有效計算智慧為主的方法用於誘發突變引子設計

The Valid Computational Intelligence Method Applies to Mutagenic Primer Design

在本研究中，提出一種有效計算智慧為主 的方法來設計聚合酶鏈鎖反應──限制性片段長 度 多 型 性 （Polymerase Chain ReactionRestriction Fragment Length Polymorphism, PCR-RFLP）的誘發突變引子，用於單核苷酸多 型性（Single Nucleotide Polymorphism, SNP）基 因定型。一種具效率且基於 REBASE 之限制酶 搜尋的新方法被引入到該方法中。誘發突變矩 陣被用於提升獲得目標單一核苷酸多型性限 制酶的效率。此外，較準確的熱力學解鏈溫度 計算也被應用於該方法。我們已經利用所提出 的方法設計於具有錯配 PCR-RFLP 的 25 個目 標單一核苷酸多型性上。結果顯示此方法有助於設計符合條件的誘發突變引子與獲得目標 單一核苷酸多型性上可用的限制酶。

In this study, we propose an efficient computational intelligence-based approach to design mutagenic primers for single nucleotide polymorphism (SNP) genotyping in polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). A new method of restriction enzyme search based on REBASE is introduced into the method. A mutagenic matrix is used to increase the efficiency of obtaining restriction enzymes for the target singlenucleotide polymorphism. In addition, an accurate thermodynamic melting temperature calculation is also applied to the method. There are 25 target single nucleotide polymorphisms with mismatch PCR-RFLP have been performed by the method. The results show that the proposed method facilitates the design of mutagenic primers that satisfied the desired conditions and get available restriction enzymes for the target SNP.