Ali Ali, Guangtu Gao, Rafet Al-Tobasei, Ramey C Youngblood, Geoffrey C Waldbieser, Brian E Scheffler, Yniv Palti, Mohamed Salem
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引用次数: 0
摘要
利用Canu管道、高覆盖PacBio长读序列数据、Bionano光学图谱和Hi-C近接序列数据对虹鳟鱼Swanson双单倍体(DH) YY雄性系基因组进行从头组装,得到29个与Swanson系核型一致的主要支架(2 N = 58)。该组装体总计2.3 Gb, N50为52.4 Mb,在29条染色体序列中约占基因组的95%,支架之间只有109个间隙。值得注意的是,对Swanson系基因组组装中先前的错误进行了修正,包括在Omy05染色体上发现了双大反转(~57 Mb),在Arlee和Swanson组装之间没有Omy20反转,以及在Omy26上发现了~6.7 Mb的反转。这种综合组装有助于完善虹鳟鱼参考基因组,并作为该物种未来遗传研究的宝贵资源。
Chromosome level genome assembly and annotation of the Swanson rainbow trout homozygous line.
The genome of the Swanson doubled haploid (DH) YY male line of rainbow trout was de novo assembled using the Canu pipeline, high-coverage PacBio long-read sequence data, Bionano optical maps, and Hi-C proximity ligation sequence data, resulting in 29 major scaffolds aligning with the karyotype of the Swanson line (2 N = 58). This assembly, totaling 2.3 Gb with an N50 of 52.4 Mb, represents approximately 95% of the genome in 29 chromosome sequences with only 109 gaps between scaffolds. Notably, corrections to previous errors in the Swanson line genome assembly were made, including the identification of a double large inversion on the Omy05 chromosome (~57 Mb), the absence of the Omy20 inversion between the Arlee and Swanson assemblies, and the discovery of a ~6.7 Mb inversion on Omy26. This comprehensive assembly contributes to refining the rainbow trout reference genome and serves as a valuable resource for future genetic studies within this species.
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.