Author:
Wang Fushun,Zhang Ruolan,Sun Xiaohua,Wang Junhao,Liu Hongquan,Zhang Kang,Wang Chunyang
Abstract
AbstractChromosome 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.
Funder
Science and Technology Project of Hebei Education Department
China Agriculture Research System of MOF and MARA- Food Legumes
National Natural Science Foundation of China
Scientific Research Project of Introducing Talents of Hebei Agricultural University
Innovative Research Group Project of Hebei Natural Science Foundation
Publisher
Springer Science and Business Media LLC