A telomere-to-telomere genome assembly of Chinese grain sorghum 654.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-03-19 DOI:10.1038/s41597-025-04791-6

Fulin Wang, Jiandong Bao, Heng Zhang, Guowei Zhai, Tao Song, Zhijian Liu, Yu Han, Fan Yu, Guihua Zou, Ying Zhu

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引用次数: 0

Abstract

The grain sorghum inbred line 654 serves as a parent for numerous Chinese commercial hybrids and recombinant inbred lines (RILs), which have played a pivotal role in the cloning of several agronomically important traits. In this study, we present a telomere-to-telomere (T2T) genome assembly of the inbred line 654 (728.81 Mb) using PacBio HiFi, ultra-long Oxford Nanopore Technology, and Hi-C sequencing data. The T2T genome assembly has high integrity (contains all of 10 centromeres and 20 telomeres without any gaps), high contiguity (contig N90: 52.02 Mb), high completeness (98.33% BUSCO completeness, 98.88% k-mer completeness, and LAI 24.38), and extremely low base error (3.37 × 10^-7, QV: 64.72). A total of 62.34% sequences were identified as repetitive, and rest region contained 44,399 protein-coding genes, of which 30,245 were functionally annotated. The gap-free T2T genome assembly enables the full picture of the potential translational genomics, and provides the highest resolution genetic map for future studies on genome evolution, structure variation, and the genetic control of agronomic traits in sorghum breeding.

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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.