DNA Sequences Under Multiple Guanine–Cytosine (GC) Base Pairs Constraint

IF 3.7 4区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS IEEE Transactions on NanoBioscience Pub Date : 2023-09-18 DOI:10.1109/TNB.2023.3316431
Xuwei Yang;Changjun Zhou
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Abstract

DNA computing is a new computing method that has high efficiency in solving large-scale nonlinear and Non-deterministic Polynomial complete problems. The design of DNA sequences is an important step in DNA computing, and the quality of the DNA sequences directly affects the accuracy of DNA computing results. Efficiently designing high-quality DNA sequences is currently a significant challenge. In order to improve the efficiency of DNA sequence design, a sparrow evolutionary search algorithm (SESA) is proposed by us. It inherits the fast convergence of the sparrow search algorithm and avoids the situation that the sparrow search algorithm is prone to fall into a local optimum, which greatly improves the search performance of the algorithm on discrete numerical problems. In order to improve the quality of DNA sequence, a new constraint, multiple GC constraint, has been proposed in this paper. Simulated experiments in NUPACK show that this constraint can greatly improve the quality of the DNA sequences designed by us. Compared with previous results, our DNA sequences have better stability.
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多个鸟嘌呤-胞嘧啶(GC)碱基对约束下的DNA序列。
DNA计算是一种新的计算方法,在求解大规模非线性和非确定性多项式完全问题时具有很高的效率。DNA序列的设计是DNA计算的重要步骤,DNA序列的质量直接影响DNA计算结果的准确性。高效设计高质量的DNA序列目前是一项重大挑战。为了提高DNA序列设计的效率,我们提出了一种麻雀进化搜索算法(SESA),它继承了麻雀搜索算法的快速收敛性,避免了麻雀搜索容易陷入局部最优的情况,大大提高了算法在离散数值问题上的搜索性能。为了提高DNA序列的质量,本文提出了一种新的约束条件——多重GC约束。在NUPACK中的模拟实验表明,这种约束可以大大提高我们设计的DNA序列的质量。与以前的结果相比,我们的DNA序列具有更好的稳定性。
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来源期刊
IEEE Transactions on NanoBioscience
IEEE Transactions on NanoBioscience 工程技术-纳米科技
CiteScore
7.00
自引率
5.10%
发文量
197
审稿时长
>12 weeks
期刊介绍: The IEEE Transactions on NanoBioscience reports on original, innovative and interdisciplinary work on all aspects of molecular systems, cellular systems, and tissues (including molecular electronics). Topics covered in the journal focus on a broad spectrum of aspects, both on foundations and on applications. Specifically, methods and techniques, experimental aspects, design and implementation, instrumentation and laboratory equipment, clinical aspects, hardware and software data acquisition and analysis and computer based modelling are covered (based on traditional or high performance computing - parallel computers or computer networks).
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