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Participants are encouraged to come by the booth for a hands on demonstration and explanation of the system, its components and applications. For more details visit the website at www. UTM RBT is a training company, which manufactures training ammunition, weapon conversions, products and provides training course, curriculum development, training facility designs and consultation. However, these techniques cannot always be generalized to MSA cases due to the excessive computation that is incurred after the addition of each extra sequence. Therefore, using classical methods in the MSA case is almost impossible.

Regardless of the solution methodology, MSAs can be categorized into three main solution categories: exact, progressive and iterative [ 3 ]. In exact methods, which are usually the generalized methods of the Needleman and Wunsch algorithm [ 4 ], all sequences are aligned simultaneously to find the optimal answer. The main drawback of this class of algorithms is their massive computational need, usually impossible to find the answer in polynomial time. In progressive algorithms, sequences are first aligned two-by-two using an appropriate pair-wise algorithm before finding the final alignment.

Then, an alignment guidance tree is generated based on these pair-wise alignment scores. Sequences are combined step by step to find the optimal answer by starting from the closest two sequences. In this case, current sequences are modified to get the best fit for new combining sequences. Although this class of algorithms normally manages to find reasonable alignments especially for generating phylogeny trees , their main disadvantage is their sensitivity for getting trapped into local minima.

In iterative methods, all sequences are aligned simultaneously. By using one or more heuristic algorithms, an initial answer is computed first. Then, this initial answer is improved iteratively by using intelligent routines designed for this type of MSAs. Although these algorithms are not as sensitive as progressive algorithm to falling into local minima, however, they have their own drawbacks. For example, the accuracy of the final answer is greatly dependent on the quality of the seed solution. They all use a global alignment algorithm in [ 4 ] to construct an alignment for the entire length of the sequences.

The main difference among these methods is in the order they combine the input sequences. MULTAL deploys a sequential branching method to align the two closest sequences before building up the final alignment by subsequently aligning the next closest sequence to it. This tree is then used to align larger and larger groups of input sequences. CLUSTALX that uses the alternative neighbor-joining algorithm [ 11 ] to construct a guide tree has one of the most sophisticated scoring systems.

It considers sequence weighting, position dependant gap penalties, and the automatic switching among scoring matrices based on the degree of similarity among the input sequences. Then, an iterative procedure is deployed to combine these segments toward generating the final alignment. PRRP [ 14 ] iteratively divides the input sequences into two groups and then subsequently realign them using a global group-to-group alignment algorithm. SAGA [ 15 ] evolves a population of alignments in a quasi evolutionary manner to gradually improve their fitness.

Using its simplified scoring matrix, MAFFT manages to significantly reduce the CPU time and increases the accuracy of alignments even for sequences having large insertions and extensions as well as distantly related sequences of similar length. ProbCons [ 17 ], which computes posterior-probability matrices and expected accuracies for each Pair-wise comparison, applies the probabilistic consistency transformation, and then computes an expected accuracy guide tree to progressively generate the final alignment.

T-Coffee [ 18 ] pre-processes a data set of all pair-wise alignments between the input sequences to generate a guide tree for the progressive alignment. T-Coffee not only does focus on the next aligned sequences but also on the whole set of input sequences. MUSCLE [ 19 ] as one of the very fast algorithms in this field has three stages: draft progressive, improved progressive, and refinement. At the completion of each stage, a multiple alignment is available and the algorithm can be terminated. The first stage builds a progressive alignment, the second stage that might be iterated attempts to improve the tree and builds a new progressive alignment according to this tree, and, the third stage performs iterative refinement using a variant of tree-dependent restricted partitioning.

Parameters for such models have been estimated from a large library of structure-based alignments. There are also other HMMs methods that use statistical models of the primary structure consensus to align input sequences [ 21 , 22 ]. HMMT [ 23 ] uses the simulated annealing algorithm to maximize the probability that an HMM represents the sequences to be aligned.

RBT [ 24 , 25 ] is another iterative algorithm that uses the n -dimensional version of the DP table n is the number of input sequences to find the best alignment among input sequences. The analogy of a Rubber Band is a unique contribution of this work.

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Further, GA based algorithms were among the some of the most effective approaches used to solve the MSA problem. In [ 26 ], a combination of a GA and DP is used with two different distance matrices. The main drawback of this technique is its limitation in performing crossover and mutation operations. In addition to this algorithm's convergence problems, forcing the GA to work around the CSA and the initial population creates a major disadvantage for this approach.

It leads to the inability of the main search algorithm to explore all parts of the solution space. In [ 28 ], a very different GA approach is presented. In this work, five mutation operators are designed to be randomly selected in each cycle of the algorithm.

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Here, no particular optimization plan is used; therefore, this greedy algorithm just moves toward any potential answer. Although, the authors carefully define their chromosome, crossover and mutation operators, the definition of their scoring function appears to be their main drawback. In [ 30 ] a very interesting divide-and-conquer GA based approaches is presented. Here, the sequences are divided into smaller sequences and then they are aligned by a GA.

If these partial alignments generate better results, they would be replaced by the original ones. In [ 31 ], the authors present a very simple implementation of the GA. In this work, the GA's convergence speed is significantly reduced by the simplicity of the algorithm.

The fact that this GA approach discards many offspring is the main reason for its slow convergence.

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In [ 32 ], the convergence speed of a GA is increased by combining it with a Simulated Annealing algorithm. The GA in [ 3 ] use quantum mechanics concepts by employing a binary matrix to represent only four chromosomes that are used to solve the problem. In each GA cycle, the best three solutions are directly copied to the next generation and only one of them the worst one is selected for the GA operations. The proposed GA is significantly biased toward good answers, which strongly prevents it from exploring other parts of the solution space.

Authors of the research in [ 33 ] present a GA based approach to find the optimal cut-off-points to divide the large sequences to several smaller ones.


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Each of these smaller sequences is solved by an Ant-Colony approach. The limited use of the GA just to find the cut-off-points is quite time consuming in this approach. The rest of the paper is structured as follows. This is followed by simulation results, discussion and analysis, and conclusion. The MSA problem can be defined as finding with the following properties:. The alignment score, is maximized where denotes a quotation of similarity between and , and, g is related to gaps of.

Based on the above, the MSA can be formulated as an optimization problem.

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However, it is important to note that the complexity of the problem increases exponentially as we add more sequences to the input sequences set — finding the optimal answer is not always possible. Thus, this is why classical methods like DP and Needleman's algorithm can only deal with a small number of short sequences. Metaheuristics are powerful classes of optimization techniques.

A popular class among these techniques is GAs that are very robust search methods [ 34 , 35 ]. A GA is always initiated with a set of possible solutions of the problem, known as initial population. The initial population consists of several chromosomes. Each chromosome is formed from a series of binary or decimal numbers representing genes. The initial population is normally constructed by generating several random chromosomes that are supposed to represent the solution space rather homogenously.

This attribute is much more important that the quality of the individual chromosomes in the initial population. During the optimization process, the chromosomes are evaluated by the genetic optimizer and the best of them are selected to generate the next population.

What is Risk-Based Testing