weighted directed graph. This TE-interaction network is a rich representation of the predicted interactions between all TEs throughout evolution and is a powerful tool to predict the insertion evolution of these TEs. It is applied to the human genome, but can be easily applied to other genomes. Furthermore, it can also be applied to multiple related genomes where common TEs exist in order to study the interactions between TEs and the genomes. Lastly, a simulation of TE transpositions throughout evolution is developed. This is especially helpful in understanding the dynamics of how TEs evolve and impact their host genomes. Also, it is used as a verification technique for the previous theoretical models in the thesis. By feeding the simulated TE remnants and activity data into the theoretical models, a relative age order is predicted using the sequential interruption model, and a quantified correlation between this predicted order and the input age order in the simulation can be calculated. Then, a TE-interaction network is constructed using the recursive interruption model on the simulated data, which can also be converted into a linear age order by feeding the adjacency matrix of the network to Tabu search. Another correlation is calculated between the predicted age order from the recursive interruption model and the input age order. An average correlation of ten simulations is calculated for each model, which suggests that in general, the recursive interruption model performs better than the sequential interruption model in predicting a correct relative age order of TEs. Indeed, the recursive interruption model achieves an average correlation value of ρ = 0.939 with the correct simulated answer

A general Framework for Utilizing Metaheuristic Optimization for Sustainable Unrelated Parallel Machine Scheduling: A concise overview

Sustainable development has emerged as a global priority, and industries are increasingly striving to align their operations with sustainable practices. Parallel machine scheduling (PMS) is a critical aspect of production planning that directly impacts resource utilization and operational efficiency. In this paper, we investigate the application of metaheuristic optimization algorithms to address the unrelated parallel machine scheduling problem (UPMSP) through the lens of sustainable development goals (SDGs). The primary objective of this study is to explore how metaheuristic optimization algorithms can contribute to achieving sustainable development goals in the context of UPMSP. We examine a range of metaheuristic algorithms, including genetic algorithms, particle swarm optimization, ant colony optimization, and more, and assess their effectiveness in optimizing the scheduling problem. The algorithms are evaluated based on their ability to improve resource utilization, minimize energy consumption, reduce environmental impact, and promote socially responsible production practices. To conduct a comprehensive analysis, we consider UPMSP instances that incorporate sustainability-related constraints and objectives

Anchor Point Approach For Initial Population Of Bat Algorithm For Protein Multiple Sequence Alignment

Penjajaran pelbagai turutan atau Multiple sequence alignment (MSA) adalah satu langkah asas kepada banyak aplikasi bio-informatik seperti pembinaan pokok filogenetik, ramalan struktur sekunder dan pengenalpastian motif domain dan yang dipulihara. Kebolehpercayaan dan ketepatan aplikasi-aplikasi ini bergantung kepada kualiti MSA. Multiple sequence alignment (MSA) is a fundamental step for many bioinformatics applications such as phylogenetic tree construction, prediction of the secondary structure and identification of domains and conserved motifs. The reliability and accuracy of these applications depend on the quality of MSA

An Enhanced Flower Pollination Algorithm For Multiple Sequence Alignment

Multiple sequence alignment (MSA) is an alignment of three or more sequences. Studies show that MSA exhibits a challenge, that is, how to find the MSA that maximises the Quality (Q) score and Total Column (TC) score. This problem is an NP-complete problem. Hence, numerous researchers have devoted their efforts to tackle the MSA problem, yet, there is still a shortcoming in the accuracy