牛毛滴虫病病原体--胎儿毛滴虫的染色体级基因组组装。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-09-20 DOI:10.1038/s41597-024-03818-8
Mostafa Y Abdel-Glil, Johannes Solle, Daniel Wibberg, Heinrich Neubauer, Lisa D Sprague
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引用次数: 0

摘要

胎牛毛滴虫(Tritrichomonas foetus)是一种寄生原生动物,是造成牛毛滴虫病的罪魁祸首,这种生殖疾病给全世界的畜牧业带来了巨大的经济负担。在这里,我们利用短线程(Illumina Miseq)、长线程(Oxford Nanopore)和染色质连接(Hi-C)测序技术,展示了T. foetus -KV-1 (ATCC 30924)的染色体组水平参考基因组。这是三尖杉单胞菌目寄生原生动物的第一个染色体级基因组,也是继阴道毛滴虫(人类性传播感染的人类相关致病菌)之后,寄生原生动物中的第二个染色体级基因组。我们构建的基因组大小为 148 Mb,支架的 N50 长度为 22.9 Mb。等位基因锚定在五个超级支架上,与该物种的五条染色体相对应,覆盖了基因组组装的 78%。我们预测了 41,341 个编码蛋白质的基因,其中 95.10% 的基因已经进行了功能注释。这一高质量的基因组组装可作为胎牛的重要参考基因组,为未来的功能基因组学、基因保护和分类学研究提供支持。
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Chromosome-level genome assembly of Tritrichomonas foetus, the causative agent of Bovine Trichomonosis.

Tritrichomonas foetus is a parasitic protist responsible for bovine trichomonosis, a reproductive disease associated with significant economic burden to the livestock industry throughout the world. Here, we present a chromosome-level reference genome of T. foetus -KV-1 (ATCC 30924) using short-read (Illumina Miseq), long-read (Oxford Nanopore) and chromatin-linked (Hi-C) sequencing. This is the first chromosome-level genome of a parasitic protist of the order Tritrichomonadida and the second within the Parabasalia lineage, after Trichomonas vaginalis, the human-associated causative agent of the sexually transmitted infection in humans. Our constructed genome is 148 Mb in size, with a N50 length of the scaffolds of 22.9 Mb. The contigs are anchored in five super-scaffolds, corresponding to the expected five chromosomes of the species and covering 78% of the genome assembly. We predict 41,341 protein-coding genes, of which 95.10% have been functionally annotated. This high-quality genome assembly serves as a valuable reference genome for T. foetus to support future studies in functional genomics, genetic conservation and taxonomy.

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来源期刊
Scientific Data
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.
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