GenHap: a novel computational method based on genetic algorithms for haplotype assembly

The process of inferring a full haplotype of a cell is known as haplotyping, which consists in assigning all heterozygous Single Nucleotide Polymorphisms (SNPs) to exactly one of the two chromosomes. In this work, we propose a novel computational method for haplotype assembly based on Genetic Algorithms (GAs), named GenHap. Our approach could efficiently solve large instances of the weighted Minimum Error Correction (wMEC) problem, yielding optimal solutions by means of a global search process. wMEC consists in computing the two haplotypes that partition the sequencing reads into two unambiguous sets with the least number of corrections to the SNP values. Since wMEC was proven to be an NP-hard problem, we tackle this problem exploiting GAs, a population-based optimization strategy that mimics Darwinian processes. In GAs, a population composed of randomly generated individuals undergoes a selection mechanism and is modified by genetic operators. Based on a quality measure (i.e., the fitness value), inspired by Darwin’s “survival of the fittest” laws, each individual is involved in a selection process. Our preliminary experimental results show that GenHap is able to achieve correct solutions in short running times. Moreover, this approach can be used to compute haplotypes in organisms with different ploidity. The proposed evolutionary technique has the advantage that it could be formulated and extended using a multi-objective fitness function taking into account additional insights, such as the methylation patterns of the different chromosomes or the gene proximity in maps achieved through Chromosome Conformation Capture (3C) experiments.

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GenHap: A novel computational method based on genetic algorithms for haplotype assembly

10.7287/peerj.preprints.3246 ◽

2017 ◽

Author(s):

Andrea Tangherloni ◽

Simone Spolaor ◽

Leonardo Rundo ◽

Marco S Nobile ◽

Ivan Merelli ◽

...

Keyword(s):

Genetic Algorithms ◽

Selection Process ◽

Fitness Function ◽

Computational Method ◽

Nucleotide Polymorphisms ◽

Optimization Strategy ◽

Genetic Operators ◽

Chromosome Conformation ◽

Haplotype Assembly ◽

Fitness Value

The process of inferring a full haplotype of a cell is known as haplotyping, which consists in assigning all heterozygous Single Nucleotide Polymorphisms (SNPs) to exactly one of the two chromosomes. In this work, we propose a novel computational method for haplotype assembly based on Genetic Algorithms (GAs), named GenHap. Our approach could efficiently solve large instances of the weighted Minimum Error Correction (wMEC) problem, yielding optimal solutions by means of a global search process. wMEC consists in computing the two haplotypes that partition the sequencing reads into two unambiguous sets with the least number of corrections to the SNP values. Since wMEC was proven to be an NP-hard problem, we tackle this problem exploiting GAs, a population-based optimization strategy that mimics Darwinian processes. In GAs, a population composed of randomly generated individuals undergoes a selection mechanism and is modified by genetic operators. Based on a quality measure (i.e., the fitness value), inspired by Darwin’s “survival of the fittest” laws, each individual is involved in a selection process. Our preliminary experimental results show that GenHap is able to achieve correct solutions in short running times. Moreover, this approach can be used to compute haplotypes in organisms with different ploidity. The proposed evolutionary technique has the advantage that it could be formulated and extended using a multi-objective fitness function taking into account additional insights, such as the methylation patterns of the different chromosomes or the gene proximity in maps achieved through Chromosome Conformation Capture (3C) experiments.

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Applications of Genetic Algorithms to the Determination of Reaction Mechanisms

Determination of Complex Reaction Mechanisms ◽

10.1093/oso/9780195178685.003.0012 ◽

2006 ◽

Author(s):

John Ross ◽

Igor Schreiber ◽

Marcel O. Vlad

Keyword(s):

Genetic Algorithms ◽

Rate Coefficient ◽

Optimization Problems ◽

Chemical Species ◽

Optimization Methods ◽

Computational Method ◽

Rate Coefficients ◽

Next Generation ◽

Binary Coding ◽

Fitness Value

The mathematical computational method of genetic algorithms is frequently useful in solving optimization problems in systems with many parameters, for example, a search for suitable parameters of a given problem that achieves a stated purpose. The method searches for these parameters in an efficient parallel way, and has some analogies with evolution. There are other optimization methods available, such as stimulated annealing, but we shall use genetic algorithms. We shall present three different problems that give an indication of the diversity of applications. We begin with a very short primer on genetic algorithms, which can be omitted if the reader has some knowledge of this subject. Genetic algorithms (GAs) work with a coding of a parameter set, which in the field of chemical kinetics may consist of a number of parameters, such as rate coefficients; variables and constraints, such as concentrations; and other quantities such as chemical species. Binary coding for a parameter is done as follows. Suppose we have a rate coefficient = 9.08 × 10−7; then if we write that rate coefficient as 10−P , with −10 ≤ P ≤ 10, a binary coding with string length of 16 bits is given by . . . P = 10 − 20 R /(216 − 1) (10.1) . . . where 0 ≤ R ≤ 216 − 1. Since P = 6.04 we have R = 12,971, or R = 0011001010101010 to the base 2. Thus the value of the rate coefficient is encoded in a single bit string, called a chromosome. For the solution of a given problem an optimization criterion must be chosen. With a given choice of parameters this criterion is calculated; the comparison of that calculation with the goal set for the criterion gives a fitness value for that set of parameters. If the fitness is adequate but not sufficient, when both are selected by prior choice, for any individual, then retain that individual for the next generation. Reject individuals below that choice. Select individuals for the next generation with a probability proportional to the fitness value from a roulette wheel on which the slot size is proportional to the fitness value. Notice that genetic algorithms use probabilistic, not deterministic, transition rules.

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Lamination Optimization of Composite Curved Wing for Maximum Flutter Stability Using Micro Genetic Algorithm

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.324-325.743 ◽

2006 ◽

Vol 324-325 ◽

pp. 743-746

Author(s):

Dong Hyun Kim ◽

Il Kwon Oh

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Composite Materials ◽

Computational Method ◽

Computational System ◽

Wing Model ◽

Weight Condition ◽

Micro Genetic Algorithm

Flutter characteristics of composite curved wing are investigated in this study. The efficient and robust computational system for the flutter optimization has been developed using the coupled computational method based on the micro genetic algorithms. The present results show that the micro genetic algorithm is very efficient in order to find optimized lay-ups for a composite curved wing model. It is found that the flutter stability of curved wing model can be significantly increased using composite materials with proper optimum lamination design when compared to the case of isotropic wing model under the same weight condition.

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A computational method using genetic algorithms for obtaining Stackelberg solutions to two-level linear programming problems

Electronics and Communications in Japan (Part III Fundamental Electronic Science) ◽

10.1002/ecjc.1101 ◽

2002 ◽

Vol 85 (6) ◽

pp. 55-62 ◽

Cited By ~ 6

Author(s):

Ichiro Nishizaki ◽

Masatoshi Sakawa ◽

Keiichi Niwa ◽

Yasuhiro Kitaguchi

Keyword(s):

Genetic Algorithms ◽

Linear Programming ◽

Computational Method ◽

Stackelberg Solutions ◽

Linear Programming Problems

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WWW portal usage analysis using genetic algorithms

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis ◽

10.11118/actaun200957060201 ◽

2009 ◽

Vol 57 (6) ◽

pp. 201-208 ◽

Cited By ~ 1

Author(s):

Ondřej Popelka ◽

Jiří Šťastný

Keyword(s):

Genetic Algorithms ◽

Differential Evolution ◽

Sequential Analysis ◽

Fitness Function ◽

Basic Structure ◽

Grammatical Evolution ◽

Computational Method ◽

Second Phase ◽

Web Usage ◽

Advanced Analysis

The article proposes a new method suitable for advanced analysis of web portal visits. This is part of retrieving information and knowledge from web usage data (web usage mining). Such information is necessary in order to gain better insight into visitor’s needs and generally consumer behaviour. By leveraging this information a company can optimize the organization of its internet presentations and offer a better end-user experience. The proposed approach is using Grammatical evolution which is computational method based on genetic algorithms. Grammatical evolution is using a context-free grammar in order to generate the solution in arbitrary reusable form. This allows us to describe visitors’ behaviour in different manners depending on desired further processing. In this article we use description with a procedural programming language. Web server access log files are used as source data.The extraction of behaviour patterns can currently be solved using statistical analysis – specifically sequential analysis based methods. Our objective is to develop an alternative algorithm.The article further describes the basic algorithms of two-level grammatical evolution; this involves basic Grammatical Evolution and Differential Evolution, which forms the second phase of the computation. Grammatical evolution is used to generate the basic structure of the solution – in form of a part of application code. Differential evolution is used to find optimal parameters for this solution – the specific pages visited by a random visitor. The grammar used to conduct experiments is described along with explanations of the links to the actual implementation of the algorithm. Furthermore the fitness function is described and reasons which yield to its’ current shape. Finally the process of analyzing and filtering the raw input data is described as it is vital part in obtaining reasonable results.

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Implementation of Genetic Algorithms for Optimization of Transportation Problem

Sir Syed Research Journal of Engineering & Technology ◽

10.33317/ssurj.v8iii.89 ◽

2019 ◽

Vol 8 (II) ◽

Author(s):

Soobia Saeed

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Transportation Problem ◽

Optimal Solution ◽

Computational Method ◽

Different Types ◽

Index Terms ◽

The Impact ◽

Single Processor ◽

Human Problem

Transportation problem is a model which is commonly used in data structure solving a problem (human problem solving due to the computational method) because all the humans are related to transportation in any type of manner. Normally, traditional mathematical procedures used for solving the problem which is quite lengthy, after the computational solving procedures it comes to the bit easier to solve it except traditional lengthy methods. The Genetic Algorithm (GA) is most powerful tool for solving transportation problem. It refines the better optimal solution, for enhancing the optimization of transportation problem, using genetic algorithms lots of the work already has been done. This paper discusses the impact of genetic algorithms on two different types of systems environments i.e., Single-Processor Environment Systems and Multi-Processor Environment Systems, for solving the transportation problem and found the best optimal solution time of both systems. Index Terms— Transportation Problem, Genetics Algorithm (GA), Single-Processor Systems, Multi-Processor Systems, Optimization.

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