An chromosome-level haplotype-resolved genome assembly and annotation of pitaya (Selenicereus polyrhizus).

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-04-01 DOI:10.1038/s41597-025-04678-6

Juncheng Li, Wenlong Luo, Biao Jiang, Satish Kumar, Mengfei Lin, Qingming Sun

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引用次数: 0

Abstract

Pitaya, (Selenicereus spp.), a fruit originating from North and Central America and extensively cultivated in China and Vietnam, holds significant economic value. Utilizing PacBio HiFi sequencing and Oxford Nanopore Technologies ultra-long sequencing, aided by Hi-C data, we have assembled a chromosome-level haplotype-resolved genome. The sizes of the two haplotype genomes were determined to be 1.477 Gb (hap1, contig N50 = 133.35 Mb) and 1.442 Gb (hap2, contig N50 = 132.57 Mb), with 96.7% (hap1) and 98.4% (hap2) respectively allocated to 11 pseudochromosomes. Hap1 comprises 58.94% repeat sequences and predicts a total of 29,139 protein-coding gene models and 18,378 non-coding RNAs. Hap2 comprises 58.37% repeat sequences and predicts a total of 28,538 protein-coding gene models and 19,458 non-coding RNAs. Notably, 93.5% and 93.6% of protein-coding genes were annotated for the two haplotypes. The high-quality genome assembly presented in this study provides a valuable resource for future ecological, evolutionary, biological, and breeding research in pitaya.

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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.