Wang Fushun, Zhang Kang, Zhang Ruolan, Liu Hongquan, Zhang Weijin, Jia Zhanxiao, Wang Chunyang
Department of Information Science and Technology, Hebei Agricultural University, Baoding, China.
Hebei Key Laboratory of Agricultural Big Data, Baoding, China.
Front Genet. 2022 Apr 20;13:842387. doi: 10.3389/fgene.2022.842387. eCollection 2022.
Polyploidization plays a critical role in producing new gene functions and promoting species evolution. Effective identification of polyploid types can be helpful in exploring the evolutionary mechanism. However, current methods for detecting polyploid types have some major limitations, such as being time-consuming and strong subjectivity, etc. In order to objectively and scientifically recognize collinearity fragments and polyploid types, we developed PolyReco method, which can automatically label collinear regions and recognize polyploidy events based on the dotplot. Combining with whole-genome collinearity analysis, PolyReco uses DBSCAN clustering method to cluster dots. According to the distance information in the -axis and -axis directions between the categories, the clustering results are merged based on certain rules to obtain the collinear regions, automatically recognize and label collinear fragments. According to the information of the labeled collinear regions on the -axis, the polyploidization recognition algorithm is used to exhaustively combine and obtain the genetic collinearity evaluation index of each combination, and then draw the genetic collinearity evaluation index graph. Based on the inflection point on the graph, polyploid types and related chromosomes with polyploidy signal can be detected. The validation experiments showed that the conclusions of PolyReco were consistent with the previous study, which verified the effectiveness of this method. It is expected that this approach can become a reference architecture for other polyploid types classification methods.
多倍体化在产生新的基因功能和促进物种进化方面起着关键作用。有效识别多倍体类型有助于探索进化机制。然而，目前检测多倍体类型的方法存在一些主要局限性，如耗时、主观性强等。为了客观科学地识别共线性片段和多倍体类型，我们开发了PolyReco方法，该方法可以基于点阵图自动标记共线区域并识别多倍体事件。结合全基因组共线性分析，PolyReco使用DBSCAN聚类方法对点阵进行聚类。根据类别之间在x轴和y轴方向上的距离信息，按照一定规则合并聚类结果以获得共线区域，自动识别并标记共线片段。根据标记的共线区域在x轴上的信息，使用多倍体化识别算法进行穷举组合，得到各组合的遗传共线性评估指标，进而绘制遗传共线性评估指标图。基于图上的拐点，可以检测多倍体类型和具有多倍体信号的相关染色体。验证实验表明，PolyReco的结论与先前的研究一致，验证了该方法的有效性。预计该方法可成为其他多倍体类型分类方法的参考架构。
