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Gene sequence alignment method and system
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A gene sequence and sequence technology, applied in the field of sequence comparison, can solve the problems of slow algorithm convergence and low solution accuracy, and achieve the effects of accelerating convergence speed, enhancing randomness, and enhancing global optimization and search capabilities
Active Publication Date: 2022-05-27
SHANDONG NORMAL UNIV
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However, with the in-depth research on the ABC algorithm, it is found that the probability selection mechanism in the bee-following stage fails in the late iteration of the population, which leads to the slow convergence of the algorithm in the late iteration and the problem of low solution accuracy.
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Embodiment 1
[0049] This embodiment provides a gene sequence alignment method, such as figure 1 shown, including the following steps:
[0050] Step 1. Obtain multiple gene sequences;
[0051] Step 2. Initialization: including the initialization of parameters and the generation of initial honey source. Among them, the initialization parameters include the population size SN, the number of nectar sources SN, the individual dimension D, the threshold limit, the maximum number of iterations MCN, the maximum number of evaluations MFE, and the maximum value UB j and the minimum value LB j ; The generation of the initial nectar source is to randomly generate SN initial nectar sources through formula (1):
[0052] x i，j =LB j +rand(0,1)·(UB j -LB j ) (1)
[0053] where x i，j represents the jth dimension vector of the ith nectar source (individual), i=1, 2, 3, ... SN, j=1, 2, 3, ..., D, {LB j , UB j} represents the value range of the jth dimension variable, and rand(0, 1) represents a ra...
Embodiment 2
[0083] The present embodiment provides a gene sequence comparisonsystem, which specifically includes the following modules:
[0084] A gene sequence acquisition module, which is configured to: acquire a plurality of gene sequences;
[0085] The gene sequence alignment module is configured to: encode the parameters of the hidden Markov model as nectar sources, and for each gene sequence, adopt the hidden Markov models corresponding to all nectar sources to obtain a variety of hidden Markov models corresponding to each gene sequence State sequence; judge whether the termination condition is met, if so, compare the hidden state sequences of all gene sequences obtained by the hidden Markov model corresponding to the nectar source with the largest fitness value, and obtain the most similar gene sequence to each gene sequence. Gene sequence; otherwise, based on the fitness value of each nectar source, all nectar sources are divided into multiple populations, and differential learning...
Embodiment 3
[0088] This embodiment provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, implements the steps in the gene sequence alignment method described in the first embodiment above.
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Abstract
The invention provides a genesequence comparison method and system, and the method comprises the steps: coding parameters of hidden Markov models as nectar sources, and for each gene sequence, adopting the hidden Markov models corresponding to all nectar sources to obtain a plurality of hidden state sequences corresponding to each gene sequence; judging whether a termination condition is met or not, and if yes, comparing the hidden state sequences of all the gene sequences obtained by the hidden Markov model corresponding to the nectar source with the maximum fitness value pairwise to obtain a gene sequence most similar to each gene sequence; and otherwise, based on the fitness value of each nectar source, dividing all nectar sources into a plurality of populations, and carrying out differential learning among different populations to optimize parameters of the hidden Markov model until a termination condition is met. The randomness of parameter search of the hidden Markov model is enhanced, the solution is prevented from falling into local optimum, and the solution precision during multi-sequence alignment is improved.
Description
technical field [0001] The invention belongs to the technical field of sequence comparison, and in particular relates to a gene sequence comparison method and system. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] In recent years, with the rapid development of biological science and technology, how to analyze and process the hidden meaning of data in biological database is a serious challenge faced by human beings. Sequence alignment can reflect the information possessed by biological sequences and has been widely used to identify related DNA and protein sequences. Sequence alignment has been developed for decades, and a large number of sequence alignment methods have been proposed. For example, the sequence alignmentalgorithm based on dynamic programming, but the algorithm consumes a lot of time and space and cannot solve practical pro...
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