A Near Complete Genome Assembly of the Oshima Cherry Cerasus speciosa.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-04 DOI:10.1038/s41597-025-04388-z

Kazumichi Fujiwara, Atsushi Toyoda, Bhim B Biswa, Takushi Kishida, Momi Tsuruta, Yasukazu Nakamura, Noriko Kimura, Shoko Kawamoto, Yutaka Sato, Toshio Katsuki, Tsuyoshi Koide

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Abstract

The Oshima cherry (Cerasus speciosa), which is endemic to Japan, has significant cultural and horticultural value. In this study, we present a near complete telomere-to-telomere genome assembly for C. speciosa, derived from the old growth "Sakurakkabu" tree on Izu Oshima Island. Using Illumina short-read, PacBio long-read, and Hi-C sequencing, we constructed a 269.3 Mbp genome assembly with a contig N50 of 32.0 Mbp. We examined the distribution of repetitive sequences in the assembled genome and identified regions that appeared to be centromeric. Detailed structural analysis of these putative centromeric regions revealed that the centromeric regions of C. speciosa comprised repetitive sequences with monomer lengths of 166 or 167 bp. Comparative genomic analysis with Prunus sensu lato genome revealed structural variations and conserved syntenic regions. This high-quality reference genome provides a crucial tool for studying the genetic diversity and evolutionary history of Cerasus species, facilitating advancements in horticultural research and the preservation of this iconic species.

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.