CAJ | 학술논문

本文利用cvTree和复杂网络理论相结合的方法，构建了3420条tRNA序列亲缘关系进化网络。计算了相关网络的平均度、平均聚类系数和平均最短路径随进化距离Dis变化的关系。同时还分析和讨论了tRNA序列之间的亲缘关系及其进化机制，观察到一个与其它方法不同的结果：随着亲缘距离Dis的增加，网络的度分布先是从power-1aw分布转变为Gussian分布，然后又从Gussian分布转变为反power-1aw分布。本研究结果说明由cvTree方法和复杂网络理论相结合的方法研究tRNA基因序列的进化的结果也许更加符合其进化历程。
본문이용cvTree화복잡망락이론상결합적방법，구건료3420조tRNA서렬친연관계진화망락。계산료상관망락적평균도、평균취류계수화평균최단로경수진화거리Dis변화적관계。동시환분석화토론료tRNA서렬지간적친연관계급기진화궤제，관찰도일개여기타방법불동적결과：수착친연거리Dis적증가，망락적도분포선시종power-1aw분포전변위Gussian분포，연후우종Gussian분포전변위반power-1aw분포。본연구결과설명유cvTree방법화복잡망락이론상결합적방법연구tRNA기인서렬적진화적결과야허경가부합기진화역정。
We constructed the kinship evolution network of 3 420 tRNA sequences based on the cvTree method and complex network theory, and calculated the changes of the average degree, the average clustering coefficient and the average shortest path of the network under different Dis values. At the same time, we analyzed and discussed the kinship and evolutionary mechanisms oftRNA sequences, and observed a different results from the other methods that along with the increasing of kinship Dis, the degree distribution of the network first switch from power-law to Gussian distribution, and then convert from Gussian to the opposite power-law distribution. The re- sult meaned that our method combining the cvTree method with complex network theory was more effective on the studying of the evolutionary relationship of modern tRNA sequences.