Article Details

Abstract

Repetitive sequences are an important component in the field of human genome structure analysis. Accurate and efficient identification of genome repetitive sequences is of great significance for genome sequence structure analysis. In this study, the latest human reference genome version T2T-CHM13 is used as the research object. Two popular bioinformatic algorithms, MashMap and Minimap2, are employed to rapidly locate repeat regions in the genome, obtain detailed sequence alignment results, and calculate the similarity between repetitive sequences within each chromosome. The experimental results show that the number and length distribution of repetitive regions varies significantly among different chromosomes, whereas the difference in average similarity between repetitive sequences is relatively small, with all average similarity values exceeding 99%, demonstrating a high degree of sequence conservation.

Keywords

repetitive sequence; repetitive region; human genome; MashMap; Minimap2

References

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Funding

Graduate Innovative Research Project of Baoji University of Arts and Sciences (YJSCX25YB42); Projects of Shaanxi Provincial Department of Education (23JK0287, 22JK0242); Shaanxi Provincial Department of Science and Technology Project (2024SF-YBXM-134); Research Plan Project of Shaanxi Institute of Basic Sciences (23JSQ051); 2023 Ministry of Education Industry School Cooperation Collaborative Education Project (230705211175618); 2023 Innovation and Entrepreneurship Training Program for College Students at Baoji University of Arts and Sciences (S202310721033); National Natural Science Foundation of China (62402010); Shaanxi Provincial Department of Education Youth Innovation Team Scientific Research Plan Project (25JP004)

About the authors

Wei Zegang (1990-), male, Han ethnicity, from Yuncheng, Shanxi Province, associate professor, doctoral student, research direction: bioinformatics; Corresponding author: Zhang Xiaodan (1988-), female, Han ethnicity, from Yuncheng, Shanxi Province, lecturer, master's student, research direction: biological data mining.