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 KS dotplot. Combining with whole-genome collinearity analysis, PolyReco uses DBSCAN clustering method to cluster KS dots. According to the distance information in the x-axis and y-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 y-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.

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Chromosome rearrangements play an important role in the speciation of plants and animals, and the recognition of chromosome rearrangement patterns is helpful to elucidate the mechanism of species differentiation at the chromosome level. However, the existing chromosome rearrangement recognition methods have some major limitations, such as low quality, barriers to parental selection, and inability to identify specific rearrangement patterns. Based on the whole genome protein sequences, we constructed the combined figure according to the slope of the collinear fragment, the number of homologous genes, the coordinates in the top left and bottom right of the collinear fragment. The standardized combination figure is compared with the four standard pattern figures, and then combined with the information entropy analysis strategy to automatically classify the chromosome images and identify the chromosome rearrangement pattern. This paper proposes an automatic karyotype analysis method EntroCR (intelligent recognition method of chromosome rearrangement based on information entropy), which integrates rearrangement pattern recognition, result recommendation and related chromosome determination, so as to infer the evolution process of ancestral chromosomes to the existing chromosomes. Validation experiments were conducted using whole-genome data of Gossypium raimondii and Gossypium arboreum, Oryza sativa and Sorghum bicolor. The conclusions were consistent with previous results. EntroCR provides a reference for researchers in species evolution and molecular marker assisted breeding as well as new methods for analyzing karyotype evolution in other species.

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