# A Method for Degenerate Primer Design Based on Artificial Bee Colony Algorithm
## 1 Introduction  
In recent years, degenerate polymerase chain reaction (PCR) technology has been widely used in new gene cloning, gene expression detection, virus detection and genome research. It has the advantages of rapidity, simplicity, and high sensitivity [1]. A PCR primer sequence is called degenerate if some of its positions have several possible bases. The degeneracy of the primer is the number of unique sequence combinations it contains. This can improve the coverage of DNA template, but values that are too high will reduce the specificity of PCR. Therefore, the suitability of degenerate primers directly affects the success rate of PCR. The design of degenerate primers can be described as a given set of n strings and integers k, d and m, looking for a primer of length k and degeneracy of at most d that matches at least m input strings [2; 3]. In traditional primer design, if primers are required to match the target DNA sequence and some unknown sequences as much as possible, the number of bases at each position along the sequence is typically counted, and the appropriate primer length to minimize its length determined [4]. However, this method is often used, due to the inconsistency between the sequence and inappropriate fitness evaluation function, during operations in primers with higher degeneracy. Therefore, in the process of primer design, primers should usually be selected to match some but not all of the target DNA sequence. In recent years, iterative heuristic algorithms have been widely used to solve primer design problems. [PERSON] et al. [5] proposed a method using dynamic pattern matching to complete the design of degenerate primers, inputting nucleotide sequences from different bacteria into a system consisting of three steps: data reconstruction, primerdesign and attribute filtering, freely combining the results with Gibbs, and designing and selecting the most suitable sequence as a series of primers. [PERSON] et al. [6] proposed an algorithm for designing the minimum degeneracy of degenerate primers for a given DNA sequence that combined the topic discovery methodology and iterative technology to perform testing and comparison in random and real biological data sets. [PERSON] et al. [7] proposed an iterative beam search algorithm and multiple iterative primer selector for the design of multiple degenerate primers that was superior to existing related algorithms for degenerate primer design and had a smaller number of stray amplifiers. [PERSON] et al. [8] proposed the use of the genetic algorithm to solve the primer design problem, and the constraint conditions of primer design were described using symbols and formulations; the algorithm was able to design primers meeting the requirements of specific restriction sites and specificity. [PERSON] et al. [9] proposed a new method for designing multiple PCR primers using the genetic algorithm and the MAP model; this method found several sets of appropriate primer pairs for five gene family cDNA templates, which were not only able to meet many primer design constraints, but also made the primers specific. In addition, software systems based on iterative heuristic algorithms have been developed for the design of degenerate primers, such as Primer 3 [10], Primo Degenerate 3.4 [11], CODEHOP [12], etc. [PERSON] et al. [13] introduced the design of maximum coverage degenerate primers and minimum degeneracy primers, and developed a program named HYDEN on the basis of the proposed approximate algorithm, successfully applying it for the identification of olfactory receptor genes in mammals. [PERSON] et al. [14] used existing algorithms to design cluster-based degenerate primers, and applied them in parallel in the GPUDePiCt software package using the shared memory model and graphics processing units (GPUs) to accelerate processing, and tested them in a large number of sequences in the human genome.